research on improving methods for estimating crop...
TRANSCRIPT
SYNTHESIS OF LITERATURE AND
FRAMEWORK
Research on Improving Methods
for Estimating Crop Area, Yield
and Production under Mixed,
Repeated and Continuous
Cropping
January 2016
Working Paper No. 5
Global Strategy Working Papers
Global Strategy Working Papers present intermediary research outputs (e.g.
literature reviews, gap analyses etc.) that contribute to the development of
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The opinions expressed and the arguments employed herein do not necessarily
reflect the official views of Global Strategy, but represent the author’s view at
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by the Global Office. Comments are welcome and may be sent to
Synthesis of Literature and Framework
Under the project
Research on Improving
Methods for Estimating Crop Area, Yield and Production under Mixed, Repeated and
Continuous Cropping
Drafted By
U.C. Sud
Tauqueer Ahmad
V.K. Gupta
Hukum Chandra
Prachi Misra Sahoo
Kaustav Aditya
Man Singh
Ankur Biswas
and
ICAR-Indian Agricultural Statistics Research Institute
New Delhi, India
2015
Table of contents Abstract……………………………………………………………………………... i
Acronyms and Abbreviations………………………………………………………. ii
1. Introduction……………………………………………………………………... 1
2. Concepts and definitions……………………………………………….............. 3
3. Methods based on mapping…………………………………………………….. 12
3.1. Methods based on mapping……………………………………………. 13
3.2. Land surveying methods………………………………………………. 22
3.3. Farmer assessment of crop area……………………………………….. 30
3.4. Farmer assessment of crop area……………………………………….. 31
4. Methods of crop yield estimation………………………………………………. 37
4.1. Whole plot harvest……………………………………………………... 37
4.2. Crop cut and farmers’ estimate methods……………………………… 38
4.3. Other methods of crop yield estimation………………………………. 48
5. Crop acreage and yield estimation under mixed cropping…………………... 56
6. Crop acreage and yield estimation under continuous cropping……………... 64
7. Small area estimation………………………………………………………….... 66
7.1. Synthetic estimation…………………………………………………... 67
7.2. Mixed models in small area estimation……………………………….. 68
7.3. Extension of mixed models in small area estimation…………………. 72
7.4. Some applications of small area estimation in agriculture……………. 73
8. Country experiences on crop acreage and yield estimation…………………. 75
8.1. Bulgaria………………………………………………………………... 75
8.2. Brazil…………………………………………………………………... 76
8.3. Canada………………………………………………………………… 76
8.4. Egypt…………………………………………………………………... 78
8.5. Ethiopia………………………………………………………………... 79
8.6. Nigeria…………………………………………………………………. 80
8.7. France………………………………………………………………….. 81
8.8. United States of America……………………………………………… 82
8.9. India…………………………………………………………………… 83
8.10. South Africa…………………………………………………………... 84
8.11. Sudan…………………………………………………………………. 85
8.12. Morocco……………………………………………………………… 86
8.13. Rwanda………………………………………………………………. 86
8.14. Indonesia……………………………………………………………… 87
8.15. Jamaica……………………………………………………………….. 88
9. Conclusions……………………………………………………………………… 89
Annex I……………………………………………………………………... 91
Annex II…………………………………………………………………….. 95
References…………………………………………………………………………... 105
i
Abstract This technical report focuses on methodologies used for estimation of a crop
area and crop yield under mixed and continuous cropping. It provides a review
of the literature on methodologies used for estimation of crop area and crop
yield in developed and developing countries. The report begins with
descriptions of relevant concepts and definitions and follows with descriptions
of various methods used for estimation of crop area and crop yield along with
their positive and negative attributes. Also in the report, gold standard methods
of crop area and crop yield estimation are identified and various methods are
compared against those gold standards and country experiences with regard to
crop area and crop yield estimation are discussed. The potential of a small area
estimation technique to provide reliable estimates of crop area and crop yield in
cases of small sample size is also described in detail along with some
applications. Based on a critical review of literature, some relevant issues are
highlighted and recommendations are made.
The authors would like to thank Naman Keita, Christophe Duhamel and
Michael Austin Rahija of the Food and Agriculture Organization of the United
Nations (FAO) for their helpful and constructive comments that contributed to
improving the final version of the technical report. The authors are also grateful
to Consuelo Señoret of FAO for her continuous administrative support. The
authors thank Dr. S.D. Sharma for improving the quality of the technical report.
The authors gratefully acknowledge the suggestions made by peer reviewers,
which led to further improvement in the technical report.
ii
Acronyms and Abbreviations
CONAB National Food Supply Company, Brazil
CSA Central Statistical Agency
EBLUE Empirical Best Linear Unbiased Estimator
EBLUP Empirical Best Linear Unbiased Predictor Estimator
ESA Egyptian Survey Authority
FAO Food and Agriculture Organization of the United Nations
FASAL Forecasting Agricultural output using Space, Agrometeorology
and Land based observations
FCC False Color Composite
GCES General Crop Estimation Surveys
GDP Gross Domestic Product
GHS General Household Survey
GIS Geographic Information System
GPS Global Positioning System
IBGE Brazilian Institute of Geography and Statistics
ICAR India Council of Agriculture Research
IFPRI International Food Policy Research Institute of Niger
INRAN National Agricultural Research Institute of Niger
LACIE Large Area Crop Inventory Experiment
LISS Linear Imaging Self Scanner
LUCAS Land Use/Cover Area frame Survey
MAAIF Ministry of Agriculture Animal Industry and Fishery, Uganda
MAPA Ministry of Agriculture, Livestock, and Supply, Brazil
MSS Multispectral Scanner System
MXL Maximum Likelihood
NBS National Bureau of Statistics
NDVI Normalized Difference Vegetation Index
NSSO National Sample Survey Office
PC Principal Components
PSU Primary Sampling Unit
SSU Secondary Sampling Unit
UBOS Uganda Bureau of Statistics
USDA United States Department of Agriculture
1
1 Introduction Agriculture, with its related sectors, such as horticulture, animal husbandry,
fishery and forestry, is the largest livelihood provider and contributes
significantly to the national gross domestic product (GDP) in most of
developing/under developed countries. Lack of quality agricultural statistics
may lead to misallocation of scarce resources and policy formulations that fail
to resolve critical development problems (Kelly et al. 1995). As such, the
generation of timely, reliable and quality agricultural statistics is critical for
policy planning and administrative decision-making. Reviewing and upgrading
a mechanism for the continuous generation of timely and reliable agricultural
statistics, therefore, is of paramount importance.
Two major approaches for development of appropriate methodologies for the
generation of agricultural statistics are (a) complete enumeration and (b) sample
survey. Sample survey is generally adopted because it provides an output that is
cost effective, timely, precise and of high quality. The choice of an appropriate
sampling design and the estimation procedure are therefore critical in this
context.
The availability and quality of agricultural statistics have been declining in the
developing and underdeveloped countries. Some of these countries even lack
the capacity to produce a minimum set of data as evidenced by the poor
response rates to Food and Agriculture Organization of the United Nations
(FAO) questionnaires (World Bank 2010). The global strategy to improve
agricultural and rural statistics is a groundbreaking effort to strengthen
agricultural statistics. At its forty-first session, in February 2100, the United
Nations Statistical Commission endorsed the technical content and strategic
directions of the global strategy and urged the rapid development of an action
plan for implementation (hereafter, Global Action Plan). One of the issues
identified in the global strategy under the component data collection methods is
estimating area, yield and production of mixed, repeated and continuous
cropping. The purpose of this document is to provide a synthesis of literature
2
and framework for estimating area, yield and production, in general, and in
mixed, repeated and continuous cropping, in particular. To begin with, in
section 2, the related concepts and definitions of various terms used in the
document are given. The different methods used for estimation of crop area and
crop yield are discussed in sections 3 and 4, respectively. Sections 5and 6
depict the methods of estimating crop area and yield under mixed and
continuous cropping systems, respectively. Section 7 deals with small area
estimation technique. A glimpse of various methods of crop area and yield
estimation procedures employed in different countries is given in section 8.The
concluding remarks on "synthesis of literature and framework" are given in
section 9.
3
2 Concepts and definitions
The purpose of this section is to describe concepts and give definitions of
various terms used in the report. Before giving definitions, it should be noted
that crop production estimates are usually derived from two key components,
namely total area harvested/planted and yield per unit area. The area under the
crop and the yield per unit area are estimated separately and the product of
those two estimates provides the estimate of the total output or crop production.
However, there is another approach of estimation of crop yield wherein crop
yield is estimated by dividing crop production by crop area. Under this
approach, crop production estimates are obtained using farmers recall/diary
method. The former approach is widely practiced in most of the countries.
Crop area
Estimated crop area is one of the two major components of estimated crop
production. To estimate crop production properly, the crop area must be
estimated precisely, accurately and correctly. In the following paragraph,
concepts and definitions of the crop area are given.
Crop area can be defined as “the horizontal projection of a particular extent of
earth’s surface “which corresponds to the area shown on cadastral maps (FAO
1982). This definition takes care of crop areas in the plains and in hilly regions.
It also ensures that the total area is equal to the sum of the component area,
which is not the case when an area is measured on slopes. Some of the
commonly used methods for estimating crop area are land surveying methods,
farmers’ appraisal and remote sensing. Because of natural calamities or
economic considerations, certain areas planted or sown with a given crop are
not harvested or are harvested before the crop reaches maturity. Reasons cited
for this are poor germination, pest or disease damage, animal grazing, floods,
lack of labor or lack of market. Thus, the area planted may not be equal to the
area harvested. In addition, some crops, such as cassava, may be grown as an
insurance measure and are only fully harvested during a drought or a food
shortage. In any of the above circumstances, the definition of crop area that is
4
used has a large influence on area, yield and production estimates. The concept
of crop area, therefore, needs to be subdivided into sown or planted area and
harvested area.
Sown or planted area: The area that corresponds to the total sown area for
producing a specific crop during a given year is termed as the sown or planted
area. Regulation (EC) No. 543/20091 on crops statistics defines this as cropped
area. Sown or planted area figures are required to estimate quantities used for
seeding purposes. Data on sown area, also, helps to come up with a rough
estimate of the production. The concept of planted area is used in such countries
as India and Bangladesh.
Harvested area: FAO (Martinez et al. 2015) and Regulation (EC) No.
543/2009 on crops statistics defines harvested areas the part of the sown or
planted area that is harvested. The harvested area may, therefore, be equal to or
less than the planted area. It serves as an important basis for obtaining a reliable
and accurate yield and production estimates. Both planted area and harvested
area concepts are used in Sri Lanka.
For practical reasons, most agricultural surveys and censuses record crop area
as the planted area instead of a harvested area. Casley &Kumar (1988),
however, argue that the harvested area is always the most relevant area
measurement for recording crop area and estimating crop yield at the plot level.
Yield estimates by daily recording, sampling harvest units, farmer recall, and
crop cards are based on planted area, whereas yield estimates by crop cut and
whole plot harvesting are based on the harvested area. On the other hand, for
the Uganda National Census of Agriculture and Livestock (1990-1991), crop
yields were determined on the basis of crop cuts using the planted area as the
crop area (MAAIF 1992).
Some additional concepts of area that may be useful in considering area
statistics (FAO 1982) are described below:
Area intended for planting (or sowing) refers to the area that the holders plan
or intend to sow under various crops. Area intended for planting may be equal
to the planted area, but it can be smaller or larger than the planted area. These
data are usually collected before planting starts.
Area tilled describes the part of arable land on which work has been done to
make the land fit for raising crops at a given point of time. The work involved
may comprise different practices, such as ploughing, harrowing and manuring.
1Regulation (EC) No 543/2009 of the European Parliament and of the Council. 2009.Crop
statistics and repealing Council Regulations (EEC) No 837/90 and (EEC) No 959/93, Official
Journal of the European Union, Special edition in Croatian Chapter 03, 27, 318 – 328
5
Area damaged gives an account of loss due to the effect of unfavorable factors,
such as floods, rain, winds, snowfall and insect attack.
Area abandoned is the part of the area intended or area planted that has been
abandoned for raising crop or harvesting crop for different reasons, such as
difficult weather conditions for raising the crop. Crop areas are sometimes
abandoned from harvesting if a poor harvest is expected.
Regulation (EC) No 543/2009 on crops statistics provides some more important
concepts and definitions:
Harvest year: The calendar year in which the harvest begins.
Utilized agricultural area: Total area taken up by arable land, permanent
grassland, permanent crops and kitchen gardens used by the holdings,
regardless of the type of tenure or its use as common land.
Area under cultivation corresponds to the total area sown or planted, but after
the harvest, it excludes the ruined area resulting from, for example, natural
disasters and calamities. Area under cultivation may be the same or smaller
than the sown or planted area.
Production area: In connection with permanent crops, production area means
the area that can potentially be harvested in the reference harvest year. It
excludes all non-producing areas, such as new plantations that have not yet
started to produce crops.
Main area of a given parcel: The area where the parcel has been used only
once during a given crop year is unequivocally defined as the main area of a
given parcel.
Crop Yield
The concept of crop yield is generally used to represent the average amount of
produce obtained per unit of the crop area, while the concept of production
covers the total amount produced (FAO 1982). Regulation (EC) No 543/2009
on crops statistics defines crop yield as the harvested production per unit area
under cultivation. In cases of tree crops the concept of yield covers the average
amount of produce per tree and the production is calculated as the product of
the average yield per tree and the number of producing trees. Some of the
commonly used methods for measuring crop yield are crop cut and farmers’
appraisal.
The three main concepts of crop yield being used by many countries are
described below: (Fermont & Benson 2011):
6
Biological yield or gross yield is the yield obtained before any loss
occurs during and after harvest;
Harvested yield is the biological yield minus harvest losses;
Economic yield is the quantity that the farmer can use after post-
harvest losses that may occur during cleaning, threshing, winnowing
and drying (Casley &Kumar 1988; Keita 2003).
Some of the important concepts and definitions relating to crops are discussed
below.
Primary crops: Primary crops are crops that come directly from the land
without having undergone any real processing apart from cleaning (FAO
Statistics 2011). They can be further divided into temporary and permanent
crops:
Temporary crops: Crops that are sown and harvested during the same
agricultural year, sometimes more than once.
Permanent crops: Crops that are sown or planted once and need not be
replanted after each annual harvest.
Sole crop: A crop grown in pure stand.
Mono-cropping: The practice of growing only one crop on a piece of land year
after year is termed as mono-cropping, such as growing only winter season
crops in a dryland area. This may be due to climatic or socioeconomic
conditions or the result of specialization of a farmer in growing a particular
crop. For instance, groundnut, cotton and sorghum are grown year after year
due to scarce rainfall in different countries. This term can be better described as
continuous cropping or continuous mono-cropping.
Mixed cropping: Mixed crops refer to two or more different temporary and
permanent crops grown simultaneously in the same field or plot (FAO 1982).
Each of these crops is referred to as associated crops. As per Regulation (EC)
No. 543/2009 on crop statistics, a combination of crops that are cultivated on a
parcel of agricultural land at the same time is termed as “combined cropping”.
For example, in Brazil, cocoa is planted with clove and rubber and to some
extent with coconut (Alvim &Nair 1986).Uganda National Census of
Agriculture and Livestock (1990-1991) indicates that80 to 90 percent of their
planted area are in mixed stands (MAAIF 1992).Under this cropping scenario,
it is recommended that the estimated area for each one of the associated crops
be the area that the particular crop would have covered if it had been grown
7
alone (FAO 1982).The area under cultivation is distributed between the crops in
proportion to the area of the land they are cultivated on.
To elaborate on mixed cropping, the following terms need to be defined:
Intercropping: The practice of intercropping refers to growing more than one
crop in the same land area in rows of definite proportion and pattern. When a
particular crop is planted between rows of another crop it is usually referred to
as an interplanted crop. A good example of this is planting sorghum and
groundnut between rows of cotton. In Asian and African countries,
intercropping is a widespread and traditional cropping system because of scarce
land and small field size. Thus, intercropping has the potential to increase
natural resources in space and time. Intercropping is commonly used because it
gives higher returns per unit area than sole cropping. It acts as an insurance
against crop failure in an abnormal year. It also helps the soil fertility as the
nutrient uptake is made from both layers.
Intercropping can be further divided into the following subcategories
(Vandermeer 1992):
Mixed Intercropping: Growing two or more crops simultaneously on
the same piece of land with no distinct row arrangement is called mixed
intercropping. This practice is more commonly applied in traditional
and subsistence farming in many developing countries. For example, in
some parts of India, large numbers of crops are sown in mixed intercrop
arrangement.
8
Figure 1: Pearl millet, Maize, Sunflower, Ragi, 2 types of Sorghum, Groundnut, 2
Green leafy Vegetables in India
Source: http://agropedia.iitk.ac.in/content/mixed-cropping-pearl-milllet.
Row Intercropping: Growing two or more crops simultaneously where
one or more crops are planted in rows is called row intercropping. This
pattern is usually found in areas involved in intensive agriculture where
the plough has replaced the machete and fire is used for land
preparation. As an example, in parts of India, pearl millet-groundnut
intercropping and sorghum-pigeon pea combinations are sown as row
intercropping (figure 2). Figure 3 shows wheat-pea intercrop fields in
the United States of America.
9
Figure 2: Sorghum-pigeon pea row intercropping in India
Source: http://agropedia.iitk.ac.in/content/intercropping-pearl-millet
Figure 3: Wheat-pea row intercrop in the United
Source: Machado 2009
Strip Intercropping: Growing two or more crops simultaneously in
different strips wide enough to carry out independent cultivation, but
narrow enough for the crops to interact agronomically is called strip
intercropping. This form of intercropping is more common in highly
modernized systems, especially in systems that require intensive use of
machinery. For example, maize, soybean and other cereals are planted
as strip intercropping in the United States (figure 4).
10
Figure 4. Maize, soybean and other cereals in the United States as strip intercropping
Source: http://orgprints.org/18950/
Relay intercropping: A technique in which different crops are planted
during different time periods in the same field and both (or all) crops
are being gown simultaneously at least part of the time is called relay
intercropping. This form of intercropping may actually include the other
three as subsets, as its primary categorization variable is time. In
Northern China, farmers harvest wheat and maize within one growing
season under a relay intercropping system (Knörzeret al. 2009).
Continuous cropping
Continuous crops or successive crops or sequential crops or catch crops are
crops that are sown and harvested from the same piece of land previously
occupied by another crop, or even by the same crop, during the same
agricultural year. The area of crops growing under this condition is accounted
for in the total crop area and if necessary, ad hoc surveys for this purpose
should be conducted (FAO 1982). Continuous cropping can take different
forms:
Continuing planting/harvesting: Repeated planting and harvesting of
crops at particular intervals of time in an agricultural year is termed as
continuing planting/harvesting. The practice of continuous planting is
very common in many African countries. For example, farmers in
Uganda plant and harvest crops throughout the year.
11
Successive cropping: Planting and harvesting either the same crop or
different crops more than once in the same field during the agricultural
year (one crop is planted after the other crop is harvested) is termed as
successive cropping.
Some other forms of continuous cropping are found by:
Replanting the same crop on the same land after it has been damaged
(totally or partially) through natural or other causes.
Enlarging gradually (at interval of times) the area of land planted to
one or several crops.
12
3 Methods of estimating crop
area The information on crop area statistics forms the backbone of an agricultural
statistical system. The crop area should cover the entire area devoted to each
crop, including, when necessary, estimates for smaller areas not covered in the
current annual area surveys.
The area under crop in each season and yield rate is required to estimate the
production of a particular crop. As farmers grow crops under different cropping
systems, such as mono-cropping, mixed cropping, continuous cropping or
repeated cropping, a sound methodology is required to obtain the area under a
particular crop. This can be obtained with relative ease when a single crop is
grown in a field in a particular season, but it becomes difficult under mixed
cropping or intercropping. It becomes even more difficult in areas where the
land records and crop registers are not maintained properly.
Crop area often has a strong inter annual variability. Crop yield remains
relatively stable, barring arid countries during normal climatic conditions, but in
cases in which there is variability in weather conditions with droughts or floods,
crop yield may be variable. Area statistics is generally generated using a sample
survey approach. However, with more emphasis being placed on disaggregate
level planning, there is an increased need for estimating crop area with respect
to, among other factors, different varieties, irrigation availability and soil type,
etc. Advancements in computer and space technology during the past few
decades has led to the availability of compact and high performance computing
systems that are well suited to the demands of remote sensing satellite data
processing. Remote sensing satellite data are advantageous because of the vast
area coverage, synoptic view and online information. A number of
methodologies are suitable for providing acreage estimates for major crops
using remote sensing satellite data. Remote sensing integrated with geographic
information system (GIS) technologies are also being used for crop acreage
estimation. The Global Positioning System (GPS) is being used to identify the
13
selected plots for estimation of the corresponding plot area. The various
methods for area estimation are broadly grouped into two categories, as
discussed below.
3.1. METHODS BASED ON MAPPING
Mapping is essential for crop area estimation. Crop areas can be easily
estimated when large-scale accurate maps depicting the actual positions of
parcels and fields are available. In many countries such maps are compiled and
kept up-to-date by the cadastral services to generate revenue. Cadastral maps
show the boundaries and area of each parcel of land together with an
identification number. The name of the owner and other characteristics of the
parcel are also kept in a separate register. In many developing countries such
cadastral maps do not exist or do not cover the whole country. In the European
Union, cadastral maps for taxing purposes seldom correspond to the real
cropped parcels and cannot be used for crop statistics. This situation is worse in
most developing countries. Yearly administrative registers are usable in a few
countries, such as Scandinavian countries, which have few large farms and only
a marginal amount of small plots for hobby, which are often excluded from the
registers. In many countries, survey maps are prepared on the basis of aerial
photographs. This process can be problematic because coverage based on aerial
photographs may not be complete. Additionally, the scale may vary from
region to region and the maps and photographs may not be up-to-date. Some
developing countries, such as Algeria, have undertaken plot mapping activities,
in order to define a sampling frame, but not for direct area estimation. This may
be useful, but is costly. Direct area estimation from classified satellite images
usually has a large bias that becomes enormous if the agricultural landscape is
complex. Various methods adopted for estimating crop area using maps were
described in detail by FAO (1982). Craig & Atkinson (2013) provide a vide
literature review on crop area estimation methodologies. Various method of
crop area estimation are elaborated below.
3.1.1. AREA FRAME OF SEGMENTS WITH PHYSICAL
BOUNDARIES
For effective implementation of area sampling, a complete set of large-scale,
accurate and detailed maps and/or aerial photos/imagery of a study area is
required. Updating regularly the maps, so that they are error-free (there is no
omission or duplication), is a practical necessity. In addition, the maps should
cover the entire study area. Subdividing maps/photos/imagery into segments is
a prerequisite. To the extent possible, the segments should be "natural
geographic areas" established by natural borders, such as ridges, rivers and
14
roads. When these segments are numbered and perhaps characterized through
the recording of ancillary information, the maps or photos/imagery are
transformed into frames of area sampling units from which samples of
segments to be observed are selected.
When the cadastral maps are available, the fields are numbered and those
falling within the sample segment can be identified and listed. The lists of
selected fields are sent to the enumerator who is assigned a given area. The
enumerator records the name of the crop for each of the fields falling in his/her
assigned area. A precondition for proper implementation of this technique is
that the boundaries of the listed fields should be clear and well known and
clearly designated in local records. In situations in which more than one crop is
grown in the field, the area occupied by each crop can be determined either
subjectively or by measurement. Subjective determination of a crop area is
usually a rapid and cheap process, but it also may not be very accurate. On the
other hand, determination of a crop area through measurements may be very
accurate, but it can be very expensive.
3.1.2. AREA FRAMES OF SEGMENTS WITH A REGULAR SHAPE
This method is implementable when large-scale, accurate and detailed maps or
photographs/imagery are available. It is also known as grid sampling. Under
this method, small administrative or agro economic regions are covered by a
large number of non-overlapping photographs/imagery or maps, which can be
used as primary sampling units. On each sample map or photograph (when
maps or photos/imagery are considered as primary sampling units), a grid is
superimposed in which each square has a fixed known area, such as 10
hectares. The ultimate sampling units are squares on the grid, which are
identified based on coordinates. The crops in the different fields within the
selected sample square are then identified on the ground. Thus, in this method,
the total area of each of the sampling units is fixed. It is expected that the data
in the grid method of sampling shall have smaller variability because of the
equal size of the ultimate sampling unit. Furthermore, development of estimates
at a higher level is also easy, but identification of the boundaries of a square
may be difficult. The boundaries of the square do not need to be identified on
the ground, but problems can arise if the actual field boundaries do not coincide
with the boundaries visible on images (this may also happen for segments with
physical boundaries).
3.1.3. POINT SAMPLING
As for point sampling, the second stage sampling units within the primary
sampling units are points and the primary sampling units are the area covered
15
by a map or a photograph/imagery. The points are selected either randomly or
systematically, such as by the points of intersection of the lines in a grid. The
enumerator is provided with an enlarged photograph/imagery on which the
sample points are drawn as the intersection of two branches of a cross. The
enumerator goes to the precise place and takes note of the type of soil, the land
use category and the crop. Notably, point sampling methods are not necessarily
a two-stage sampling.
Merits: A positive aspect of the point sampling technique is that the estimation
of crop areas appears to be simpler than the actual measurement of the areas of
the fields. Also, an enumerator can visit from 50 to 100 points on the ground
identified on an aerial photograph/imagery in a day's work, a far greater number
that is possible when using other techniques.
Drawbacks: The main problems faced during the implementation of this
method are: difficulty in identifying the exact location of the sample point on
the ground; identifying the type of cultivation; and potential inaccuracy of the
results when the area under the crop is a small fraction of the total area covered
by the photo. Many countries have needed to estimate crop areas using this
method. However, the results have not been satisfactory. The introduction of
GPS/Tablet/Smart phones for the field work has changed substantially the
problem of locating the point. An issue that remains problematic, however, is
clearly stating the priority between GPS and imagery if they are inconsistent
with each other. Priority should be given to the image.
The LUCAS (Land Use/Cover Area-frame Survey) was launched by the
European Union in 2001 based on a non-stratified, systematic, two-stage
sampling scheme, with primary sampling units (PSUs) defined as a rectangle of
1500 × 600 m following a grid of 18 km (Delincé 2001; Bettio et al. 2002). In
each PSU, 10 points (SSUs) were selected arranged on two rows of 5 points
with a step of 300 m. The “point” (SSU) was defined as a circle with a radius of
3 m to be consistent with ground survey specifications. For LUCAS 2006
(Gallego 2007), the sampling plan changed and became a two-phase sampling
plan of unclustered points (always defined as squares of 3 m). From 2002, the
Italian AGRIT survey (Martino 2003; Carfagna 2007) also adopts a sample
design based on unclustered points. It is a single stage two-phase sampling: the
first phase gives a systematic sample of unclustered points that are
photo/imagery-interpreted and subsampled (second phase) with higher rates in
agricultural strata. A similar approach was tested in Greece in 2004.
Jinguji (2014) introduced a new survey method known as the dot sampling
method by combining a traditional attribute survey method with two current
information technologies, namely Excel and Google Earth, to estimate rice
16
planted area in Japan. This method has been effective in developing countries,
such as Sri Lanka and Thailand, and thus shows that it is possible to execute a
survey that is simple, reliable, and cost-effective in comparison to existing
methods. The survey results obtained through dot sampling method were closer
to official estimates. However, it must be noted that it may be difficult to adopt
this method in countries where critical Internet infrastructure required for using
Google Earth is inadequate. Another complication of using Google Earth may
be use of multi-date images with varying resolution resulting into inconsistency
in delineation of various crops. However, this application can be used even if
Internet access is not good with screen captures (geometrically corrected) or
even printouts on paper, but rules need to be established when the images are
updated and not geometrically consistent.
3.1.4. REMOTE SENSING AND GEOGRAPHIC INFORMATION
SYSTEM
Remote sensing is an important tool for generating agricultural statistics.
Remote sensing and geographic information system (GIS) technology has been
widely adopted to estimate crop area statistics.Classified satellite images and
land cover maps produced by photo-interpretation are useful tools for this
purpose. Direct use of satellite images in terms of pixel counting for area
measurement or simple area measurements of polygons of a land cover map is
not recommended.
Initially, three broad approaches for use of remote sensing to generate crop
statistics were recognized:
(1) Remote sensing forms a base for estimating parameters of spatial
variability through area frame sample design. It provides an efficient
and low cost stratification based on crop proportion derived from visual
interpretation or digital classification of remote sensing data;
(2) Direct and independent estimation that uses remote sensing data and a
recognition technique to estimate the crop area in the study region.
(3) The use of remote sensing data as an auxiliary variable helps make the
estimates based on ground surveys more precise and reduces the amount
of the field data to be collected, if the precision to be reached is fixed.
On the contrary, if the sample size is fixed, this approach provides
higher precision of the estimate.
17
Crop acreage estimation using remote sensing data
The steps involved in the crop acreage estimation using remote sensing data
are:
(a) Study area extraction
As satellite data are available in the form of scenes covering fixed areas, the
acreage estimates are obtained by overlaying the administrative boundary on
the scenes and masking out pixels outside the boundary using a point-in-
polygon algorithm. Raw scenes can be used for overlaying boundaries by using
control points, but the scene rectification is carried out for mosaicing map
outputs or analysis in the GIS system. To sample an area and conduct a rapid
crop inventory, a rigorous sampling approach is required in which sample
segments in the form of square or rectangular sub scenes are extracted for
analysis. This sampling approach requires that the following parameters be
defined in a study: segment size; spatial stratification; sample allocation; and
sampling fraction. For LACIE 5 nmi ×6 nmi (9km×11km) segments and a2.5
percent sampling fraction was adopted (MacDonald and Hall 1980). Other
schemes have been adopted for other studies. Hallum and Perry (1984) have
proposed an objective methodology for defining the optimum sampling unit
size, which takes into consideration non-sampling errors in remote sensing, as
well as a model of sampling error variance as a function of segment size.
(b) Crop discrimination/identification from satellite data
Crop discrimination/mapping using space data is carried out either by visual or
digital interpretation techniques. Visual techniques are generally based on
standard false color composite (FCC) generated using green, red and near-
infrared (IR) bands assigned blue, green and red colors. Haack and Jampoler
(1995) demonstrated that a color composite formed by the best three bands (TM
bands 3, 4 and 5) gave better discrimination in comparison to the standard FCC
over a study site in the Imperial Valley in California. The digital techniques are
applied to each pixel, use a full dynamic range of observations and are preferred
for crop discrimination. A multi-temporal approach is used when single-date
data do not permit accurate crop discrimination.
In this case, the procedure entails the following three stages:
(i) Pre-processing: The pre-processing includes multi-date registration and
removal/ minimization of atmospheric effects through radiometric
normalization or atmospheric correction. The bands from the multiple
acquisition dataset are used in the multi-date classification procedures (Bizzell
et al. 1975; Bauer et al. 1979; Hixson et al. 1980).
18
(ii) Data compression: Data compression techniques are commonly adopted.
For more than two dates, the number of bands becomes very large. The first
four principal components (PC) from three date data from TM (15 bands,
Band1 to Band5) in Germany (Mauser 1989) gave higher accuracies than
various band combinations.
(iii) Image classification: This is the major step involved in area measure as
the estimate depends on classification and accuracy of classification only. The
main techniques applied for classification of remote sensing satellite imageries
are (Vibhuteet al. 2013):
Supervised classification: In this approach, it is assumed that prior
knowledge for the classification of land, namely land cover types in
specific sites, is available. The most useful classifiers of this type are
the maximum likelihood classifier, the minimum distance classifier, the
parallelepiped classifier and the Mahalanobis classifier.
Unsupervised classification: In this category of methods, there is no
prior knowledge of the area to be classified. The two most common
classifiers of this group are K-means clustering and Iterative Self-
Organizing Data Analysis.
Hybrid classifier: These methods are a combination of supervised and
unsupervised classifications.
Fuzzy classifier: These classification methods are based on fuzzy logic.
Supervised/unsupervised and hybrid approaches are commonly used to classify
images. Techniques involving graphical shape, such as profile modeling
techniques, angular measures and Delta classifiers have also been used
(Badhwar 1984). Campbell et al. (1987) evaluated the direct use of temporal
spectral data for wheat acreage estimation in Australia. Using a discriminant
function on total data set improved separation, as compared to a profile-based
approach, with loss in separability occurring at both the data reduction steps,
namely spectral to the vegetation index (VI) and multi-date VI to spectral
profile step. Belward and de Hoyos (1987) compared accuracy for crop
classification between supervised maximum likelihood (MXL) and binary tree
classification approaches. Although similar accuracies were obtained by the two
classification procedures, the binary tree approach seems to be a viable
alternative to MXL in terms of ease of application and training of datasets.
Knowledge-based crop classification was suggested by Janssen & Middelkoop
(1992) for situations in which the crop rotation information about the area was
formalized using Markov chains and a transition matrix. These conditional
probabilities and remote sensing data were input for a Bayesian image
19
classification. Many of these approaches are still at the research level and are
not being applied for practical purposes.
(c) Estimation of area under a crop in the study area
The estimation of a crop area from the classified image generated by the crop
discrimination procedure is the final step for obtaining crop area estimates.
Some of the estimators used are:
Direct estimator
This is the simplest estimator, where in a study region of known total area D,
the crop area (Zc) is given as:
Zc = D × (Xc/N)
where, Xc and N are number of pixels in crop C and total number of pixels,
respectively.
Depending upon the classification accuracy, the estimate could be biased. The
information on the classification accuracy is used in the next set of estimators
for obtaining improved crop area estimates (Maselli et al. 1990).
Global estimate using confusion matrices
Let A={Aij}be the confusion matrix on a test set, Ai the number of pixels
classified into crop Ci(ground truth), Aj the number of pixels classified into land
use Cj. Then, the elements of Pr= {Prij} and Pc= {Pcij} matrices are defined as:
Prji = Aij/Ai and Pcij = Aij/Aj
If Pr and Pc are unbiased estimators of the corresponding matrices for the whole
population, the next area estimators are unbiased too (Hay 1988; 1989; Jupp
1989):
Zdir = D ×(Pc×X/N) and Zinv = D ×(Pr-1×X/N)
This estimator exploits a larger part of the information contained in the
confusion matrix and gives good results, but has not been widely adopted.
Regression estimator
Regression estimator uses both ground data and classified remote sensing data
for acreage estimation. The regression estimators are described in standard
statistical texts. The formulation of separate form is:
(Re )
1
ˆ L
R h h g
h
Y N y
20
where, (Re )ˆ ( - ) h g h h h hy y b X x
hy = average ground-reported crop area per sample segment of stratum h, i.e.,
1
1 hn
h hj
jh
y yn
ˆhb = regression coefficient of ground-reported area on remote sensing-derived
area based on nh segments for stratum h.
hX = average remote sensing-based area for all frame units of stratum h (Thus.
the entire area must be classified to obtain this mean of the population, namely,
1
1 hN
h hj
jh
X XN
hx = average remote sensing-based crop area per sample segment of stratum h,
i.e., 1
1 hn
h hj
jh
x xn
)
This approach has been extensively studied in the United States where in the
June Enumeration Survey, the crop data are collected on a sample basis through
a questionnaire method. Other examples of application of regression approach
are potato and canola-rapeseed in Canada using Landsat MSS (Ryerson et al.
1985) and wheat in Brazil using Landsat MSS (Moriera et al. 1986). Gonzalez-
Alonso & Cuevas (1993) show improved performance by using regression
estimator with confusion matrix information over a test site in Spain. Gonzalez-
Alonso et al. (1994) have compared results from the regression approach using
two different sampling approaches, namely square sample segments and
irregular segments over a test site (900 km2), in Spain. The weighted relative
efficiency in the case of the square segment approach was higher than for the
irregular segment approach.
In recent times, much work has been done in the field of remote sensing for
area estimation. Gallego (2004) gave an overview of different ways to use
satellite images for land cover area estimation. Carfagna & Gallego (2005)
discussed remote sensing as a valuable tool for agricultural statistics when area
frames or multiple frames were used. Sahoo et al. (2005) developed an
integrated approach based on remote sensing, GIS along with survey data for
crop area estimation under paddy crop in the North-Eastern hilly regions of
India. In those regions, particularly Meghalaya, different crops are grown at
different elevations. For example, paddy is grown in valleys, pineapple is
grown on hill slopes and potato and ginger are grown on relatively flat surfaces
21
on the hill top. Thus, elevation plays a significant role in growing crops in those
regions. Furthermore, the total cropped area in the state is also much less (only
10 percent) and is mostly scattered throughout the state. These conditions make
it difficult for the usual stratification criterion based on administrative
boundaries to provide accurate/efficient strata. Based on the fact that elevation
and extent of cultivation plays a major role in influencing the acreage under a
crop, Sahoo et al. (2010) suggested using spatial stratification based on
elevation and the extent of cultivation for crop acreage estimation of multiple
crops in North-Eastern hilly regions in India.
Sahoo et al. (2012) extended the integrated methodology in Jaintia Hills district
of Meghalaya for estimation of acreage under paddy crop in India. Extensive
cloud coverage makes it difficult for researchers to estimate crop area using
remote sensing satellite data for kharif crops. Sahoo et al. (2013) developed
methodology for generating cloud-free images, which can be used to provide
reliable estimates of crop acreage and a methodology for estimating crop area
directly from satellite images having cloud cover and shadows. Goswami et al.
(2012) highlighted the application of remote sensing and GIS technologies for
the wheat acreage estimation for Indore district, Madhya Pradesh, India.
Wu and Li (2004) described a new method of crop area estimation using remote
sensing based on a stratified two-stage sampling method. To develop the strata,
physical factors, such as temperature, precipitation, soil type, sun eradiation and
proportions of main crop types, were considered. Crop areas were estimated
using cluster sampling assisted by remotely sensed images where the cluster-
sampling frame was built using 1:100000 scaled map-sheet. It had been difficult
to extract crop area from remotely sensed data, such as Landsat-TM. However,
this hurdle was overcome by first estimating crop proportion (Wu and Li
2004).Remote sensing technology is being used by some countries to generate
agricultural statistics, as this technology enables the actual ground situation to
be presented efficiently in which the agricultural features are evident and
clearly visible on the images. This method, however, is difficult to use in
countries where agricultural practices are dominated by small plots, diverse
planting dates, dispersed trees and intercropping systems. Even in the United
States where agriculture is dominated by mono-cropping on large fields, the use
of remote sensing techniques is still limited to supplementing area estimates
obtained using other methods in a few selected states. There are other factors
that limit the intensive application of remote sensing for collection or
generation of agricultural statistics, particularly crop area and crop yield.
Among them is crop discrimination, which depends on classification techniques
adopted for satellite data analysis. Classification of satellite data is a subjective
22
method, as it depends on a number of factors, including, among them, ground
truthing, number of ground control points (GCPs) considered and method of
classification adopted. To test the classification, an accuracy assessment is done
which is also a subjective method, as it depends on the number of GCPs
considered for the accuracy assessment. An important aspect to be considered
when using remote sensing satellite data is the question of choosing satellite
data with appropriate spatial resolution. The satellite images are of varying
spatial resolutions. The most appropriate resolution for crop area or yield
estimation is debatable as satellite data of different pixel size/pixel purity are
available and should be carefully analyzed before finalizing the satellite data to
be used. The complexity of the landscape is also an important element that must
be investigated as crops also grow in hilly regions with undulating topography
and variable topographic geometry. This also plays an important role in satellite
data classification and affect the accuracy of classification. Another important
aspect is the cost involved in setting up the infrastructure, which includes cost
of software and satellite data and training of manpower for using satellite data,
which tends to on the high side.
Merits: This method provides quick crop area estimates covering a vast
geographical area. It is also useful for obtaining estimates of areas in hilly
terrains and in areas that are inaccessible.
Drawbacks: This method is expensive. There may be problems in getting
estimates for areas under cloud cover. The area estimates may not be accurate
for small plots.
3.2. METHODS BASED ON LAND SURVEYING
Land surveying determines the form and extent of a portion of the earth’s
surface by measurements. It may be linear or angular. Choice of a particular
method depends on the availability of resources, including, for example,
manpower or instruments used for the survey. Another important factor in these
surveys is the requirement of the level of accuracy of the results. In general, the
methods for measuring crop area in various countries are costly and time-
consuming. Various methods of measurement of crop area are described below:
3.2.1. POLYGON METHOD
This method, also known as the traverse measurement, traversing, chain and
compass, or the Topofil method is one of the most prevalent traditional ones
used to measure crop area (MAC 1965; Schøning et al. 2005). It can be
considered the gold standard for crop area estimation in view of its potential to
provide accurate area. In cases in which the plots are of a regular shape, the
23
method involves measuring the length of each side and the angle of each corner
using a measuring tape and a compass. The surface area of the plot can then be
calculated using trigonometry (FAO 1982). In cases involving an irregular
shaped plot, an approximate polygon is obtained with straight sides by
demarcating its vertices on the ground. Due care is taken to ensure balancing
the protruding pieces left out in the process by including other small pieces that
are not part of the plot. During the give-and-take process and during the
measurement process, errors are introduced. According to Casley & Kumar
(1988), in situations in which the polygon does not close and the closing error is
larger than 3 percent of the perimeter of the polygon, the measurement
procedure should be repeated. The polygon method is commonly considered the
most objective method to accurately estimate crop areas. Diskin (1997)
observes that it may even be worth spending extra time, training and cost to
pursue this method. Nigeria widely deploys this method for estimating crop
area.
In this method, first the boundaries of a field to be measured are identified by
use of sight poles, and taking compass hearings and measuring the length of
each side of the obtained polygon. The traditional procedure of evaluating the
area of a field on the basis of measurements entails plotting the field in the
office by using a ruler and a protractor and then measuring the area of the
sketch by using a planimeter or grid paper. These methods were first
implemented in the 1974 Census of Agriculture in the Côte d'Ivoire. The
Statistics Division of FAO (FAO 1982) has developed several methods for
calculating areas with programmable calculators. A description of the
calculation of the area using the polygon method is given in annex1.
Merits: This method often provides accurate area measurements and can be
used directly in the field when measurements are made. The closure error can
be evaluated directly on the spot, and when the error of the measurement is
considered to be too large, the process can be repeated.
Drawbacks: Obtaining area measurement through this method is time-
consuming.
3.2.2. RECTANGULATION METHOD
Some land parcels and crop fields have irregular boundary lines that need not
be the straight lines. To accomplish this, the rectangulation method is
frequently used to measure the crop areas. The method consists of determining
the length of the parcel or field by measurement somewhere more or less across
the middle and then the area is obtained by determining the average width
through eye estimation. The method was slightly improved by having three
24
measures of the width: two near the two ends of the field and one in the middle.
It was further improved by measuring the width at a large number of equidistant
positions, thus dividing the total area into a number of rectangles, more
precisely a number of trapeziums. A graphical illustration of rectangulation
(Figure 5), as given in FAO (1982), is shown below:
Figure 5: Rectangulation method
Feasibility: The method is feasible for land parcels having almost regular
shapes.
Merits: When the shape of the field is not too complicated, the method of
rectangulation for estimating crop areas can be useful and reliable.
Drawbacks: Application of this method on the ground can be difficult,
resulting in substantial errors in the measurement of area. Additionally, in order
to measure the length and the different widths, the enumerator may have to
enter in the field and in the process step on the crop, which would be
disagreeable to the farmer.
3.2.3. TRIANGULATION METHOD
To carry out this method, the enumerators split the field into a number of
imaginary triangles and measure the height and base of each triangle using a
measuring tape (FAO 1982). The total area of the field is obtained by adding
the area falling under each of the individual rectangles and triangles. A
graphical illustration of the triangulation method (figure 6) as given in FAO
(1982) is highlighted below:
25
Figure 6: Triangulation method
Feasibility:
When the boundaries of the field are rectilinear and the field is a plane
polygon with well-defined vertices;
When all the vertices of the polygon representing the field can be seen
from one particular point;
When all the distances between the fixed point and the vertices can be
easily measured. This condition implies that either the farmer allows the
enumerator to measure the distances across the field (with the risk of
trampling the crop) or that the distance measuring instrument eliminates
the need for the surveyor enters the field.
Merits: The advantage of triangulation over other area measuring techniques is
that it requires that only the distances be measured as the triangle is uniquely
determined when its three sides are known.
Drawbacks: Triangulation does not permit the direct discovery of errors of
measurement.
The triangulation method is being used in Nigeria for crop area estimation. A
pilot study was conducted in 1963 for the 1965 agricultural census in Uganda,
in which the rectangulation and triangulation method were compared with the
polygon method (MAC 1965). It was found that though the polygon method
gave accurate area figures, it took a longer time. In addition, the polygon
method required two enumerators instead of just one needed for the
triangulation method. The study results also revealed that the rectangulation and
triangulation method underestimated the total cultivated area and area per
26
holding by approximately 5 percent whereas some individual crop areas were
underestimated by 12-15 percent enumerator error, tedious and time-consuming
measurements are some other problems associated with this method. However,
Muwanga-Zake (1985) pointed out that the enumerator bias may be substantial
in districts that are covered by only a few enumerators.
3.2.4. P2/A METHOD (PERIMETER SQUARED OVER AREA)
If the perimeter is known, the area of a field can be quickly estimated, which is
used to check the gross errors of calculation, such as misplacing decimal point.
This is done by dividing the perimeter by a number between four and five and
then squaring the result (FAO 1982). The choice of the divisor is subjective and
depends on the degree of complexity of the boundary, such as the number of
sides or number of concavities. Thus, if the field is very complicated, the
divisor should be nearer to five and if the field is close to being a square or
rectangle then the divisor is nearer to four. When the field is highly variable, the
divisor might exceed even five.
The P2/A method, where P stands for perimeter and A for area, is a subjective
method based on a relative stable relation between the perimeter squared of a
field and its area (Mpyisi 2002; Fermont & Benson 2011). By using this
method, it is possible to get an estimate of a field area quickly, making it a good
option in situations in which there is an inadequate number of enumerators. The
method is relatively inexpensive and less time- consuming. The value of ratio
between P2 and A depends on the complexity of the shape of the plot.
A study in Rwanda showed that the simple correlation between perimeter
squared and area was found to be 0.95. Thus, a field's perimeter could be used
as a rough substitute of its area by utilizing the fixed ratio. A comparison
between P2/A method and the polygon method showed that P
2/A method
provided accurate estimates of area with a net error as low as 2 percent (Mpyisi
2002). This methodology has been used in the Food Security Research Project
under the Agricultural Statistics Division of the Ministry of Agriculture,
Rwanda to conduct surveys on a national sample with a limited number of
enumerators at a reasonable cost. However, based on experiences and analysis
conducted on thousands of plots measured during the Agricultural Census of
the Ivory Coast, the P2/A method failed produce any acceptable results and at
the same time, no mathematical relation could be established between the area
and perimeter of a complex polygon.
Merits: The main advantage of this method is that it is possible to get estimates
of a field area quickly with limited manpower. Another advantage is that it is
relatively inexpensive.
27
Drawbacks: This method is subjective and therefore may not give accurate
estimates.
Measuring distances
The above-mentioned methods require measurement of distances whether it is
length or breadth of a rectangle or a base and the height of a triangle or the
perimeter. Consequently, the methods for measuring distance form an important
component of all these methods. In developing countries, most of the farmers
are unaware of the magnitude of the areas under the crops grown in their fields.
Accordingly, field staffs are employed to collect area statistics.
i) Pacing
Pacing means walking at a normal gait and counting the number of steps to
cover a certain distance. The steps are then converted to standard units. To
begin with, an average length of steps (usually 0.83m) is used by the
enumerators. Since there may be individual differences in steps, the step of each
enumerator needs to be calibrated by pacing a well-known distance and use that
as the conversion factor.
Merits and drawbacks: It has been observed that the length of the pace of
even the same enumerator changes with the change in the type of surface, such
as sandy soil, clay and uneven ground. The length of the pace also varies with
the enumerator's state of health and level of fatigue. Therefore, it has become
necessary to calibrate the step several times a day, which has taken away most
of the advantages of the pacing method.
There is also a risk of miscounting the number of paces, especially when this
number is large. In order to eliminate this risk, a simple instrument, the
pedometer, was proposed to be used. A pedometer, which consists of a digital
reader and a dial, measures the movements of the body; each step taken is
registered and shown on the dial. Each pedometer needs to be tested before uses
in case some of them may be out of order.
For the above reasons, the pacing method for measuring the length of sides or
diagonals of a field is not recommended and, in fact, has been discontinued in
almost all countries. However, it can be used, even without calibration, when
random points are to be selected within a field for the purpose of laying crop-
cutting plots.
ii) Measuring Distances with Instruments
A commonly used method for measuring distances involves standardized cord
and a wooden pole. A cord of fifty meters has been used for the allotment of
28
parcels 50m × 50m of communal land to village members in many African
countries. To estimate crop areas, the cord needs to be of a non-extensible
material and care should be taken to avoid getting it wet, otherwise, its length
would be altered.
For example a wooden pole, the kassaba, of 3.55m, has been used to measure
the sides of fields in Egypt. A common source of error encountered in large
fields is the miscounting of the number of kassabas.
a) Surveyor's chain
The classical method for measuring distances is with a surveyor's chain.
The metallic chain is composed of straight links with circular ends connected
by rings with a handle. Each link is 0.20m long measured from the center of
one connecting ring to the center of the next. The usual chain length is 20m
(100 links) but there are 10mand 50m chains. Similar chains graduated in yards
and feet are also available (FAO 1982).
Two men are required to measure a distance, say AB, with a chain: one man
holds one end of the chain at the point A while the other stretches the chain on
the ground along the direction AB and marks the point A1, corresponding to the
end of the chain. Then, the first man moves to point A1 and the operation is
repeated as many times as necessary. The distance AB is calculated as so many
complete chains plus a number of links.
Merits: The advantage of the chain is that it is a cheap and secure instrument.
Drawbacks: It is heavy and not easy to handle. If not handled carefully, the
links often tend to bend, which reduces its length and results in overestimating
the distance over a long distance. There is also the risk of forgetting to count a
chain length.
When the ground is uneven and the chain is not placed on the ground but held a
few centimeters above, a slight error may be introduced due to catenary effect.
b) Tapes
A low cost instrument for measuring distance is metallic or plasticized tape,
which is a substitute for surveyor's chain. Tapes are wound on a special reel and
are graduated in meters, decimeters and centimeters or in yards, feet and inches.
They are available in different lengths, say 20m, 30m, and 50 m or 50 feet and
100 feet. The distances are determined in the same way as with chains.
29
Merits: The tape is not liable to bend and the catenary effect is almost
inexistent. It is easier to handle and generally more accurate.
Drawbacks: The tape is likely to break easily. It may get rusted if not cleaned
after use and the plasticized tapes may lose their markings of the graduations.
Although the above measuring instruments (chains and tapes) are not very
costly, the operating expenses are high as two persons are needed to do the
measurements. The remuneration of two persons, even if one of them need not
be a professional enumerator but simply a laborer, in the long run, is more
expensive than the use of more costly instruments that can be managed by a
single person. Such instruments are the topofil, the Trumeter or Smith Wheel
and the optical range finder.
c) Topofil
The topofil is a distance measuring device fitted with a non-recoverable, light
but strong string and a counter that registers distances in decimeters, meters and
hectometers. The string runs out of the instrument as the enumerator walks the
distance to be measured. It can be easily carried and used by the enumerator.
The process is the following: the enumerator fastens the end of the string to a
fixed point and sets the counter at zero; as the enumerator walks the distance,
the string unrolls and the counter registers the length of the string unrolled; at
the terminal point, the enumerator reads on the counter the length of string
unrolled, which is then cut and discarded.
Merits: Any distance not longer than the length of the string on the reels can be
measured in one single operation (maximum length 5480m) using topofil. The
speed of measurement matches the gait of the enumerator; the enumerator can
read the counter at any intermediate point and set back the counter to zero, and
as the distance is recorded mechanically, there is no danger of miscalculating
long distances.
Drawbacks: The apparatus is costly, with a high running cost as a reel can be
used to measure at the most 20 fields of small dimensions (about 5 ha.).The
topofil case is a bit heavy and therefore inconvenient for carrying over long
distances, and the string sags slightly and may even rest on the ground or on the
plants.
d) Graduated wheel/ Trumeter / Smith wheel
The main elements of a Trumeter or a Smith wheel are a graduated wheel, a
handle to push it and a counter, which registers the number of revolutions of the
wheel. The circumference of the wheel is either one meter or one yard. At the
starting point, the enumerator sets the counter at zero and pushes the wheel
30
along the line until the length of the distance to be measured. The reading on
the counter plus the length corresponding to an incomplete revolution gives the
length of the distance under consideration.
Merits: The cost of a graduated wheel is not very high and there are no running
expenses. The instrument is easy to use and, therefore, does not require skilled
enumerators. Accuracy of the instrument is high on smooth dry land, and the
mechanical recording of the number of revolutions eliminates the risk of
mistakes in counting.
Drawbacks: The instrument is not ideal for use in number of areas, including
those with rough ground, ploughed land and irrigated and humid land. It cannot
be used for direct measurement of horizontal distances when the land is sloping,
and when the land surface is undulating, the wheel measures the wavy curve
and not the straight line.
3.3. FARMER ASSESSMENT OF CROP AREA
In this method, the farmers are enquired to estimate the area of their fields. The
enumerator and the farmer may visit all fields of the farmer and estimate the
surface area by visual inspection (David 1978). In Uganda, the agricultural
module of the population census of 2002 used farmer estimates to obtain area
estimates (Menyha 2008). David (1978) concluded from two studies conducted
in Philippines that farmers overestimated their area by just 6 to 8 percent, while
in a third study, it was found that farmers slightly underestimated their area.
Ajayi &Waibel (2000) observed that in the Côte d'Ivoire, coast farmers were
able to confidently estimate crop areas when their plot size was larger than the
local area unit(± 0.25 ha), but when plot size was smaller than the local area
unit, farmers greatly overestimated the plot size (125 percent error). Several
authors (David 1978; Ajayi &Waibel 2000; de Groote &Traoré 2005) observed
that the accuracy of farmer estimates reduces with increasing plot size, resulting
in underestimations.
De Groote and Traoré (2005) mentioned several problems with the use of
farmer area assessments. Farmers may not trust the enumerators out of concern
that they may be subjected to more taxes. The problem is compounded if a
farmer is illiterate and thus may not be able to provide accurate information.
David (1978) found that farmers were likely to round off figures to the nearest
quarter or third of an acre. Due to the multitude of possible problems, FAO
(1982) considers the accuracy of farmer surface area estimations to be
insufficient. To increase the accuracy of farmer area estimates, one or more
subsamples of the sample can be defined from which enumerators obtain both
farmer estimates and direct area measurements. A correction factor may be
31
defined on the basis of their correlation (David 1978). De Groote &Traoré
(2005) suggested that the enumerator should discuss the area estimates with the
farmer in the field, which would improve the accuracy of the estimates. This
method is adopted for acreage estimation under crops in such countries as
Egypt and Sudan.
Merits: This method is relatively less time-consuming and inexpensive.
Demerits: This method is highly subjective as it depends on farmers’
knowledge and, experience. The farmers are likely to misreport crop area over
fear of being burdened with increased taxes.
3.4. GLOBAL POSITIONING SYSTEM
GPS is a space-based satellite navigation system that provides location and time
information anywhere on earth. GPS hardware determines coordinates for x, y
and z axis, with x and y being the geographic coordinates that determine the
location and z being the coordinate that determines the elevation. Initially, GPS
was used to determine the location of a particular point, but with the
advancement in the technology, it is now capable of determining the elevation
and even the area covered. As a result, GPS has become a very important tool
for measuring the area under a crop with an added advantage of reduced time
and labor. There are, however, some reservations regarding the accuracy of the
instrument. The working of GPS, steps for using GPS for crop area
measurement, conditions in which GPS could be used for crop area
measurement, issues related to the use of GPS for area measurement and some
studies done in past for crop area estimation using GPS are discussed in this
section.
3.4.1. STEPS FOR USING THE GLOBAL POSITIONING SYSTEM
FOR CROP AREA MEASUREMENT
The following steps must be carefully observed when acquiring data using GPS
(Keita et al.2009):
The GPS device should be thoroughly tested before use;
The acquisition strategy should be designed in advance;
The material required to check data in the field, such as maps, should be
prepared or procured accurately and in advance;
The available data or maps should be uploaded into the receiver;
Identification of the fields to be measured;
Mark the borders of the identified fields accurately;
32
Proper care should be taken regarding data storage and reporting. The
data storage can be done either on the paper (each measurement is
annotated on the paper) or on the GPS directly;
The shape of the plot on the screen of the devise during the test and in
the operational data acquisition phase should be observed to avoid
errors;
A post-acquisition integrity check is advisable for acquisition of
redundant control points;
Post-acquisition control, which includes checking the authenticity of the
data, computing the perimeter/area ratio, visualizing the plot borders,
should be conducted and inserted into the virtual globe.
The enumerator holds the GPS device in his/her hand and walks the whole
perimeter of the field from a specific starting point at one corner of the field.
The points are tracked chronologically in the memory of the device to make
lines. Lines are considered to define an area if the first point is connected to the
last point. If the loop is not closed by the enumerator, then the program will
compute the closed area obtained by connecting the last point in the log to the
first point. This geometry is used by GPS to calculate the area of the polygon. If
the user walks in an overlapping path, an impossible surface will be defined,
resulting in a grossly erroneous calculation. The calculation uses signed
numbers so if the track is crossed over at some point creating an “8” like
picture, then the program will compute the difference in area between the two
circles. The data are stored in a track-log on the device, which can be used to
calculate the area of the field. Most GPS models allow for direct area
calculation.
During the time needed for measuring the area of a plot logging along the
perimeter, the GPS constellation can be considered as being relatively stable,
thus the positioning errors do affect the measurement. Area measurement error
is linked to the operator speed and to the acquisition rate of the GPS device.
Field area measurement errors can be limited if an appropriate combination of
operator speed and GPS acquisition rate is selected, for parcels up to 4 ha, the
“optimum” range of speeds for operators on foot is between 0.5 m/sand 2 m/s
(1.8-7.2 km/h) (Bogaert et al. 2005).
33
3.4.2. ISSUES RELATED TO THE USE OF THE GLOBAL
POSITIONING SYSTEM FOR AREA MEASUREMENT
Area measurements with GPS receivers are more rapid, efficient in terms of
time, feasible, digital and thus traceable and easy to incorporate into a database.
However, issues with the accuracy and precision of the results remain. Some of
them are mentioned below:
Accuracy of measurements with GPS: If the area is measured with a compass
and a meter is considered as the true area then the most GPS measurements are
very near to the true area, although, in general, GPS receivers tend to
underestimate the area of plots. According to some experiences, about 80
percent of GPS measurements have an absolute value of the relative error less
than or equal to 0.025.
(a) Factors affecting the accuracy of GPS: The accuracy of GPS
measurements is influenced by (i) the tree canopy cover (accuracy is
high with no tree canopy cover and lower with partial or dense tree
canopy cover), (ii) the weather conditions (accuracy is higher under
sunny conditions than under cloudy conditions), (iii) the plot size (the
larger the size of the plots, more accurate are the results).
(b) Battery storage problem: Securing ample power supply is one of the
major problems faced while using a GPS device for measurement. Some
GPS devices may be plugged into the car’s 12-volt power port. Some
GPS units use large capacity battery while others use an external box of
dry-cell batteries. Small GPS units typically save weight and space by
using two AAA cells, but they may last for only four or five hours on
one set of batteries. Larger handhelds use up to four AA cells (which
mean more weight), but run for as long as 12 hours per set.
(c) Speed: The time required for taking measurements with GPS is much
less as compared to other methods that involve making measurements,
such as a compass and tape method.
(d) Improvement in accuracy by repeating the measurements: The
accuracy of the measurements with GPS units does not improve from
the first to the third measurement. The GPS measurement is, in general,
3.3 times more rapid than other measurements and if two measurements
have to be performed, the GPS becomes 1.65 times more rapid.
Therefore, when using GPS, careful cost-benefit analysis has to be done,
34
considering also the cost of the GPS receiver, before deciding to replace
the compass and tape method with GPS.
3.4.3. RECOMMENDED CONDITIONS FOR USE OF GPS FOR CROP
AREA MEASUREMENT BASED ON STUDIES
The empirical field experience conducted by FAO and several other institutions
in various regions and under different conditions provides a scientific basis for
recommending the use of GPS for crop area measurement under specific
conditions. These studies also identify the conditions in which the use of GPS
may not lead to accurate estimation of crop areas. The main conclusions of
these studies were validated during an international expert meeting, which was
held in Addis Ababa in November 2008. These studies have collected
information on 207 plots. FAO and some other institutions conducted field
studies in order to investigate the use of GPS for crop area estimation under
different conditions and compared the results with traditional estimates of the
area (Carfagna & Keita, 2009). On the same plots, the area was measured with
the traditional method first and then with the various kinds of GPS receivers,
such as Garmin 12 xl (G12), Garmin 72 (G72), Garmin 60 (G60) Garmin Etrex
Ventura (GE) and Magellan Explorist 400 (M400), often repeating the
measurement three times. The study concluded that most GPS measurements
were found to be very near to the true area when the area measured with a
compass and a meter was considered as the true area. About 80 percent of the
measurements were found to have a relative error of less than or equal to 2.5
percent. A linear regression of the measures using a compass and a tape and the
measures made by GPS shows that the linear model explains a very high
percentage of the variability of the compass and tape measures (R2 = 0.9633),
with parameters significantly different from zero and the slope very near 1
(0.9600301).
The accuracy of GPS for crop area measurements was found to be higher for
larger plots, which suggested that for large and very large plots (from 10,200
square meters), the GPS measures are very similar to the compass and meter
ones; for medium-size plots (form 5,300 to 9,999 square meters), they are less
similar. Finally, for small and very small plots (less than 0.5 hectares) the two
distributions are quite different. This lower limit of a half of a hectare as the
measuring area using current GPS devices with acceptable accuracy was also
confirmed by the experiments.
The time needed for measurement with a compass and tape and GPS receivers
shows a high variability. For some small and medium plots, the time needed
when using a compass and tape is 17 times longer than when using GPS. The
35
distributions of the time needed for the first, the second and the third
measurements are not significantly different. The accuracy of the measurements
does not improve from the first to the third measurement. As the repetition
involves cost, these data do not suggest repeating the measurement. The studies
also revealed that some types of GPS perform better than others in different
conditions. Therefore, the selection of GPS should be done with great care.
Also, regarding operational use, battery life, robustness under difficult field
conditions and simplicity of use should be taken into account when selecting a
GPS type.
The Uganda Bureau of Statistics has been testing the accuracy of GPS estimates
for crop area (Schøning et al. 2005). During the 2003 Pilot Census of
Agriculture in Uganda, area estimates obtained using GPS were compared with
the area estimates obtained using the polygon method. Lesser time consumption
was found to be the main advantage of using GPS. GPS resulted in an overall
time saving of more than 300 percent. On average, GPS area estimates were 6-
12 percent lower than area estimates obtained from the polygon method.
Analyzing the results by plot size showed that GPS estimates were strongly
correlated (R2 = 0.90) with the polygon estimates for larger plots (greater than
0.5 hectare. For smaller plots (less than 0.5 hectare), the correlation was found
to be very poor (R2 = 0.12). However, recently under the WB/LSMS project, a
researcher in Burundi observed that even for plots as small as 0.25 acres (1000
m2), the accuracy of the GPS method is still reasonable as compared to the rope
and compass method.
The accuracy of any GPS receiver is around ± 15 m. Therefore, for small plots,
there may be large errors in the estimation of area. A GPS receiver might record
a plot measuring 30m × 33.3m (0.1 hectare) as a plot of 60m × 63.3m,
overestimating the area by 385 percent. The accuracy of a GPS receiver may be
enhanced by installing a second GPS receiver in a location with known
coordinates – a Differential Global Positioning System (D-GPS) (Fermont &
Benson 2011). The precision of GPS can also be improved by repeating the
measurements using D-GPS, or using the Satellite Based Augmentation System
(SBAS) differential corrections, such as the Wide Area Augmentation System
(WAAS), the European Geostationary Overlay Service (EGNOS) and the
Japanese Multifunctional Transport Satellite Augmentation System (MSAS). A
GPS receiver capable of SBAS differential correction can give position
accuracy on an average of three meters. The precision can also be improved by
Assisted Global Positioning System (A-GPS) which provides a reliable position
under poor signal conditions, such as under trees or indoors.
36
There may be erroneous readings in GPS measurements resulting from
interference of trees and projection problems in hilly areas (Schøning et al.
2005; Sempungu 2010). It should be noted, however, that the latter is a problem
for any method measuring crop area on steep slopes. This is related to the fact
that the measured crop area should not be the physical area measured on the
ground, but instead it is projection onto a horizontal plane (Muwanga-Zake
1985). The projection problem may become acute on slopes greater than 10
degrees. As the introduced error is 0.4 and 1.5 percent for slopes of 5 and 10
degrees, respectively, the projection problem may be ignored on slopes of less
than 10 degrees (Fermont &Benson 2011). Therefore, use of clinometers with a
compass and rope in hilly regions with steep slopes is recommended.
The Living Standard Measurement Study by World Bank, which was carried
out in 2013 and 2014 in Ethiopia, Nigeria and the Untied Republic of Tanzania
compared the crop area measured through compass and rope, GPS (Garmin E
Trex 30 was used in the United Republic of Tanzania and Ethiopia, while, in
Nigeria, the Garmin GPS Maps 62 and self-reporting by farmers (the compass
and rope method of measurement was assumed to be the standard) was used.
This study revealed that, on average, GPS measures were very accurate
estimates of plot size, including for very small plots and for reasonably small
samples. Thus, the GPS method of area measurement is recommended over
compass and rope and self-reporting by the farmer. In large-scale surveys, GPS
measures were often plagued by missing observations. The study, therefore,
recommended that GPS measurement (where feasible) should be complemented
by a farmer self-reported estimated area (for all plots) as the ladder involves
negligible fieldwork costs, and, more importantly, it can serve as a baseline for
imputation where objective measurements may be missing. Another important
finding of the study was that the farmers were able to report land area correctly
in non-standard/local units. In addition, the compass and rope method may be
repeated whenever the closing error is more than 3 percent.
37
4 Methods of crop yield
estimation Crop area, crop production and crop yield are recognized as three key variables
by World Bank (2010) in the Global Strategy to Improve Agricultural and Rural
Statistics that should be part of the minimum core data set that all countries
should be able to provide. Crop productivity or crop yield is one of the essential
indicators for agricultural development. Crop yield is normally expressed in
kilogram (kg) or metric ton (MT) per hectare (ha). The estimation of crop yield
involves both the estimation of the crop area and estimation of the quantity of
produce obtained from that area. In many circumstances, it may not be easy to
estimate crop yield as both are prone to error and bias and the measuring
process may be time-consuming.
There are several methods available for crop yield estimation with known
merits and drawbacks. The use of these methods varies across countries.
Among these, the whole plot harvest method may be treated as the gold
standard for crop yield estimation. The two paramount methods for estimating
crop yield are crop cut and farmers' estimate. In addition to them, there are
some other methods available for crop yield estimation. These methods along
with their critical analysis are described discussed below:
4.1. WHOLE PLOT HARVEST
The whole plot harvest method is employed during detailed farm surveys and in
demonstration plots (Norman et al.1995). Harvesting of the whole plot is done
in cases of on-farm trials. Before harvesting, plot boundaries need to be clearly
marked and then the harvest area should be calculated. The crops having a
defined maturity date, such as cereals or legumes, with a determinate growth
that can be harvested in a single operation. Legumes with an indeterminate
growth habit, such as common bean, cowpea and mung bean, or crops with
harvests spread over different seasons or year, such as cotton, banana or
cassava, require multiple harvests per plot. Each harvest is dried and weighed
38
separately. Individual harvest weights are summed up in order to obtain total
production and to calculate crop yield.
Whole plot harvest measures the harvested yield. This method is regarded as
the absolute standard for crop yield estimation, especially if done together with
the farmer (Casley & Kumar 1988).
Merits: The main advantage is that it is almost bias-free, as all sources of
upward bias reported for crop cuts can be eliminated when the whole field is
harvested. This method is suitable for small-scale investigations of a case-study
nature (Poate 1988). The complete harvesting generates more accurate data than
crop cuts because the bias from within-field variability, which commonly is 40–
60 percent of total yield variability, is eliminated. Wherever the whole field
average size is less than 0.5 hectare, complete harvesting takes a similar amount
of time as the crop cuts in two or three subplots per field (Casley &Kumar
1988).
Drawbacks: Murphy et al. (1991) pointed out that the area measurement for
the whole plot introduces a limited source of downward bias. This is especially
the case with irregular shaped plots when enumerators have to approximate
curved lines with straight lines for calculating the surface area. It has been
observed that enumerators tend to minimize the exclusion of planted areas and
forget to include non-planted areas. This may introduce an upward bias of up to
5 percent in the area estimation, which results into a limited underestimation of
the harvested yield. In addition, whole plot harvesting requires the enumerator
to be present at the time of harvest, which does not always happen. The main
drawback of the method is that it involves a large volume of work, making it
unsuitable for moderate-to-large sample sizes or multiple crop studies.
4.2. CROP CUT AND FARMERS’ESTIMATE METHODS
The crop cuts and farmers eye estimates are the two most commonly used
methods to estimate crop production. This section provides methodological
details and their merits and drawbacks and then presents the results of
comparative studies.
4.2.1. CROP CUT METHOD
The crop cut method was developed in the late 1940s in India for estimating
crop yield based on sampling of small subplots within cultivated fields by
pioneers in sampling and survey design, especially based on the work of P.C.
Mahalanobis of the Indian Statistical Institute and P.V. Sukhatme of the Indian
Council of Agricultural Research (ICAR). Within a decade, the crop-cut
method were adopted as the standard method recommended by FAO to estimate
39
crop production (FAO 1982; Murphy et al. 1991). Since then, this method has
been commonly regarded as the reliable and objective method for estimating
crop yield. It involves randomly locating, prior to the harvest, a small subplot
within each field. The shape of the subplot is usually considered as a
square/rectangle/triangle/circle. The subplot is harvested by the survey
enumerator or farmer himself. Then the crop is weighed after proper drying and
processing (figure 7). Crop yield is calculated as total production divided by
total harvested area of the crop cut plot or subplots. Detailed description of
steps involved in conducting crop cutting experiments is given in annex II.
Figure 7. Steps involved in Crop Cut Method
40
Spencer (1989) described the yield plot method in cases in which the
enumerator would take out a randomly chosen quadrant in a farm field before
harvest. When the farmer harvests his or her plot, he or she leaves the quadrant
unharvested, which is cut and measured by the enumerator soon afterward.
However, one randomly sampled subplot may not represent the variability in
crop performance within a field. For that reason, the concept of sampling of
multiple small subplots came under consideration. Fielding & Riley (1997)
suggested using two large quadrants (in the order of 50-75 m2 each). In a study
based on Botswana, Norman et al. (1995) suggested using a systematic
sampling scheme with multiple small quadrants. It was also suggested to use a
measuring stick instead of a quadrant to facilitate the logistics and for ease of
operation. In a detailed study in five African countries, Verma et al. (1988)
found that quadrant sampling gave more accurate and reliable results than the
row sampling, as subplot boundaries were clearer in the former method. Rozelle
(1991) suggested another multiple quadrant crop cut method known as the
“five-point” method, in which the enumerator harvests five “one square meter”
quadrants located in the corners and the center of a plot.
National statistical institutes in for example, Benin, India, Niger and Zimbabwe,
prefer to use the crop-cut method (MoS & PI 2008; Murphy et al. 1991). The
United States Department of Agriculture (USDA) uses crop cuts for yield
estimation of specific major crops in specific states. Uganda used crop cuts in
the annual crop yield surveys of 1967 and 1968 and in the agricultural census of
1990-1991.
The equipment/materials required for conducting the crop cutting experiments
are:
a) Measuring tape as per requirement (30m or 50m)
b) Weighing balance as per requirement (beam or spring balance)
c) Set of weights (1g, 2g, 5g, 10g, 20g, 50g, 100g, 200g, 500g, 1kg, 2kg
and 5kg)
d) String or rope (30m)
e) Four pegs
f) Hessian cloth: a coarsely woven fabric usually made from vegetable
fibers and jute. Known for its plain weaving and durable quality, it is
ecofriendly and used for the packaging of a variety of goods including
grains, sugar and pulses.
g) Cloth bags for keeping the produce for drying
h) Two strong waterproof bags (one for keeping crop cutting equipment
and the other for keeping, such things as schedules and papers)
41
i) Blank schedules, instruction manual, random number tables and other
related documents.
42
Figure 8. Equipment required for conducting the crop cutting experiments
Measuring tape Compass Rope
Beam balance with weights Beam balance pan with weights Pegs
Spring balance Set of Weights Hessian cloth
43
Merits: Since being endorsed by FAO in the 1950s, the crop cut method has
been commonly regarded as the most reliable and objective method for
estimating crop yield. A sufficient number of cuts in a sufficient number of
fields provides a valid estimate of average yield (Murphy et al. 1991). An
advantage of the crop-cut method is that the area of the cut is known and thus
an error cannot be introduced into the final yield computation
(Poate 1988).
Drawbacks: The crop cut method measures the biological yield, which does
not take into account post-harvest losses and therefore does not reflect the
economic yield that is of use to the farmer. Obtaining yield estimation through
crop cuts is both time-consuming and labor-intensive. To facilitate field work
and reduce costs and the time involved, a clustered sampling procedure is
usually applied when crop cuts are used for larger scale surveys. As crop
cutting experiments involve intensive field work requiring many field
investigators, the objective method for data collection is unaffordable, which
has prompted many countries and international agencies to consider the
farmer’s interview method to obtain relatively cheaper and quicker estimates of
average yield.
4.2.2. FARMERS’ESTIMATE METHOD
The most common alternative for the crop cut method is to use farmers'
estimates, which gives a measure of total produce and the economic yield.
Estimating crop production through farmer interviews involves asking farmers
what quantity they did harvest or what quantity they expect to harvest in order
come up with an estimate for an individual plot, field or farm. The first one is
commonly known as farmer recall, whereas the second one is referred to as
farmer prediction. As harvest quantities are farmer estimates, they are generally
expressed in local harvest units instead of kilograms or tons. To convert harvest
quantities to standard units, conversion factors are required.
4.2.2.1. FARMER RECALL
This method is the post-harvest estimation commonly made at the farmer’s
house or at the site where the harvest is stored for the enumerator to cross-check
the estimates with the available storage capacity (Casley &Kumar 1988).
Depending on rainfall distribution, recall periods may range from six months or
one season to three years or three-to-six seasons (Howard et al. 1995; Lekasi et
al. 2001; Erenstein et al. 2007). Smale et al. (2010) gave examples of longer-
term subjective recall, namely two-year average production data, instead of
asking farmers to estimate harvested quantities for individual growing seasons.
Developed countries, such as Sweden, are obtaining farmer recall data through
44
web-based surveys or telephone interviews (personal communication with G.
Ländell, Sweden Statistics, 2010).
To estimate the crop yield, the production data obtained from farmer recall
requires dividing the plot area from which the crop was harvested. This,
however, introduces an additional source of error. Fermont et al. (2009)
obtained a direct estimate of average crop yield to remove this error by asking
farmers to estimate the number of local harvest units they would have obtained
from a well-known unit of land, often the farm compound, if it had been planted
with a specific crop.
4.2.2.2. FARMER PREDICTION
This method is the pre-harvest estimation commonly obtained on a plot-by-plot
basis, in which the enumerator and the farmer are in visual contact with the
growing crop. The enumerator needs to be able to judge the validity of the
farmer’s response. The basis of the farmer’s predictions of expected yield is
their previous experiences, by comparing the current crop performance to
previous crop performances (David 1978). Singh (2003) suggested that yield
estimation should be made at the time of maximum crop growth. In the United
States, to obtain production forecasts, monthly telephonic interviews are
conducted with farmers (USDA 2009).
Merits: The use of farmer estimation does not require laborious measurements
and allows for a more efficient, random sampling design (Murphy et al. 1991;
Casley & Kumar 1988). In comparison to the crop cut method, the use of
farmer estimation is less costly and quicker to carry out. Consequently, farmers'
estimation with the same resources allow for a larger number of yield estimates
to be collected than crop cuts.
Drawbacks: Even though farmer recall and predictions were found to be
effective and inexpensive in several studies, they too had their own
shortcomings. These included (i) ignorance of in-kind payments, (ii) non-
standard harvest units, (iii) intentional over/underreporting, (iv) low accuracy
with longer recall periods, (v) historical average production factors, (vi) poor
quality responses in lengthy interviews, (vii) insufficient supervision, (viii)
illiteracy, especially in African countries (David 1978; Casley &Kumar 1988;
Poate 1988; Rozelle 1991;Howard et al. 1995; Kelly et al. 1995; Diskin 1997;
UBOS 2002; Ali et al. 2009; Fermont &Benson 2011). Several studies indicate
that the use of farmers’ estimates is affected by the bias in estimation.
45
Comparison of crop cut and farmers’ estimate with the gold standard
method
As the crop-cut method measures biological yield, the whole-plot-harvest
method measures harvested yield and the farmer recall method measures
economic yield, each method takes into account different amounts of harvest
losses. As a result, theoretically, the three yield estimates obtained for the same
plot can never have the same value. The estimated yield levels for all estimates
are completely free of sampling and non-sampling errors should be in the
following order: crop cuts > whole plot > farmer recall.
For a small and controlled survey in Nigeria to compare crop-cut estimates
obtained from 60 m2 subplots with farmer predictions and whole plot harvests
of millet and sorghum, Casley & Kumar (1988) reported an average bias of 14
percent for both crop cuts and farmer predictions. They also quoted a study on
rice in Bangladesh that showed that crop cuts had an average 20 percent upward
bias, compared with whole plot harvests, while a small study in Bangladesh
showed 15 percent bias in farmer recall data (Poate 1988). A small study in the
United States using precise procedures on soybeans showed that the bias in
yield estimated using crop cuts might be as low as 5 percent. In that study, the
crop cuts even underestimated yield in comparison to whole plot harvest in
some cases (Rogers and Murfield 1965).
For years, it was assumed that farmer estimates were too subjective and
unreliable to obtain reliable data on crop yield (Verma et al. 1988), whereas
crop cuts were assumed to be unbiased (Murphy et al. 1991). Thus, when
farmer estimates differed from crop-cutting measurements, it was automatically
assumed that the differences reflected “farmer error”. The idea that crop cut
measurements were not seriously affected by bias, such as consistent over- or
under- estimation, was based on early evidence from crop cut work in India.
However, in the late 1980s, evidence started to emerge that biases associated
with crop cuts were often substantial if not carried out properly or the
enumerators were not experienced. Especially in the case of small, irregularly
shaped fields with uneven plant density, biases were found to be large, which
was the situation of many smallholder farmers in Africa (Poate 1988; Murphy
et al. 1991).
In this context, the most significant study done so far is by Verma et al. (1988)
on maize crop in five African countries, namely, Benin, Central African
Republic, Kenya, Niger and Zimbabwe, in which crop cut and farmers’
interview methods were compared with the complete harvest from the selected
fields. The study revealed that (i) the farmers generally overestimated the
planted area, (ii) farmers' post-harvest estimates were close to actual production
46
and superior to the objective method of crop cutting, (iii) farmers' pre-harvest
estimates were also good for predicting production levels but had high variance
and (iv) the crop-cut method overestimated average yield by about 30 percent.
The study, however, had several weaknesses, namely (i) the enumerators who
interviewed the farmers also carried out crop-cut work and measured the total
harvest of the sample fields, (ii) the sample size in each country was small,
resulting in high within-country variability and (iii) the selected farmers were
fully aware and willing participants at each stage of the study and thus
independence of the method was lost.
In contrast to the above study, Rozelle (1991) reported that farmers in Malawi
had great difficulties estimating crop production after harvest. Diskin (1997)
pointed out that Verma et al. (1988) evaluated production estimates, not crop
yield estimates. Thus, their study only provided evidence of the merits of
farmers' estimation over crop cuts while estimating production not yield. To
convert production estimates to yield estimates, the production estimates are
divided by the area estimates. Diskin (1997) argued that the results of Verma et
al. (1988) only supported the use of farmers' interviews over crop cuts to
estimate crop yield in cases in which farmers’ estimates of area had a minimum
source of error.
Casley &Kumar (1988) compared the crop cuts with farmer estimates of yield
on smallholder maize fields for two consecutive years across six regions using
data from the Central Statistical Office in Zimbabwe. Crop-cut estimates were
found to be on average 86 percent (with a range of 32 to 100 percent among
regions) higher than the farmers' estimates. Supervision of the crop cuts was
much tighter in the second year. As a result of this, crop-cut estimates were, on
average, 37 percent (with a range of 27 to 78 percent between regions) higher
than farmers' estimates. Though, this shows that crop cuts likely overestimate
crop yield, it does not rule out a substantial margin of bias in farmers' estimates.
Thus, crop-cut estimates, if carried out by an experienced enumerator along
with proper training and supervision, may provide reasonably accurate
estimates of crop yield.
Minot (2008) conducted a study in Ethiopia, which was in line with the
Zimbabwe study. He reported that as per agricultural sample survey of the
Ethiopian Central Statistical Agency in 2008, an average cereal yield estimates
using crop cuts were 31 to 46 percent higher than the farmer yield estimates for
the same season as observed in a large household survey carried out by the
International Food Policy Research Institute.
47
A five-year study of Statistics Sweden showed first, that farmer recall did not
systematically underestimate cereal yield and, second, that farmer estimates did
not strongly deviate from the cereal yield observed with crop cuts (‒4.9 to +9.5
percent) at the country level (Hagblad 1998). The results of this study were in
contrast to those of African studies. However, it is possible that Swedish
farmers are better able to recall production because of the higher levels than
African farmers because of mechanization, commercialization, and
recordkeeping within Swedish farming systems.
The farmer predictions (as opposed to farmer recall) were compared with crop
cuts during other studies. In two studies in Asia, crop cuts were found to be
strongly correlated (R2 = 0.86) to farmer predictions of rice yield, but 25 and 37
percent higher (David 1978). This was in line with results from India, where
farmer predictions of wheat yield were also strongly correlated (R2 = 0.87) to
crop-cut yield data (Singh 2003).
In India, the Indian Agricultural Statistics Research Institute, New Delhi
conducted a study on wheat crop in Lucknow and Aligarh districts during 1988-
1989 and 1989-1990. The unpublished results of this study reveals close
agreement between farmers' post-harvest estimate of average yield, yield
obtained through crop cutting and the whole field harvest. Kathuria (1995)
conducted a study for FAO on maize crop in Zambia and concluded that the
farmers interview method needed to be further experimented as some farmers
tended to under/overestimate the average yield suited to their interests. Mathur
et al. (2003) compared the farmers’ eye estimation method with the objective
crop-cut method in a study on wheat production of Karnal district of Haryana
state in India. They showed that the farmers’ estimate was very close to actual
production values. Ahmad et al. (2004) did a comparative study of farmers eye
estimate and crop cut estimate for general crop estimation surveys and
concluded that there was no significant difference between farmers’ eye
estimate and the crop cut estimate provided that the farmers’ eye estimate was
taken one week prior to the harvest. Thus, to obtain a more efficient estimate,
one may combine crop cut estimates taken one week prior to the harvest with
farmers eye estimate as an auxiliary variable.
The crop-cut method is regarded as a reliable and objective method for
estimating crop yield. This method, however, may be accompanied with an
inherent upward bias because of measurement errors and the increased cost and
time involved. But these deficiencies can be largely overcome by appropriate
training and supervision. The use of optimum sample sizes and auxiliary data
available in the system may provide reliable estimates.
48
The choice of these methods is spread over various countries. For example,
national statistical institutes in Kenya, Rwanda and Sweden prefer to use farmer
recall data to obtain production estimates, while those in Benin, India, Niger
and Zimbabwe favor the crop-cut method. The United States Department of
Agriculture uses a combination of farmer recall for their agricultural census and
crop cuts for yield estimation of specific major crops in a few States. Several
European countries prefer to use more expensive crop cuts for potatoes, while
cheaper methodologies such as farmer recall, or expert assessment for other
crops.
4.3. OTHER METHODS OF CROP YIELD ESTIMATION
In this section, other methods used to estimate crop yield are described. These
include daily recording, sampling of harvest units, expert assessment, crop
cards, crop modeling, purchase records, allometric models and remote sensing.
4.3.1. DAILY RECORDING
This is an intensive method for estimating crop production at the smallholder
farmer’s level. In this method, first the enumerators visit each plot of a farm
household and record its surface area. Over a certain time period, such as a
cropping season or a year, the enumerators visit the farmers regularly (ideally
daily) to record the weight and state of harvest of any crop that has been
harvested since the previous visit. In order to determine factors for converting
to a standard state of harvest for each crop, the enumerator may take
subsamples of the harvested crops from time to time. This method was used
during Ugandan agricultural census of 1965.
This method estimates the economic yield of a crop. As it is frequently
recorded, this method is able to capture multiple harvesting of the same plot, a
common practice for crops with an extended harvest period, such as cassava,
banana or coffee, and for crops in which the ripening period is spread over a
period of time, such as green maize and indeterminate legumes. Under this
method, unrecorded “losses” attributed to eating, selling or other activities
causing post-harvest losses are reduced. Furthermore, as detailed area
measurement of each plot is taken at the start of the exercise, crop yield can be
calculated without an additional source of error.
When this method was used for the Uganda agricultural census, in some regions
of the country, the published crop yield estimates per region were based on a
very limited number of plots (a maximum of 235 plots, very often less than 100
plots, and in a few cases only 2 plots per crop per region. In the census report, it
was argued that the sampling error might be expected to be large, especially in
49
those estimates that were obtained on the basis of a very limited number of
plots (MAC 1967). In addition, non-sampling errors could also have been high
because of possible errors in weighing or failure to weigh the entire harvested
crop. The other reason for high non-sampling errors might be attributed to the
use of average national conversion factors to convert the recorded weights into
standard harvest conditions. Though the conversion factors that were used were
calculated from data collected during the survey, it was found that in order to
estimate conversion factors with a high degree of accuracy, more work was
required.
Merits: This method may generate very high quality data when enumerators do
their job properly and farmers do not harvest a specific crop from more than
one field per day.
Drawbacks: This is a very labor-intensive method and thus requires cluster
sampling (Muwanga-Zake 1985), which has a negative impact on overall
sampling error. The likelihood of measurement and recording errors increases
because of the daily weighing and recording operations. Other disadvantages
observed with this method was that enumerators often lack motivation to visit
each farmer every day and farmers sometimes mix harvests from various plots
in cases in which they harvest the same crop from several plots in one day
(Muwanga-Zake 1985).
4.3.2. SAMPLING OF HARVEST UNITS
In this method, instead of harvesting and weighing the whole field, the
enumerator may wait for the farmer to harvest his or her field. An attempt is
made to estimate the number of units, such as sacks, baskets and bundles that
has been harvested by the farmer. The enumerator then randomly selects a
number of harvest units and weighs them to obtain an average unit weight.
Sampling of harvest units is generally done just before storage, which includes
a measurement of the moisture content of the harvested product (Casley
&Kumar 1988).
The method of sampling of harvest units measures either harvested yield or
economic yield, depending on the time between harvesting and sampling (that
is, the amount of post-harvest losses). To estimate crop production for a specific
plot using the sampling of the harvest units method, Casley & Kumar (1988)
suggested that the harvest be collected in identifiable and complete units and
reviewed before being stored in a granary or otherwise disposed. The units
should not be too variable so that average unit weight could be estimated
without too much error. Crops should be harvested at once and the enumerator
should make the estimation shortly after harvesting. In addition, the harvested
50
units should originate from one specific plot. This is especially a concern if a
household has multiple plots with the same crop or one field with the crop of
interest partially intercropped with a second crop. Poate & Casley (1985) found
this method more appropriate for estimating crop production at the farm level
than to estimate crop yield at the individual plot level as the above conditions
are usually not met. Rozelle (1991) pointed out that, in cases in which the
enumerator was unable to visit the household directly after harvesting, this
problem could be overcome by including questions to estimate the amounts of
the harvest that have already been used.
Merits: The technique is straightforward and can be used on larger samples as
compared to the crop-cut and whole-plot harvesting methods. Unlike farmer
estimates, it does not matter if the harvest units are particular to each individual
farmer, as the enumerator either weighs the complete harvest or weighs a
random, unbiased, selection of harvest units of each farmer (Poate 1988).
Drawbacks: When the harvest is stored in one or several large granaries or
stores, the enumerator needs to use analytical skills to accurately estimate total
production (Rozelle 1991). This method is considered unsuitable for crops with
an extended harvest period and multiple pickings, such as root crops, banana,
cotton and similar crops.
4.3.3. EXPERT ASSESSMENT
Experts that have extensive experience with crops, such as extension staff, field
technicians or subject matter experts, can estimate crop yield by either visually
assessing the field or by estimating yield on the basis of a combination of tools.
This technique gives an estimation of biological yield. Experts are often able to
estimate crop production or yield by visually assessing the condition (for
example, color, plant vigor and plant density) of the crop in the field. This is
known as eye assessment. In the 1990s, several European countries, including
Belgium, Ireland, Germany and the Netherlands, used eye assessment to
estimate crop yield for their annual agricultural statistics (Bradbury 1994). In
Australia and the United States, eye assessment has been upgraded through a
combination of visual assessment, field measurements and empirical formulas
to a so-called expert assessment method.
For cereals and grain legumes, the yield in t/ha is estimated by multiplying the
average number of grains per head by the average number of heads per 5m row
and dividing this by a constant K that depends on the row spacing and grain
weight are carried out in at least 10 representative sites within a field (DPI
2010). For cotton, extension staff counts the number of cotton bolls that are
open or expected to open by harvest in 10 representative one row-feet sections
51
in the field. To determine average boll weight, the bolls on three plants are
picked and weighed in each section. The lint yield is estimated assuming a
certain picker efficiency and gin turnout and knowing the row spacing
(Goodman and Monks 2003). The expert assessment may become so detailed
that the difference between this method and that of crop cuts on the basis of row
segments may become blurred, though expert assessments will never involve
harvesting the whole row segment.
Few studies have compared expert assessments with other yield or production
estimation methods and their results are contradictory. A poor correlation of
rice yield that were eye estimated by experts and actual crop yield was observed
by David (1978);he concluded that eye estimation of yield should not be used.
However, In Zimbabwe, Casley and Kumar (1988) observed that expert
assessment was closely related (<10 percent difference) to farmer estimates.
Merits: Two advantages of the expert assessment method are that it can be
applied on a relatively large scale as compared to the crop-cut and farmer
estimation methods and it does not require area estimation and eliminates a
source of potential bias. Another important advantage is that one team of
experts can be used throughout a study, which results in a similar bias for all
yield estimation (Rozelle 1991).
Drawbacks: Eye estimations of crop yield require not only practical but also
technical familiarity with the yield potential of different varieties of a crop and
their relative performance in different environments (David 1978). The
accuracy of the yield assessment, therefore, strongly depends on the level of
expertise of the expert. When assessments are made by extension officers, yield
estimation may be upward biased, especially if the assessments are made in
their own work area and the information collected thus pertains to the quality of
their own work (Casley and Kumar 1988). In contrast, Bradbury (1996b)
reported that yield estimates by expert judgment in Europe were generally
considered to be biased downward. Considering that a national survey or an
agricultural census requires yield estimates of a large range of crops, it is
difficult to find experts that have the required practical and technical expertise
to provide accurate estimation across all crops.
4.3.4. CROP CARDS
The crop card method is a refined version of the farmer recall method. It also
entails estimating the economic yield. The method has evolved to obtain more
reliable yield estimates of crops with an extended period of harvest, such as
cassava, banana and sweet potato, because farmers may have problems in
remembering the amounts they harvested over time for one or several plots.
52
Under this method, each farmer participating in the survey is given a set of crop
cards by a crop card monitor and receives training on how to use the cards. The
cards are used to record the quantity the farmer harvested in local harvesting
units after each harvest operation. The card crop monitor is expected to visit
each farmer on a regular basis to monitor the farmers' recordings and to make
corrections if the farmer has any problem. After a specified period, the crop
card monitor collects all the cards for processing.
This method was tested in Uganda during the Uganda National Household
Survey of 2005-2006 and was compared with farmer recall estimates.
Using the data collected for the Survey, Carletto et al. (2010) showed that crop
card production estimates were 40 to 60 percent lower than the farmer recall
production estimates for both crops with an extended harvest time (cassava and
banana) and for other crops (maize and beans).This was in line with the
findings of Sempungu (2010), who, using the same data set, found that cassava
and sweet potato yield estimates from the crop card method were 30 and 46
percent, respectively, lower than those obtained from farmer recall. The above
studies suggested, first, that farmers were either seriously overestimating crop
production during the recall exercise or underestimating crop production with
the crop card method and, second, the upward or downward bias that resulted
did not seem to depend on the type of crop. This is contradictory to the
assumption that farmers have difficulties in accurately recalling multiple
harvests of crops over an extended harvest period.
Merits: This method provides more reliable yield estimates of crops with an
extended period of harvest than the farmer recall method, as farmers may have
problems in remembering the amounts they harvested over time for one or
several plots.
Drawbacks: There are several problems with this method. Among them are
irregular monitoring by enumerators, illiterate farmers who were not able to fill
in the crop cards and, some recordings often include crop purchases and a very
large range of observed harvest units (Ssekiboobo 2007).
4.3.5. CROP MODELING
This method is widely used to estimate average biological yield of smallholder
farmers. Crop models vary widely in terms of complexity. The simplest sets of
models are of an empirical-statistical nature, whereas the most complex models
are based on crop physiology. The former is intended to find the best
correlation between crop yield and environmental factors, such as weather
parameters (temperature, humidity, rainfall) from long-term data sets. Using the
established relations, the model attempts to predict crop yield at a regional or
53
national level on the basis of actual environmental observations, whereas crop
growth models estimate crop yield as a function of physiological processes and
environmental conditions. They range from relatively simple models taking into
account only basic crop physiology processes, such as the Penman-Monteith
models, which are based on estimation of actual evapo-transpiration, to
extremely complex models that estimate daily gains in biomass production by
taking into account all known interactions between the environment and
physiological processes (Sawasawa 2003). The crop modeling approach is used
in India for multiple season crop forecasting using weather parameters as well
as parameters, such as crop area and price in the previous years under the
Forecasting Agricultural Output using Space, Agro-meteorology and Land-
based Observations (FASAL) project (Parihar & Oza 2006).
Merits: Crop models can be used to predict crop yield in specific conditions or
a range of conditions and are an extremely useful tool in research studies
exploring the impact of specific factors on average crop yield.
Drawbacks: Crop models cannot be used to predict crop yield for individual
farmer fields, as this requires far too much input data.
4.3.6. OTHER METHODS
In addition to those mentioned above, other methods for crop yield estimation
are described in this section. They include:
Purchase records from agro-industries
Generally in the case of cash crops, such as coffee, cotton, tea, cocoa and
sugarcane, purchase records for individual farmers can be obtained from agro-
industries and linked to farmers involved in the agricultural survey. Purchase
records can be a valuable source of production estimates (economic yield) at
regional or national levels. If the produce is sold to the
agro-industries and the records can be linked to the individual farmers in the
survey, production estimates at the farm level can be obtained. These may be
converted to crop yield if the total crop area is known. This works well in
developed countries. For example, Sweden and Norway obtain records on sugar
beet production from the agro-industry and the national grain administrator,
respectively (Bradbury 1996a). In Uganda, this may work for cotton, though
linking records from the cotton ginneries to individual farmers in the
agricultural survey may not be straightforward. Data on total cotton production
from each ginnery and aggregated data on cotton area in the same region can be
used to obtain a proxy estimation of cotton yield in a region. This may be more
difficult for coffee, as this crop is sold in several batches to one or more buyers,
and some of it is consumed locally.
54
Allometric Models
Allometric models are mathematical models based on plant morphological
attributes and crop yield. When these relationships are sufficiently accurate
(R2>0.75), then measurements of several morphological characteristics of a
selected number of plants can be used to predict biological yield in a field.
These models need to be based on variables that can be quantified easily using
rapid, inexpensive and nondestructive methods of data collection. Tittonell et
al. (2005) used plant height and ear length to predict maize yield in Western
Kenya. Wairegi et al. (2009) found that with a multivariate model using girth of
the pseudostem at the base and at 1 m, the number of hands and the number of
fingers gave a robust prediction of bunch weight for bananas in Uganda. Both
models were valid for a range of cultivars and soil fertility levels, whereas the
banana model was also valid for a range of agro-ecologies and not specific to
the development stage. This indicates that such models can be used in a wide
range of conditions. Labor demands for data collection for use in allometric
models are likely to be somewhat lower than for crop cuts, but enumerators
need additional training and an adapted datasheet for data collection. These
models are mainly applicable for fruit crops.
Remote sensing
Crop yield is the result of environmental factors, such as soil, weather, pest and
disease outbreaks, and farmer management. The total effect of these factors
translates into the production of green biomass and finally yield. Green plants
have a unique spectral reflectance or spectral signature. The proportion of
radiation reflected in different parts of the spectrum depends on the state,
structure, and composition of the plant. This information is captured in satellite
images as spectral data (that is, spectral reflection in various bands), which can
be used to construct several vegetation indices such as the Normalized
Difference Vegetation Index (NDVI) and the ratio vegetation index. High
correlations are found between NDVI and green biomass in studies done at the
field level (Groten 1993). Ground truthing in the form of field visits to
determine crop types and actual yield estimation in selected fields (pixels) that
cover the full range of observed NDVI values is required in order to correlate
NDVI values to crop types and crop yield.
In many countries, including China, India and the United States, using remote
sensing to estimate biological crop yield is being explored and is likely to
become the keystone of agricultural statistics in the future (Zhao et al. 2007).
However, considerable research is still needed before remote sensing can be
widely applied to estimate crop yield. In India, for example, vegetation indices
from satellite images showed only a moderate correlation (R2 between 0.45 and
55
0.54) with crop cut data (Singh 2003). One important shortcoming for the use
of satellite images to estimate crop yield of smallholder farmers is the spatial
resolution of the sensor, which is not sufficient to capture the variability of
crops and crop performance in smallholder fields that often are less than 0.1
hectare and may be intercropped as well. A detailed field level study by
Sawasawa (2003) on rice in India highlighted this problem by showing that,
even with high resolution images, only 52 percent of observed yield variability
was captured. In developing countries, the problems of cloud coverage and the
need for expensive ground truthing, specialist knowledge and expensive image
processing software, limits the current usefulness of remote sensing (Reynolds
et al. 2000).
Das and Singh (2013) used the multiple frame approach to estimate wheat yield
for the state of Haryana in India. For this purpose, information was collected
from Wide Field Sensor (WiFS) and Linear Imaging Self Scanner-III (LISS-III)
data from the Indian Remote Sensing-1D (IRS-1D) satellite and crop-cutting
experiment data collected by probability sampling design from a list frame of
villages. Multiple-frame estimators were found to be more precise in
comparison to single-frame estimators.
56
5 Crop acreage and yield
estimation under mixed
cropping Accurate estimation of crop yield is a difficult task, which becomes even more
challenging with the involvement of diverse farming systems, including a wide
range of crops and cropping patterns, such as mixed cropping, intercropping,
continuous cropping and staggered harvesting, for crops with an extended
harvest period, such as banana, cassava and coffee, and heterogeneous crop
performance in the field. An excellent review on estimation of crop area and
yield under mixed cropping or intercropping is available in Fermont & Benson
(2011).Recently, farmers have diversified into new farming systems that are
poorly enumerated in most of the national surveys. They tend to employ
intercropping or relay cropping in their fields in order to (i) evenly spread the
weather/calamities associated risks, and (ii) increase the production/total output
of individual fields as compared to pure stands. In the 1970s, researchers
identified eight different crops grown in mixtures in the middle belt of Nigeria
(Norman et al. 1982). Intercropping is the dominant agricultural practice in
many countries. As an example, intercropped fields in the IFPRI/INRAN
(National Agricultural Research Institute of Niger) data set of Niger have six
crops per field. Figures from the Uganda National Census of Agriculture and
Livestock (1990-1991) indicated that 80 to 90 percent of the area was under
mixed stands. A more recent survey by Henrietet al. (1997) showed that mixed
cropping such as millet/cowpea, sorghum/cowpea, sorghum/groundnut
intercropping was the predominant system in the Sudan Savannah of Nigeria.
The Tanzania Agriculture Sample Census, 2007/08 (NSCASHA 2012)
indicated that about 23% of national area comes under temporary mixed crops,
permanent mixed crops and permanent/annual mixed crops. Intercropping
became more essential for such crops as maize, beans, millet groundnuts and
cassava. Much of the early work focused on the motivations for planting mixed
57
rather than single-cropped fields, owing to risk aversion, labor constraints or
higher profits (Norman 1973a; Abalu & D'Silva 1979; Just 1981; Just &
Candler 1985). The final impact of intercropping on crop yield is the result of
complex interactions of many factors, including, among others, relative time of
planting, plant density, rainfall, soil fertility and crop management. The overall
effect of intercropping on the production and yield of individual crops is mostly
negative, though, in some cases intercropping may actually increase crop yield.
Hopkins & Berry (1994) discussed potential underestimation in national
production and yield by not fully enumerating mixed cropping fields which
could be very high. They reported that cereal (millet or maize or sorghum) was
the principal crop for 64 percent and pulse (peanuts or bambara nuts) for 36
percent of the intercropped fields. Hence, if only the principal crop was
enumerated on an intercropped field, the value of output captured represented
only 74 percent of the total value produced per hectare and only 72 percent of
the total value produced per labor day. Kelly et al. (1995) recommended that
only the two principal crops in an intercropped field be accounted for. Thus,
would be useful to include a minimum percentage (say, 20 percent of plot area
occupied by a crop) before a crop is counted as an intercrop.
The problem of correct recording of area under mixed crops becomes difficult.
This is because cultivators plant a large number of crops with considerable
variation in the proportion of seed of the component crops. The crops in
mixture are sown either row-wise, separately or mixed altogether. It has been
observed that even the row-ratio may vary. Kathuria (1998) highlighted the
problems of area and yield estimation under mixed and continuous cropping.
He pointed out that mixed cropping was a subsistence need and a widely used
practice by small farmers in many African countries. In the case of a mixed
cropping situation, several crops are planted at the same time in the same field
with the seeds either mixed or planted in rows in some fixed ratios. Harvesting
of these mixed crops may not always be at the same time and hence
enumerators are required to make repeated visits to record yield data.
Cassava is a staple food crop grown in many African countries. It is not
completely harvested at any particular time of the year, but it may be taken out
from the ground as and when required or used as a "reserve crop" in
emergencies. In some countries, it is not feasible to identify the area planted
with cassava at any moment as some may have just been harvested in a
continuous cropping sequence.
Kathuria (1998) concluded that preparation of a sampling frame, sample
selection and measurement of area are problems that need to be addressed in
these situations. Each of these situations requires an approach for conducting an
58
average yield estimation. When and how often the data on yield should be
collected in cases of continuous cropping are important points for
consideration.
Four strategies have been identified for estimating crop area, production and
yield in farming systems that have a significant proportion of crops produced
under intercropping (Kelly et al. 1995). Fermont &Benson (2011) provided an
excellent review on these four strategies with their advantages and
disadvantages.
Strategy 1 - Ignore intercropping
For the first strategy, intercropped plots are completely ignored. Crop areas are
recorded for sole cropped plots only, resulting in the estimated total crop area
being an underrepresentation of the actual area and the average crop yield being
an overestimation of actual crop yield. The estimated total production for crop
A is given as
Total production of crop A = (ΣArea crop Apure)× Avg. yield crop Apure.
Strategy 2 - Only record main crop
Under second strategy, though the intercropped plots are not ignored, but only
the main or predominant crop is recorded and the minor crop is ignored.
Estimates of crop area and crop yield are presented as if they are obtained in
sole cropped plots, though in reality they are obtained from a mixture of pure
and mixed stands. Total area for crop A is estimated as the sum of the total pure
area of crop A and the total area in which crop A is the main crop. As areas
with crop A as a minor crop are ignored, the estimated crop area is still an
underrepresentation of the actual area, though the underrepresentation is
significantly less than as in case of strategy 1. Average yield is determined from
a random selection of fields that have crop A either as a pure stand or as the
main intercrop. Total production for crop A is estimated as
Total production of crop A = (ΣArea crop Apure / main crop) × Avg. yield crop Apure
/ main crop.
Strategy 3 - Use whole plot as a denominator for each crop in the mixture
The third strategy uses the entire plot size as a denominator for each crop in a
mixture during both area and yield estimations. The crop mixture is indicated
for each area and yield estimation. The total area for crop A consists of the total
area for crop A as a pure stand plus the total area for crop A in all its recorded
mixtures. If crop A is a minor crop in prevalent mixtures, the total area for crop
A will be overestimated. The average yield for crop A is determined separately
for crop A as a pure stand and for each of its recorded mixtures.
59
Capturing total production for crop A involves inclusion of the area and average
yield of crop A for all recorded mixtures. According to Kelly et al. (1995), the
included mixtures should be limited to the two most important mixtures for
crop A within a region in order to capture most of the production value,
simplifying data recording and reducing possible recording errors.
Alternatively, a threshold (for example, area of mixture × 10 per cent of total
area of crop A) may be used to decide whether to include a certain mixture in
the estimation. Total production for crop A is then estimated as
Total production of crop A = (ΣArea crop Apure) × Avg. yield crop Apure +
(ΣArea crop Amix_1) × Avg. yield crop Amix_1+
(ΣArea crop Amix_2) × Avg. yield crop Amix_2
where mix_1 and mix_2 represent the most common crop mixture for crop A.
These may be mixtures in which crop A is the main crop or the minor crop. In
4situations in which the excluded mixtures represent relatively important areas,
their exclusion will result in the underreporting of the total production of crop
A.
Strategy 4 - Allocate part of plot size to each crop in the mixture
For the fourth strategy, the plot size is proportionally divided between the crops
planted in the mixture during both area and yield estimations in order to adjust
the observed area and yield estimations to pure stand estimation. The division
of the area between various crops can be done in three different ways:
Strategy 4a: visual estimation of the proportion occupied by each crop;
Strategy 4b: examining the seeding rates or measurements of crop density;
Strategy 4c: using fixed area ratios for each intercrop combination.
Total area for crop A is estimated as the sum of the adjusted crop areas,
whereas average adjusted yield is determined from a random selection of plots
that have crop A either as a pure stand or as a major or minor intercrop. Total
production for crop A is then estimated as
Total production crop A = (ΣArea crop Aadjusted to pure stand) ×Avg. yield crop
Aadjusted to pure stand
According to Kelly et al. (1995), strategies 3 and 4 are the most commonly used
around the world. Mortensson et al. (2004) emphasizes that, FAO and some
European countries use strategy 3 whereas all European countries reporting to
Eurostat use strategy 4a. Kelly et al. (1995) reported that the strategy 4b is used
in Rwanda. Strategy 4c is used in many states in India (MoS&PI 2008). For
strategy 3, the entire plot size is used as the denominator, which allows
60
comparison to be made between yield of sole crop versus intercrop. The main
disadvantage of this strategy is area reported under intercropped fields is double
counted. Thus, it does not allow for aggregating crop area at a higher level.
Strategy 4 proportionally allocates area to each intercrop as it adjusts all area
and yield figures to pure stand data. This facilitates a comparison across regions
or countries and removes the risk of double counting areas. Under strategy 4a,
estimating area proportions may be a questionable and difficult exercise,
especially if the crops are planted at random or more than two crops are present
in the plot. In strategy 4c, use of a fixed area ratio simplifies data collection by
the enumerators. Although it does not result in correct estimations of crop yield
at the individual plot level, it may generate satisfactory results at higher
aggregate levels (MoS&PI 2008). Fermont & Benson (2011) has suggested that
in future agricultural censuses or surveys, complex intercropping scenarios
should be taken into account.
Strategy 5 – Estimation of crop area based on imputed area
The estimation of the crop area through the two approaches yields completely
different statistical data pertaining to two different concepts of area. The first
denoted by "allocated area" is that fraction of the physical field area in which
the particular crop is cultivated. The sum of the allocated areas of the different
crops in the mixture should be equal to the total physical area of the field. The
second denoted by "imputed area" is the area which would have been occupied
by the crop had it been cultivated in pure stand. In general, the sum of the
imputed areas is not equal to the physical area of the field. The ratio between
the imputed area and the physical area can be considered as an indicator of the
intensity of cultivation of the land.
Under this strategy, as discussed in FAO (1982), the concept of imputed area is
utilized. The imputed area is described as the area occupied by the crop when it
is in pure stand. The sum of the imputed area is not equal to the physical area of
the field whereas the previously mentioned methods are based on the concepts
of allocated area. Allocated area is the fraction of physical field or area where
the particular crop is cultivated. The sum of allocated areas of different crops in
the mixture is equal to the physical area of the field. To calculate the imputed
area under the condition of mixed cropping, the following formulas are used.
61
Let, A be the physical area of the field
i is the subscript denoting the crop in the mixture.
ci is the numerical value of the characteristic for crop i under the conditions of
mixed cropping.
Ci is the value of the same characteristic of crop i under the conditions of pure
stand.
Then the imputed area Ai of crop i is given as
ii
i
cA A
C
and the allocated area of crop i is given as
i
i ii
i i
i
c
C AA A A .
c A
C
Imputation of crop areas in mixed cropping can be based on different criteria.
However, these criteria should depend on those characteristics of the crops are
highly correlated with either the area or production. The main characteristics
that can be used for the imputation of areas are:
Amount of seeds;
Density of the plants, such as mound or hills;
Volume of production; commercial value of the produce.
The choice of the proper characteristic depends on its relevancy to the
objectives of the survey and also on the availability of the data.
The different characteristics to be used in the imputation or allocation of areas
to different crops are not always readily available. The holder usually knows
the quantity of seeds he has utilized (in some local unit of measurement) but is
not always aware of the amount to be sown in the case of pure stand cultivation.
The average amount of seeds in the case of pure stand cultivation could be
collected from the farmers, theoretically determined or decided upon a
posteriori. Information on crop density in mixed cropping can be obtained
through the use of density plots or by counting the number of plants within the
crop-cutting plot.
62
The theoretical average density for the crop in pure stand can be derived from
averages
calculated for regions, districts and provinces, among others, depending on
available information. Production under conditions of mixed cropping and also
in pure stand can only be obtained after the crop has been harvested and the
production is measured. The commercial value of the produce requires a study
of crop prices to supplement the information on the volume of the production.
When the imputation of crop areas is based on the criteria of the amount of the
seeds sown or the volume of the production obtained during the totality of the
time reference period (agricultural year), in cases in which some of the crops in
the mixture are harvested before other crops are planted and thus added to the
mixture (a combination of mixed and successive cropping), is automatically
covered. In such a system of estimation of crop areas, the problem of successive
cropping reduces to a special case of mixed cropping.
In the presentation and/or tabulation of the results on crop areas under
conditions of mixed cropping, it would be very useful to present separately the
following four types of areas for each particular crop:
(i) Total area of the crop in pure stand;
(ii) Total area of the crop mixed with others;
(iii) Total imputed area of the crop;
(iv) Total allocated area of the crop.
This would permit different types of aggregation, as follows:
(i) + (ii) is the total physical area on which the crop is cultivated;
(i) + (iii) is the total area which could be used for the calculation of the crop
production (multiplying it by the average yield in pure stand)
(i) + (iv) is the total land-use area of the crop.
Method of recording area under mixed crops in India
The practice of sowing intercrops in the same field is quite common in almost
every part of India. This practice of mixed cropping provides protection to
cultivators against weather uncertainties. Meanwhile, the method of sowing
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intercrop is not uniform across the country. The crop mixtures are sown either
row-wise, separately or mixed altogether. The proportionate net area of each
component crop from all crop mixtures involving it are obtained and added to
the area sown singly (pure) with it to give its net area which is then published.
The allocation of gross area of a crop-mixture to its different component crops
is done either at the source, for example at the field level, by the primary
worker during the crop inspection (Girdawari or partal) and the net area of
each component crop is recorded separately in the crop statement (Jinswar), or
the primary worker records the whole area of crop-mixture, treating it as a
single crop and the total area of the mixture is separated to the component crops
at the district level.
The assignment of net area to different component crops at the field level is
made in proportion to the number of their rows, if they are sown in separate
lines. In cases in which the crops in the mixture are sown after thoroughly
mixing the seeds, this allocation is done in proportion to the actual amount of
seeds sown or seed-rates adjusted for mixed sowing or by eye estimation of the
relative stands of component crops. The components occupying a negligible
area or area below certain specified minimum are ignored and their area is
allocated to the chief component along or proportionately to all component
crops of a mixture.
The apportionment of net areas of component crops of a mixture at the district-
level is done on the basis of a fixed ratio, which is supposed to represent the
average conditions with regard to one or more of the above-mentioned factors
for the fields of the mixture in the district.
The procedure followed in the allocation of net areas of component crops of
mixture allocations may be grouped under the following three categories:
1. Allocation is done entirely at the field level;
2. Certain major crop mixtures are recognized as single crops and
allocation of net area of their components is done not at the field level
but at the district level, while in the case of unrecognized mixtures, the
allocation is done at the field level;
3. Allocation is done entirely at the district level on the basis of fixed
ratios.
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6 Crop acreage and yield
estimation under continuous
cropping As per recommendations of FAO (1982), the estimation of crop areas under
continuous cropping should be carried out in multi round surveys depending on
the number of different configurations of the crops in the fields. In a system of
regular periodic reporting (monthly, bimonthly or quarterly), the number of
rounds for estimation of crop areas under a system of continuous cropping
could be the same.
In Rwanda, continuous or sequential cropping (one crop following another
during the same year on a given parcel of land) and intercropping (several crops
planted at the same time on the parcel of land) are common. Although the
potential for underestimating output is high because mixed cropping and
sequential cropping are common, surveys conducted to collect national
production data enumerate all crops and use measures of relative crop densities
to determine how much land is occupied by each crop. Norman et al. (1995)
states “although progress has been made in developing methods for assessing
crop densities and yields for both systematic and random crop mixtures,
researchers and statistical services need to consider the research question at
hand, as well as the cost and feasibility of getting accurate density estimates,
before adopting these procedures”.
Due to even distribution of rainfall, farmers may plant and harvest crops
throughout the year. This may extremely complicate the estimation of crop area
and production. In order to capture the continuous cropping patterns during the
1965 agricultural census in Uganda, the crop area of each farmer was recorded
three times during the census year (MAC 1965). However, it was noted that the
holders changed the constituency of crops within a plot and plot boundaries so
frequently that it was difficult to link the records of the various visits.
65
Subsequent Ugandan surveys, therefore, used a single visit to measure crop
areas.
Crops that have an extended harvest period and multiple pickings, such as
cassava, sweet potato, banana, cotton, and coffee, pose a problem in crop yield
estimation studies. Crop-cutting and whole plot harvesting methods do not take
into account the extended harvest period of the crop under study. They are
carried out at one given point of time when the crop is assumed to have
matured. If harvesting is always done at the same time after planting, the
resulting crop yield can be compared across regions and years. When crop
yields are obtained through the whole plot harvesting method, it may be
regarded as the most objective yield measurement that can be obtained for this
type of crop. Notably, crop cuts and whole plot harvesting cannot be used to
estimate banana yield due to its uneven ripening throughout the year. Wairegi et
al. (2009) developed a method to estimate banana bunch weight on individual
plants in East Africa using non-destructive field observations.
66
7 Small area estimation
techniques Surveys are generally planned to produce estimates for population,
subpopulation or larger domains, such as national or subnational/province/state
levels. Sample sizes are fixed in such a way that direct survey estimator
(defined using domain-specific survey data only) provides reliable estimates
with a predetermined level of precision for planned domains in those surveys.
Policy planners, researchers, government and public agencies often require
estimates for many unplanned domains. Such unplanned domains can be a
small geographic area or a demographic group or a cross classification of both.
The sample sizes for such unplanned domains in the existing survey data may
be very small or at times even zero. In the survey literature, a domain is
regarded as small if the domain-specific sample is not large enough to support a
direct survey estimator of adequate precision. These small domains are also
called small areas, so called because the sample size in the area or domain from
the survey is small. If the sample size is small, domain-specific direct
estimators can provide an unacceptably large coefficient of variation. Therefore,
it becomes necessary to employ indirect small area estimators that make use of
sample data from related areas or domains through linking models, and thus
increase the effective sample size in the small areas. Such estimators can give a
significantly smaller coefficient of variation than direct estimators provided the
linking models are valid. These estimators are often referred to as the indirect
estimators as they use values of survey variables (and auxiliary variables) from
other small areas or times, and possibly from both. An underlying theory that
resolves the problem of small sample sizes is often referred to as small area
estimation techniques in the survey literatures, see Rao (2003).For example, the
traditional domain estimation approach provides reliable estimates of crop area
and crop yield in a mixed cropping scenario. However, for some of the crop
mixtures, the sample sizes may turn out to be extremely small, which may
affect the reliability of the estimates. This small sample size problem can be
67
addressed through the small area estimation approach by combining the survey
data and the already available secondary data. The small area estimation
techniques are generally based on model-based methods (Pfeffermann 2002;
Rao 2003). The idea is to use statistical models to link the variable of interest
with auxiliary information, namely a census and administrative data, for the
small areas to define model-based estimators for these areas. The traditional
indirect estimation techniques based on implicit linking models are a synthetic
estimation.
7.1. SYNTHETIC ESTIMATION
When producing the synthetic estimates for small areas, availability of direct
estimates for a set of larger domains of the population is assumed. Appropriate
weights or proportions are then applied to these large population domain
estimates to obtain the desired small area estimates. This class of estimators
implicitly assumes that small areas that are being considered are similar, in
some sense, to some larger areas which contain them and for which the reliable
direct estimate is available. Gonzalez (1973) described synthetic estimator as
one in which an unbiased estimator of a large area was used to derive estimates
for subareas under the assumption that the small areas had the same
characteristics as the larger areas. The term “synthetic” refers to the fact that an
estimator computed from a large domain is used for each of the separate areas
comprising that domain, assuming that the areas are “homogeneous” with
respect to the quantity being estimated. Thus, synthetic estimators already
borrow information from other “similar areas”. (National Health Interview
Survey (1968) first used synthetic estimates to calculate state estimates of long
and short-term physical disabilities from the National Health Interview Survey
data. As the sample size in a small area increases, the direct estimator becomes
more desirable than a synthetic estimator. This is true whether or not the sample
was designed to produce estimates for small areas. Also, this motivates the use
of a weighted sum of direct estimator and synthetic estimator as a desirable
alternative than choosing one over the other. This weighted estimator is termed
as the composite estimator. These estimators are of interest because they permit
a trade-off among the advantages and disadvantages of direct and synthetic
estimators through their weighted combination (Rao 2003).
The synthetic methods have the advantage of being simple to implement. These
estimation techniques provide a more efficient estimate than the corresponding
design-based direct estimator for each small area through the use of implicit
models which “borrow strength” across the small areas. These models assume
that all the areas of interest behave similarly with respect to the variable of
interest and do not take into account the area-specific variability. However, it
68
can sometimes lead to severe bias if the assumption of homogeneity within the
larger domain is violated. That is the area specific variability typically remains
even after accounting for the auxiliary information. This limitation is handled
by an alternative estimation technique based on an explicit linking model,
which provides a better approach to small area estimation by incorporating
random area-specific effects that account for the between area variation beyond
what is explained by auxiliary variables included in the model, referred to as the
mixed effect model. It should be noted that the random area effects in the mixed
model capture the dissimilarities between the areas. In general, estimation
methods based on an explicit model are more efficient than traditional methods
based on an implicit model.
7.2. MIXED MODELS IN SMALL AREA ESTIMATION
The explicit models used in small area estimation are a special case of the linear
mixed model and are very flexible in formulating and handling complex
problems in small area estimation. Based on the level of auxiliary information
available, small area estimation methods are classified into two broad types:
i) The area level mixed effect models (or area level models), which are
used when auxiliary information is available only at the area level. They
relate small area direct estimates to area-specific covariates (Fay &
Herriot 1979);
ii) Unit level mixed effect models (or unit level models), proposed
originally by Battese et al. (1988). These models relate the unit values
of a study variable to unit-specific covariates.
These are special cases of the linear mixed model, usually referred to as area
level and unit level small area models.
7.2.1. AREA LEVEL MODELS
Fay and Herriot (1979) proposed an area-level small area model that relates
small area direct survey estimates to area-level covariates. The area-level model
is widely used by statistical organizations because of its flexibility in combining
different sources of information with different error structures, and can be
described as follows. Let i index the m small areas (small domains) of interest
and let y
i be an unbiased direct survey estimator of an unobservable population
parameter (for example, the population mean) Y
i of a variable of interest y for
69
small area i. Let x
i be a p-vector of known auxiliary variables for area i that are
related to the population mean Y
i.
i i iy Y eand
Ti i iY u x
, (7.1)
Where the first (measurement) equation accounts for the sampling variability of
the observable survey estimator y
iof the true area i population mean
Y
i, while
the second (process) equation is a linear regression model for the unobservable
Y
iin terms of the vector
x
i. Combining these two equations leads to an area
level linear mixed model of form
; i =1,...,m. (7.2)
Here is a p-vector of unknown fixed effect parameters, the regression errors
u
i are often assumed to be independent and identically distributed Gaussian
errors, with E(u
i) = 0 and
2var( ) ,i uu and the sampling errors e
i are
similarly assumed to be independently distributed Gaussian errors with
E(e
i|Y
i) = 0 and
2var( | )i i ie Y D . The regression and sampling errors are
assumed to be independent of each other within and across areas. An important
additional assumption is that the constants 2
iD are known. The parameter s
u
2 is
typically referred to as the variance component of (7.2). Under (7.2), replacing
unknown model parameters by estimates values, the empirical best linear
unbiased predictor (EBLUP) estimate of Y
i is (Henderson 1975; Fay & Herriot
1979)
ˆˆ ˆEBLUP T
i i iY u x , (7.3)
where ˆˆˆ y T
i i i iu x is the EBLUP of area-specific effects iu ,
1
2 2 2ˆ ˆ ˆi u u iD
is the shrinkage effect for small area i, is the empirical
best linear unbiased estimator of . See Rao (2003, chapter 5) for further
details. For non-sampled areas, the approach for estimating small areas is
synthetic estimation (Rao 2003) based on a suitable model fitted to the data
70
from the sampled areas. This is equivalent to setting the area effect for such an
area to zero. Under model (7.2), the synthetic EBLUP predictor for Y
i is
. (7.4)
Sudet al. (2012) considered an application of small area estimation techniques
to derive model-based estimates of average yield for paddy crop at districts
levels in the State of Uttar Pradesh in India by linking data generated under the
Improvement on Crop Statistics scheme by National Sample Survey Office
(NSSO) (data collected with much reduced sample size, however, the quality of
data is very high) and the Population Census 2001. They adopted the area level
model (7.2) as covariates were available only at the area level. In particular,
they illustrated how the small area estimation technique can be satisfactorily
applied to produce reliable district-level estimates of crop yield using a crop
cutting experiment supervised under the Improvement of Crop Statistics (ICS)
scheme. Further small area estimation techniques provided estimates for those
districts also where there was no sample information under ICS and so direct
estimates could not be computed. They recommended that wherever it is not
possible to conduct an adequate number of crop cutting experiments due to
constraints of cost or infrastructure or both, the small area estimation techniques
can be gainfully used to generate reliable estimates of crop yield based on a
smaller sample. Sisodia and Chandra (2012) used small area estimation
techniques for crop yield estimation at the developmental block level.
7.2.2. UNIT LEVEL MODELS
Battese et al. (1988) first employed the “nested error unit level regression
model”, which has since become one of the simplest models commonly used in
small area estimation. This model is also known as Random Intercept model.
They used small area estimation under a unit level model to estimate county
crop areas using sample survey data in conjunction with satellite information. In
particular, they were interested in estimating the area under corn and soybeans
for each of the 12 counties in North-Central Iowa using farm-interview data as
dependent and LANDSAT satellite data as independent variables. Each county
was divided into area segments and the areas under corn and soybeans were
ascertained for a sample of segments by interviewing farm operators. Auxiliary
data in the form of numbers of pixels (a term used for “picture elements” of
about 0.45 hectares) classified as corn and soybeans were also obtained for all
the area segments, including the sampled segments, in each county using the
LANDSAT satellite readings. In the model, it is assumed that the values of
auxiliary variables are known for every unit in the sample and that the true area
71
means of these variables are also known. Denoting by ijx the auxiliary values
for unit j in small area i , the model has the form,
T
ij ij i ijy u e x (7.5)
where ijy denotes the value of variable of interest for sampled unit
( 1,...., ) ij j n in area ( 1,...., )i i m , ijx is a 1p vector of unit level auxiliary
variables, is a 1p vector of the unknown fixed effects, in is the number of
sample units in area i, iu is the area specific random effect associated with
area i with mean zero and variance 2
uσ , and ije is individual level random error
with mean zero and variance 2
eσ . The two error terms are mutually
independent. The random error iu represents the joint effect of small areas that
are not accounted for by the auxiliary variables, also known as the model error
for area i. The normality of iu and ije is often assumed. Let population mean of
Y in small area i beT
i ii i iY u e X , where 1
1
iN
i i jjN
X x , is assumed to be
known. For sufficiently large iN , 1
10
iN
i i jje N e
, and then mean of Y in
small area i is approximated by i i iu X . Under (7.5), the EBLUP of the
mean of Y for small area i, is
ˆ ˆˆˆ ( )T T
i ii i i iy X x , (7.6)
where 1
2 2 1 2ˆ ˆ ˆ ˆi u u i en
and in sample size for small area i. Here, iy and
ix are the sample mean of y and x, respectively. Similar to (7.4), for non-
sampled areas, we can define synthetic estimator under (7.5). Note that the
EBLUE of used in (7.6) is defined under unit level model (7.5). In contrast,
the EBLUE in (7.3) and (7.4) is based on area level model (7.2).
72
7.3. EXTENSION OF MIXED MODELS IN SMALL AREA
ESTIMATION
It is noteworthy that small area estimation methods are based on a linear mixed
model, with area-specific random effects to account for between areas variation
beyond that explained by auxiliary variables included in the fixed part of the
model. In commonly used small area estimation methods, these random area
effects are assumed to be independent. That is, different small areas are
considered as independent to each other. However, in practice most small area
boundaries are arbitrary and there appears to be no good reason why units on
only one side of such a boundary should not generally be correlated with units
just on the other side. For example, in agricultural data, neighboring areas
exhibit strong spatial dependency and therefore independence assumption of
random area effects seems questionable. The EBLUP method can be improved
by including spatial structure in the random area effects. Singh et al. (2005)
defined the spatial-EBLUP approach in small area estimation under area level
model. Chandra et al. (2007) compared the EBLUP and MBDE approaches for
the spatially correlated populations under the unit level model. Chandra (2013)
described an improved method of small area estimation using spatial
information in estimating the crop yield at district level combining
Improvement of Crop Statistics data and known population level auxiliary
information. He exploited spatial association between the districts through a
spatial model, in particular, a Simultaneous Autoregressive error process in
random area effects under an area level model. He further explored various
ways to define this spatial weight matrix to exploit spatial information to
produce reliable estimates for small areas and suggested that spatial association
effects (or spatial dependence) should also be used to improve small area level
estimates.
As noted above, in order to increase the overall sample size in small area
estimation, information from other data sets must be used. This information can
be borrowed from “similar” areas or from a previous occasion. In the time
series modelling approach, we exploit information in data over time, namely
repeated surveys in order to obtain further improvement in the efficiency of
estimators. In general, empirical studies show that small area estimates that
draw upon information across time are more efficient than those that draw
information across an area, as the time series data usually represent the same
information about the target variable from the past (Pfeffermann et al. 1998;
Datta et al. 1998). Sometimes cross sectional and times series data are
combined to obtain further improvement in efficiency of the small area
73
estimators. In general, empirical studies show that for repeated surveys, a
considerable gain in efficiency can be achieved by borrowing strength across
both small areas and time (Rao and Yu 1994). Singh et al. (2005) used spatial-
temporal models in small area estimation. They used spatial models to exploit
spatial auto-correlation among the small area units and a spatial temporal model
fitted through Kalman filtering for the time series data. Chambers and Tzavidis
(2006) introduced the M-quantile approach to small area estimation. Standard
approaches of small area estimation assume that the underlying relationship
between the variable of interest y and the set of covariates x are linear.
However, in practice, in a lot of survey data (for example, agricultural), this
linear relationship is not valid. Chandra and Chambers (2011) proposed a small
area estimation method for the variable, which follows the linear model under
log transformation. Furthermore, the existing approaches of small area
estimation assume that the relationship between the variable of interest y and
covariates are stationary over the study space (the same for all areas). However,
this assumption may not be correct for a lot of survey data, such as agricultural
and environmental data. Chandra et al. (2012) described the small area
estimation for such data using a geographical weighted regression approach to
capture the spatial non-stationarity in the data.
7.4. SOME APPLICATIONS OF SMALL AREA
ESTIMATION IN AGRICULTURE
Rao (2003) discussed some approaches of small area estimation with
applications to agriculture data. Dorota (2006) applied small area estimation
methods in agricultural sample surveys in Poland, using the latest census of
agriculture as an auxiliary source of data. They used empirical and hierarchical
Bayes estimators, and some auxiliary information from the last census of
agriculture to obtain more precise estimates of agricultural characteristics from
agricultural sample surveys by county. Two different regression models were
considered: an area-level regression model and a unit-level one. The unit-level
approach required matching particular farms in the agricultural sample surveys
to the census of agriculture. The precision of the model-based estimates is
significantly increased compared to direct estimates.
In India, early development in small area estimation, for crop yield estimation
can be dated back to 1966 and 1968 when Panse et al. (1966) attempted to
estimate the crop yields at the developmental block (small area) level using a
double sampling approach. An estimate of crop yield from large number of
plots prior to harvest were obtained from farmers by enquiry while data on crop
yield through crop-cutting experiments was collected on a subsample from the
74
larger sample. The data from the two sources were suitably combined in the
form of a double sampling regression estimator to obtain precise estimators of
crop yield at the small area level. The approach was applied in two districts of
India. In one of the districts, the results were not encouraging because of the
poor correlation between the farmer estimate and the crop cut estimate. The
reason for the poor correlation was that the farmer appraisal data was obtained
well before the harvest time of the crop. Srivastava et al. (1999) used a
synthetic method for crop estimation at the block level. The population was
classified into two dimensions with small area on one side and post-strata
(homogeneous groups) on the other side. For crop yield, the cell weights were
estimated by ranking ratio methods using the data collected in the crop-cutting
approach. In fact, auxiliary information collected during crop-cutting
experiments was used in conjunction with small area level data for crop area for
estimating the cell-weights. This approach was applied to estimate crop yield at
the block level for wheat and paddy crops on the basis of data obtained from
crop estimation surveys in the Haryana state of India during the period 1987-
1988. The results were consistent and satisfactory. However, the results were
based on certain assumptions. One assumption was that different blocks are
homogeneous with respect to the target variable under study. This assumption is
widely referred to as the synthetic assumption in small area literature. It is a
very strong assumption, which may often not stand up. When this assumption
fails, estimates can be seriously biased. The synthetic approach of estimation
was also applied by Singh and Goyal (2000) to estimate crop yield for wheat
crop at the tehsil (sub-district) level, using remote sensing data. Post-strata were
formed using the vegetation index derived from remote sensing satellite data.
Wheat crop data from the General Crop Estimation Survey (GCES) during
1995-96 in Rohtak district of Haryana State in India while the spectral data of
IRS-IBLISS-II for February 17, 1996 was taken for the vegetation index. The
method improved the efficiency of the estimators, to some extent, in terms of
standard error. However, neglecting the bias remains a serious limitation.
Within a framework of sampling design conforming to the GCES approach,
Sud et al. (2001) developed crop yield estimates at blocks level using farmers’
estimates. The technique was similar to one used by Panse et al. (1966). In this
case, the farmer appraisal data was obtained within 15 days of harvest of the
crop. This resulted in a reasonably high correlation between the farmer
appraisal and the crop-cut data, which led to precise estimates of crop yield at
the small area level. These estimates were, in fact, direct estimates and were
based on usual sample survey techniques for improvement of estimators. One of
the merits of this approach is that it does not involve any explicit model that is
difficult to standardize in the context of finite populations. Sharma et al. (2004)
75
proposed two different types of gram panchayat level estimators of crop yield
using small area crop estimation methodology. Ahmad &Kathuria (2010)
conducted a study on estimation of crop yield at the block level using double
sampling approach.
76
8 Country experiences on crop
acreage and yield estimation This section contains an overview of the methodologies for estimation of crop
acreage and yield being followed in different countries.
8.1. BULGARIA
Agriculture covers half the national Bulgarian territory, forests cover one third.
The methods involved in estimation of crop parameters in Bulgaria are as
follows:
Crop area estimation
Data of Bulgaria on land cover and land use are obtained by a survey called
BANCIK, similar to the survey on land cover and land use LUCAS, carried out
in the European Union. The area frame is constructed by a systematic area
frame sampling in two stages where PSUs that are cells in a regular grid with
size 6 km × 6 km (3,123 segments) and SSUs that are 36 points, arranged in a
6×6 point grid of 234 m each. The survey is carried out during May to July
throughout the country. The surveyors observe the same points in the same
segments each year. This shows the land cover variations and the structural
changes in the land use. The used nomenclature is fully harmonized with the
LUCAS nomenclature. (Bulgaria 2001)
Crop yield estimation
In Bulgaria forecast data on wheat and barley production and yields are
obtained through in-situ observation (expert estimation) of points in the field
during the conducting of the land use and land cover survey (BANCIK). The
data on harvested area and production of main crops result from the survey on
main crops. The survey is carried out in November through interviews with
farmers. The holdings are defined through a random stratified sample based on
the census holdings list. Data on vegetables are obtained from a separate sample
77
survey on vegetable production. A list sample is used, based on the agricultural
census list of holdings (Bulgaria 2013).
8.2. BRAZIL
The Ministry of Agriculture, Livestock, and Supply, through the National Food
Supply Company (Conab), systematically carries out assessments of
agricultural crops to quantify and to follow Brazilian production. The Brazilian
Institute of Geography and Statistics is developing a new System of Integrated
Household Surveys based on a master sample. This process consists of many
steps, such as developing and updating a master frame, designing a sample
survey that can cover the demands of most household surveys and modifying
current household surveys in order to adapt them to the new master sample. The
methods involved in estimation of crop parameters in Brazil are as follows.
Crop area estimation
To obtain information about the estimated area of major crops, Conab uses
satellite imagery, aerial photography and geo-referenced information for
mapping cultivated areas in the main producing states. This activity began with
the launch of the GeoSafras Project, the purpose of which is improve the
methodology of the crop forecasting in Brazil through the development of
technologies related to remote sensing, satellite positioning, geographic
information systems and statistical, spectral and agro-meteorological models to
be applied in the estimates of area and yield(Brazil2013).
Crop yield estimation
By providing accurate information about the location of crop fields, mappings
are used in the spectral and agro-meteorological monitoring of crops, through
the monitoring of meteorological conditions, such as rainfall, soil moisture and
temperature, and of vegetation indices calculated from satellite images, which
reflect the condition of the vegetation and provide an indication of the level of
productivity. Historical data of agrometeorological and spectral parameters in
the mapped areas in each crop year are being used in the development,
calibration and application of models/systems to estimate productivity.
8.3. CANADA
The Agriculture Division of Statistics Canada conducts an extensive statistical
program with several highly integrated components comprised of the Census of
Agriculture, crop and livestock surveys, farm-economic statistics, agri-
environmental statistics, tax and other administrative data, research and analysis
and remote sensing data. Data are collected through computer-assisted
78
telephone interviews from the regional offices and transferred electronically to
headquarters in Ottawa. The methods involved in estimation of crop parameters
are discussed as follows (Statistics Canada2015):
Crop area estimation
For estimation of crop area in Canada, the Agriculture Division of Statistics
Canada uses a sample survey approach with a cross-sectional design. During
surveys, two types of sampling frames: list and area, are used. In the farm
surveys, only the list frame is used for the sample selection. This list frame is
stratified into homogenous groups on the basis of census characteristics, such as
farm size and crop area, and sub provincial geographic boundaries. Sample
sizes, namely the number of farms corresponding to each survey, varies. The
March Farm Survey estimates farmers' seeding intentions. In June, preliminary
seeded acreage results are published and in November the estimates are revised
with information from the surveys conducted in the fall. The survey data
collected are weighted in order to produce unbiased level indicators that are
representative of the population. Presently, the agricultural area sample survey
has been redesigned and is referred to as the area farm survey. It is used to
complement the list samples of other agricultural surveys with sometimes
different list frames. As the Census of Agriculture in Canada is conducted at the
same time as the Census of Population and uses the same enumerators, the
enumeration areas created for the Population Census serve as useful units for
the area frame. To estimate crop acreage of potato, Agriculture and Agri-Food
Canada conducts sample survey on remote sensing data, using a regression
estimator to obtain the crop acreage in major agricultural states.
Crop yield estimation
For estimation of crop yield and production, Statistics Canada uses a sample
survey approach. Since March 2014, for response burden matters, the
operations with, total arable land that is lower than a provincial threshold are
excluded from farm survey samples (null sampling). This threshold, which
varies from one province to the other, is such that all farms having an area
greater than the threshold itself represent in total 95percent of the total arable
land of a given province. The estimate share of the non-surveyed farms is then
modelled and added to the estimation derived from surveyed farms, in order to
draw a complete picture of field crop productions. The July, September and
November farm surveys provide data on the harvested area, expected yield and
production of crops on farms. The survey data are weighted to estimate
production at the provincial and crop district levels.
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8.4. EGYPT
The structure of farming in Egypt has totally changed over the past 50 years. It
has gone from being a small number of very large holdings, managed by
landowners, with little government control to consisting of a very large number
of small holdings with total government control. The methods involved in
estimation of crop parameters in Egypt are as follows (Fawzy et al. 1998):
Crop area estimation
The method formerly used by the Egyptian Survey Authority to obtain crop
area estimates entailed complete enumeration, which was very expensive. To
overcome this, a sampling technique to reduce costs and effort was developed,
with the area under major food grain crops estimated by using sample survey
methods. Later, the Ministry of Agriculture and Land Reclamation proposed a
more accurate and less expensive technique based on a check-sample of the
area determined by subjective methods of the agricultural local staff to remove
its bias. The country also adopts new technologies, such as GPS and remote
sensing, to improve the quality of crop area estimates. Currently, crop area is
estimated in Egypt by using following four methods:
(a) Direct measurements by the monitoring, verification, and evaluation
unit team, using a modern optical instrument;
(b) Direct measurement by the sampling staff using a tape on the
ground;
(c) Inquiry from the local extension staff in the village;
(d) Farmers’ estimate for his or her crop area.
Crop yield estimation
Both subjective and objective methods are used for estimating crop production
in Egypt. The best yield data come from the sampling offices, which conduct
crop-cutting surveys at harvest time. The sampling frames used for the survey
vary by governorates, a region headed by a governor. The national headquarters
determines the number of crop cutting samples for each governorate and crop.
These sample sizes are based on analysis of the previous year’s data. A
stratified multistage sampling procedure is followed to select samples. The land
areas are classified into strata based on type of irrigation and age of tile
drainage. Groupings of similar land areas are formed into clusters. The cluster
sizes vary depending on the governorate. Sampling units are formed within the
selected clusters consisting of about three feddans (1 feddan=4,200.833 square
80
meters). A random sample of two sampling units is selected from each cluster
for the crop cutting survey.
8.5. ETHIOPIA
Agriculture is the primary activity in Ethiopia, with about 84 percent of the
country’s population engaged in various agricultural activities that generate
income for household consumption. To produce crop statistics, a sample survey
is used to produce estimates of crop area and the volume of production by farm
and crop type by the Central Statistical Agency (CSA, 2011).The methods of
collection of agricultural statistics are discussed below.
Crop area estimation
The sampling methodology of crop acreage estimation consists of construction
of sampling frames, which is a list of commercial farms from all parts of the
country that includes their cropland area size and livestock number is collected
from all part of the country through the Central Statistical Agency Branch
Statistical Offices. The collected farm list is compiled at the head office and the
functional and non-functional farms at the time of updating are identified.
Before the sample selection is completed, the cutoff point for the farms is
decided. Separate cutoff points for farms involved in crop production and those
involved in livestock is set. Farms with a total area that exceeds the cutoff point
are selected with certainty whereas farms with a total area that falls below the
cutoff point is sampled using probability proportional to size, with size being
the total area of the farms. Modern technologies, such as GPS and remote
sensing, are also being used for crop area estimation. The areas of commercial
farms are directly measured by GPS while those that are state owned are
measured based on an interview.
Crop yield estimation
For crop yield estimation, a stratified two-stage cluster sample design is
implemented, where, enumeration areas are taken to be the primary sampling
units and the agricultural households are the secondary sampling units. The
sample size is determined by fixing the precision and the amount of resources
allocated for the survey. In the first stage sample, the selection of enumeration
areas is done by probability proportional to size. The second stage sample
(household) selection is done by systematic random sampling. Through its 25
branch offices, the Central Statistical Agency has put in place a comprehensive
field organization to follow up on and monitor the survey field work.
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8.6. NIGERIA
Agriculture in Nigeria is characterized by considerable regional and crop
diversity. In most of the surveys and censuses conducted by the National
Bureau of Statistics (NBS, 2007), which is the major producer of agricultural
statistics in Nigeria, crops and livestock are considered together because of the
tendency for most of the farmers to practice crops and livestock husbandry
simultaneously. The three major agricultural statistics in Nigeria are crops and
livestock statistics, forestry and wildlife statistics, and fisheries statistics. The
most important sources of survey-census-based official statistics on crops are
the National Bureau of Statistics and the Livestock Department of the Federal
Ministry of Agriculture and Rural Development. The agricultural surveys and
censuses conducted on an ad-hoc basis by the National Bureau of Statistic were
integrated into one operational program called the National Integrated Survey
of Households. The design of that survey is based on guidelines set by the
National Households Survey Capability Program, which is sponsored by the
United Nations. Notably, the National Bureau of Statistics launched the revised
General Household Survey in 2010-2011 referred to as the GHS-Panel, to
collect panel data on households, their characteristics, welfare and their
agricultural activities. The GHS-Panel is a cross-sectional survey of 22,000
households is carried out annually throughout the country. The panel
component, which is applied to 5,000 households that participate in the survey,
entails collecting additional data on multiple agricultural activities and
household consumption. In this survey, the use of the computer-assisted
personal interview was proposed for the paperless collection of the GHS-Panel.
Crop area estimation
The National Agricultural Sample Census of Nigeria covers all land holdings
(except kitchen Gardens), which are traditionally operated. A two-stage
stratified sampling design is used with enumeration areas as first stage units and
households as second stage units. In the first stage, 300 enumerated areas are
selected with equal probability from each state using a systematic selection
procedure. These are grouped into 150 strata. In the second stage, two
enumerated areas are selected from each stratum. Finally, 20 households (10
households that engage in arable crops and/or livestock rearing and 10 that keep
livestock and poultry only) are selected from each stratum. The enumerated
areas are drawn from those demarcated by the National Population Commission
for the 2006 Housing and Population Census. The parcels owned by households
are measured with the help of a tape and compass, chain or by the triangulation
method. In addition, a GPS device and an area frame survey is also used for
estimation of crop area in Nigeria.
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Crop yield estimation
For crop yield estimation in Nigeria, a stratified two-stage sampling design is
adopted for the selection of household samples. The objective crop-cutting
experiments along with farmer’s prediction is used for crop yield estimation.
8.7. FRANCE
France has been one of the most dominant agricultural centers of Europe for
centuries. With 516,100 farms (Agricultural Census 2010), approximately
3percent of the workforce is employed in agriculture or similar sectors, such as
fishing or forestry. An annual survey is conducted by the surveyor as a part of a
land survey using aerial photography during the months of June and July, on a
date that falls between the sowing of seeds and harvesting for estimating areas
under cereals and fruit crops. Furthermore, cereal surveys are conducted to
measure wheat, barley and maize production from area and yield data.
Crop area estimation
To determine the land use and land cover statistics France relies on the
TERUTI system, which is similar to the Land cover/use (LUCAS) system, to
provide statistics on land use and land cover across the European Union (FAO,
2015). This is a two-stage system. For the first stage, square segments of 36
points, each 300m apart, are observed within each square. For basic
observations, the observation of the point in the field covers a circumference
having a diameter of 3m; in extended observations, the circumference has a
diameter of 40m.
Crop yield estimation
In France, yield figures are estimated from samples selected from sample plots
of known areas at random in several stages. Both subjective and objective
methods of crop yield estimation are used for crop yield estimation. Two types
of methods, namely selection of holdings and land use survey are used for
estimating yield. Regarding the selection of holdings technique, the sampling is
done at four stages, namely communes, holdings, fields, and sample plots. The
survey is conducted by interviewing farmers. Measurements of sample ears of
wheat or barley from plots of approximately one square meter and samples of
ears of maize from two rows five meters in length are taken for an objective
crop-cutting experiment.
83
8.8. UNITED STATES OF AMERICA
The National Agricultural Statistics Service is tasked with generating
agricultural production statistics in the United States. The data collection work
is assigned to state statistical offices. The March/June survey of crop acreage is
used for framing estimates of crop acreage. The major emphasis in the survey is
on planted areas. Each sampled segment is divided into tracts, which are
delineated on photographs. A tract is a parcel of land within a segment under
one management. An area frame for a land area, such as a state or country,
consists of a collection or listing of all parcels of land for the area of interest
from which to sample from. These land parcels can be defined based on
ownership or simply on easily identifiable boundaries as is done by the National
Agricultural Statistics Service. The major area frame survey conducted by the
Service is the June Agricultural Survey. This mid-year survey provides area
frame estimates primarily for crop acreages and livestock inventories. Two
questionnaires are developed, one for the people living in the segment and the
other for those who live outside the segment. See Davies (2009).
Crop area estimation
For crop area estimation, area sample is used in addition to the sample selected
from a list of large farm operators. The area sample is the outcome of a single
stage stratified sampling. The basic stratification employed by National
Agricultural Statistics Service involves: (1) dividing the land into land-use
strata, such as intensively cultivated land, urban areas and range land, and (2)
further dividing each land-use stratum into substrata by grouping areas that are
agriculturally similar. Within each stratum, the land can be divided into the
sampling units or segments and then a sample of segments is selected for a
survey. This is a very time-consuming endeavor. The time spent developing and
sampling a frame can be greatly reduced by: (1) dividing the land into larger
sampling units called first-step or PSUs, (2) selecting a sample of then
delineating the segments only for these units , and (3) selecting a sample of
segments from the selected PSUs. Boundaries of sample segments are
delineated on an aerial photograph. The area of segment is determined by a
planimeter. The open segment concept is useful when farms are used as
reporting units and is applied in novel way. The June survey is conducted
annually and each year only 20 percent of the earlier selected sample is
replaced. The enumerators and supervisors are trained before the survey.
84
Crop yield estimation
Objective yield surveys are executed for reporting yield statistics where
specifically cropped fields are randomly selected based on probability
proportional to size. These yield surveys involve making counts and
measurements of selected crops and weighing them. The yield estimation is
made on the basis of measurement of plant characteristics. Generally, two units
are measured in a selected field. Each unit consists of a specified number of
rows of predetermined length or rectangular units if crop rows are
indistinguishable. The acreage reported in the March/June agriculture survey
provides the sampling frame for yield surveys. Probability samples are selected
for developing estimates. Sample allocations are made in such a manner that the
sampling errors of the estimates are minimized. The March agriculture survey
uses a multiple frame, namely a list and area frame. The enumerators are
provided with aerial photographs along with area segments for data collection.
8.9. INDIA
India is primarily an agriculture-based country; its economy is largely
dependent on agriculture. The Directorate of Economics and Statistics, Ministry
of Agriculture, is the pivotal agency for the coordination and compilation of
agricultural statistics at all administrative levels. Other principal agencies that
collect data and conduct methodological studies on agricultural statistics are the
National Sample Survey Office, the Indian Agricultural Statistics Research
Institute and the State Directorate of Economics and Statistics. For more details
see MoS & PI (2008).
Crop area estimation
The crop area statistics are generated in large parts (84 percent) of the country
through complete enumeration. Cadastral maps of these areas exist. In areas
that have not been cadastrally surveyed (15 per cent), a sample survey approach
is used for data collection. A stratified unistage sampling design is adopted for
data collections in areas where in blocks are the strata, and a 20 per cent sample
of villages is selected. In successive years, a new group of non-overlapping 20
percent sample of villages is selected. Thus, over a five-year period, the entire
area is enumerated. In the remaining portions (1 percent), crop area statistics are
generated by the village headman. The data quality in those areas is not very
high, as the village headmen are usually not trained to in the field of statistics.
Crop yield estimation
Yield of crops in the country is estimated on the basis of crop-cutting
experiments. A stratified multistage random sampling design where tehsils are
85
strata, villages are the first stage unit of sampling, fields growing a particular
crop are the second stage unit of sampling and a plot of a specified size is the
ultimate unit of sampling. Generally, square plots are used for the crop-cutting
experiments. For crops, such as cotton, rectangular plots of a larger size are
used. In some parts of the country, triangular and circular plots are also used.
8.10. SOUTH AFRICA
The South African Department of Agriculture Crop Estimates Committee is
tasked with producing crop estimates for the country on a monthly basis. To
perform this task, the Committee receives data from various input suppliers.
The National Department of Agriculture is the custodian of the Crop Estimates
Committee. The grain crop production estimates are published monthly. The
Committee meets monthly to review and debate the current status of cropped
areas and conditions to determine crop production estimates for grain crops.
Grain crops in South Africa consist of two groups: the first group is comprised
of crops, such as maize, sunflower, Soya beans, and sorghum, that are
cultivated during summer and second one is made up of crops grown during the
winter, such as wheat, barley, oats and canola. Information about crops was
previously only supplied by provincial and industry representatives and through
qualitative reports on weather patterns and crop conditions that resulted in
subjective calculations. See Ferreira et al. 2006.
Crop area estimation
The Producer Independent Crop Estimate System was developed in 2005.
Implemented after a successful pilot study conducted in the Gauteng province,
the System uses crop field boundaries digitized from satellite imagery with a
point frame sampling system to objectively estimate the area planted with grain
crops. It involves several steps, starting with the procurement of satellite
imagery. Then, digitization of crop field boundaries from satellite imagery is
completed, followed by designing the point frame and selection of random
sample point. The next step entails using aerial survey sample points to capture
crop data and as the final step, statistical analysis is performed.
Crop yield estimation
The Crop Estimates Committee is responsible for the official crop forecasts and
estimates of summer and winter field crops for the country. The summer crops
for which estimates obtained are maize, sorghum, groundnuts, sunflower seed,
soya beans and dry beans. For the purposes of the Committee, white maize and
yellow maize are treated as two separate crops and then added together to
obtain total maize. For yield estimation, two kinds of surveys are done, namely
an area and farmer expected (subjective) yield survey and an objective yield
86
survey. The subjective survey is conducted by randomly selecting a number of
points over the relevant provinces. The points where maize is located are then
used to select subsamples for the objective yield surveys. For the winter crops
survey, data are collected for wheat, malting barley, canola and sweet lupines.
The points where wheat is located are then used for the objective yield survey
subsample. The subsample is selected using a probability proportional to size
sampling design. The fields of the subsample are visited and two plots are
randomly chosen in the fields and the plots are laid out. Measurements are then
taken on the plots, the number of plants in a selected area is counted, the
number of ears and the number of seeds per ear is counted and the mass is
calculated. The counted plants and measurements are then analyzed for
estimation of crop yield.
8.11. SUDAN
Agriculture plays a very important role in the economy of the country as more
than 70 percent of the population is engaged directly or indirectly in this
activity. The General Directorate of Planning under the Ministry of Agriculture
in Sudan conducts the crop estimation surveys (Elsaied & Ahmed 2013).
Crop area estimation
For crop area estimation, a stratified two-stage sampling design with an uniform
sampling fraction (proportionate allocation) is adopted where the sheikh ships
(villages in Sudan) are the first stage unit of sampling and holdings growing
crops form the second stage unit of sampling. For the wheat crop, area statistics
are compiled through complete enumeration. Farmers’ eye estimation is also
used for estimation of the areas for some crops.
Crop yield estimation
Crop yield estimation is done using crop-cutting experiments. A stratified
multistage sampling design is adopted for estimating the yield rate of wheat and
sorghum. For -cutting experiments, plots of size 7m×6m are randomly
demarcated. The statistics division of the Agricultural Planning Administration
carries out an annual survey to estimate yield of the main crops, namely
sorghum, wheat, groundnuts and cotton. The blocks are considered as strata; the
first stage unit of sampling are tenancies (i.e. farm operator) and the ultimate
stage units are the plots of size 2m×2m. Then the crops are cut dried, threshed
and weighed, and the weighted average is calculated to estimate the yield of
crop.
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8.12. MOROCCO
The country’s agricultural survey program is based on area sampling methods
conducted by the Division of Statistics and Computer Science of the Directorate of
Planning and Economic Affairs, Ministry of Agriculture and Agricultural
Development. Some of the main crops are cereals, legumes, such as beans, peas and
lentils, olives, vegetables, sunflower and citrus.
Crop area estimation
In the early 1980’s, a stratified area and list sampling design was used for crop
area estimation (Bouzaffour & Hanuschak 1998).Stratification of the area frame
was carried out on a variety of cartographic materials depending on what was
available for an area when the work was being done. The stratification was also
carried out by using aerial photography and maps of various ages and, in some
instances, the frames were stratified using only maps. The stratification is now
based on recent photography and/or recent TM and SPOT images. The survey is
conducted in two phases. The first round takes place from 15 February to 15
April. It entails gathering data on autumn crops planting and on planting
intentions for the spring. The second round, which takes place in May and June,
involves gathering final planting data for the spring crops. The sample includes
about 70,000 farmers and provides estimates at the provincial level and at the level of
"special action zones". The area frame sample covers 90percent of the area surveyed,
the remaining 10 percent is covered by a village sample.
Crop yield estimation
The objective crop-cutting experiment and farmers eye estimation method is
used to obtain the crop yield estimates at different provincial levels by the
Director of Programming and Economic Affairs. The crop yield surveys are
carried out from May to September, with the exact date depending on the time of
maturity of each crop. Samples of the mature crops are harvested and sent to a
laboratory for threshing and assessing moisture content, among other things, to make
an objective estimate of yield. In addition, forecasting of crop yield is done using
agro-meteorological models based on remote sensing data, meteorological
inputs from ground stations and meteorological satellites, soil types and texture,
plant growth processes and measurements, and remotely sensed vegetative
index data.
8.13. RWANDA
The National Institute of Statistics of Rwanda is coordinating the National
Statistics System and is responsible for providing timely, accurate, and useful
statistics in all sectors of the country, including agricultural statistics. It has
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embarked on designing and implementing a new and improved system of
agricultural statistics.
Crop area estimation
The multiple frame survey design combines a probability sample of segments
selected from an area sampling frame, with a complementary list of large scale
farms to be completely enumerated. A stratified two-stage random sampling
design is adopted wherein PSUs are selected by a probability proportional to
size with replacement sampling design and within selected PSU segments of 10
hectares are randomly selected and completely enumerated. The area statistics
is collected through GPS. Personal digital assistant devices are also used for
data collection.
Crop yield estimation
For the purpose of estimation of crop yield, 25 percent of the already-selected
segments are used. The crop production estimates are obtained on the basis of
farmers eye estimate. The production estimates are divided by the crop area
harvested in order to obtain crop yield.
Both the crop area and yield estimates are obtained at the national level using
the appropriate sample sizes.
8.14. INDONESIA
Indonesia has a centralized national statistical system. BPS-Statistics Indonesia,
the main agency for generating agricultural statistics, is responsible for
conducting major censuses and surveys in the country and the dissemination of
all official statistics.
Crop area estimation
Crop area estimates are obtained using complete enumeration of all the sub
districts in the country. The data on planted area, harvested area, damage and
standing crop area in wet land and dry land are collected using eye estimates,
seed utilization, the irrigation block system and reports from farmers.
Crop yield estimation
A stratified multistage random sampling design is adopted wherein census
blocks are the first stage units, households in the selected census blocks are the
second stage units, fields are the third stage units and plots of specific size and
shape are the ultimate stage units. The crop- cutting experiment approach is
used for yield estimation.
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8.15. JAMAICA
The agricultural sector of Jamaica is one of the most important contributors to
the country’s GDP. The sector, with its diverse areas of specialization, has been
a major player in the country’s food supply, which ranges from domestic crop
production to high quality meat and milk supply to a wide cross-section of the
society.
Crop area estimation
Areas under cultivation are measured using measuring tapes or wheels on the
plains and eye estimation on sloped area.
Crop yield estimation
Crop yield is measured or estimated based on plant population by ascertaining
the planting distance for each crop.
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9 Conclusions The purpose of this report has been to define relevant concepts related to crop
area and crop yield estimation, in general, and in the context of mixed, repeated
and continuous cropping, in particular. In addition to presenting a critical
review of literature on this subject, country-experiences have also been
reported. While reviewing the literature, it has been found that there are several
issues and problems with regard to crop area and crop yield estimation.
A number of crop area estimation methods are available in practice, such as the
polygon method, triangulation/rectangulation and P2/A method. The polygon
method is accurate, but is costly and time-consuming. Thus, there is growing
demand for a cost-effective method for crop area estimation, especially in
developing and underdeveloped countries. Technologies, such as geographic
information systems, GPS and remote sensing are gaining importance for crop
area estimation. Area measurements using these technologies are, by and large,
more rapid, time efficient, digital and easy to incorporate into a database.
However, there are some concerns with regard to their accuracy and precision.
In the future, remote sensing may become an important alternative for crop area
estimation, but this method remains difficult to use in underdeveloped and
developing countries, where agriculture is primarily dominated by small plots,
different planting dates, scattered trees and intercropping systems.
The whole plot harvest method of crop yield estimation is almost bias-free, as
all sources of upward bias reported for crop cuts can be eliminated when the
entire field is harvested. However, it involves a large volume of work, making it
impractical for moderate-to-large sample sizes or multiple crop studies. Two
widely used methods in various countries are farmers' estimation and the crop-
cut method. Both methods have their own inherent pros and cons. The farmers’
estimation method is quick and cheap, but may result in poor quality data
because of intentional over/underreporting of crop production. The problem is
further aggravated by illiteracy among farmers and inappropriate timing of the
interview before/after the crop harvest.
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The crop-cut method, on the other hand, has been regarded as a reliable and
objective method for estimating crop yield. This method, however, may be
accompanied with an inherent upward resulting from measurement errors and
increased cost and time. Nevertheless, these deficiencies can be largely
overcome by appropriate training and supervision and by using optimum
sample sizes and auxiliary data available in the system. A strong advantage of
the crop-cut method is that the area of the cut is known and therefore errors are
not introduced into the final yield computation.
The choice of these two methods, however, is divided among countries. For
example, national statistical institutes in Kenya, Rwanda and Sweden prefer to
use farmer recall data to obtain production estimates, while Benin, India, Niger
and Zimbabwe opt for the crop-cut method. The United States Department of
Agriculture uses a combination of farmer recall for its agricultural census and
crop cuts for yield estimation of specific major crops in specific states. Several
European countries favor more expensive crop cuts for potatoes, but use
cheaper methodologies, such as farmer recall, expert assessment or purchase
records, for other crops.
Therefore, to estimate crop areas and yields, several region-specific issues, in
general, and the crop-specific issues, in particular, in the context of mixed,
repeated and continuous cropping need to be addressed. For crop area and yield
estimation, there is a need to conduct more studies in different countries to
establish supremacy of one method over another. For crop area and yield
estimation in the context of mixed, repeated and continuous cropping, relevant
issues are (i) non-availability of an updated sampling frame, (ii) determination
of optimum sample size relevant to the sampling design and estimation
procedure employed, (iii) choice of an appropriate sampling interval in
continuous cropping scenario, (iv) procedures for measurement of area and
yield under a particular crop, (v) procedure for apportioning of crop area when
temporary and permanent crops are grown together, (vi) suitable procedures for
capturing crop produce with extended harvest, (vii) appropriate schedules for
capturing such data, (viii) cost effective procedures for efficient collection and
processing of such data, (ix) procedures for reducing non-sampling errors for
good quality data and (x) developing user-friendly software for processing
survey data and analysis, including the sampling weights and computation of
standard errors of estimators so as to enable timely release.
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Annex I Calculation of the area of a polygon
The procedure as given in FAO 1982 is provided here. Let a polygon with n
sides be defined as
ai, αi i = 1, 2,…,n
where ai is the length of the side i and αi is the angle this side formed with
North measured in clockwise direction. Again let, a vector iar
represents the
side i in a two dimensional space XOY in which Y-axis coincides with the
North. Thus, the horizontal and vertical projections of the vector iar
(figure
A1.1) are ai sin αi and ai cos αi, respectively.
Figure A1.1. Horizontal and vertical projections of a vector
Define vectors
i
i jj 1
R a ,
r
i = 1, 2,…,n. (A1.1)
Their horizontal and vertical projections will be, respectively
i
i j jj 1
X a Sin a
(A1.2)
93
i
i j jj 1
Y a Cosa .
(A1.3)
If the polygon is closed, then nR 0.r
The area of a triangle formed by two vectors, which start from the same point,
can be calculated as a function of their horizontal and vertical projections. Thus,
the area of the triangle between vectors 1Rr
and 2Rr
(figure A1.2) is given by
1 2 1 1 21
A X Y X Y2
Figure A1.2
It should be noted that this area will have a positive value if the vector 1Rr
precedes the vector 2Rr
while looking clockwise, otherwise it will have a
negative value. The area of the whole polygon, calculated as a sum of areas of
triangles, each formed by the two consecutive vectors iRr
, will be
n 2
i 1 i i i 2i 1
1A X Y X Y
2
(A1.4)
where Xi and Yi are given by Equation (A1.2) and (A1.3).
94
Closure error and corrected area of a polygon
In practice, the polygon defined by the data which are collected in the field will
never close. In this case
nR 0.r
The-length of the vector nRr
2 2n n nR X Y
which can be used as a measure of error. However, a common practice is to
express the closure error as a percent of the perimeter of the polygon:
nn
ii 1
RC 100
a
(A1.5)
The errors are considered acceptable if the closing error is less than 2 percent.
There are different methods of closing the polygon (FAO 1982), including:
A. Closure by connecting the last but one point with the starting point;
B. Closure from the mid-point;
C. Closure by shifting all vertices on an equal basis;
D. Closure by shifting all vertices on a proportionate basis.
95
Figure A1.3. Methods A, B and C
An advantage of the first three methods is that there is no need to keep in the
memory all input data until the end of the calculation. In these three methods,
each pair of input data can be elaborated when they are entered, and required
sums can be aggregated. The moment the last pair of data is entered, corrected
area and closure error can be evaluated. On the other hand, in the fourth
method, there is a need to keep in the memory all input data for one polygon
until the calculations are completed. It is important to note that the four
methods give an unbiased estimate provided measurement errors are absent.
96
Annex II Steps involved in conducting crop-cutting experiments
The steps involved in conducting crop=cutting experiments are as follows:
a) Selection of field where the crop-cutting experiment is to be carried out;
b) Locating and marking the experimental plot of a given size and shape in
the selected field;
c) Harvesting the crop of the experimental plot;
d) Threshing of crop harvested from the experimental plot;
e) Winnowing of the threshed crop;
f) Weighing the produce obtained from the threshed crop;
g) Drying the produce, in case of excess moisture;
h) Weighing the dry produce.
Size and shape of the crop cutting experiment plot
During earlier attempts in crop surveys, greater attention was paid in deciding
the size of the crop-cutting experiment plot in a selected field. Various plot
sizes were tried varying from 1/160 of an acre for paddy in Orissa State, India
to 1/10 of an acre for cotton in Madhya Pradesh State, India. The plot size
adopted in the earlier attempts by Hubback (1946) and Mahalanobis (1939) was
very small, being of the order of 1/2000 of an acre. Attempts have been made
since 1944 to study the relative efficiency of various plot sizes for yield rates.
On the basis of detailed investigations, the size and shape of the crop cutting
experiment plot for various crops, in respect of different states, are specified.
The shapes of the cuts for various crops vary to some extent in different states.
In most of the states and for many crops, the plots are either square of size 5m ×
5m, 10m × 10m or rectangle of size 10m × 5m. In the Uttar Pradesh State,
India, the experimental plot is an equilateral triangle of side 10 m for most of
the crops and in West Bengal State, India, it is a circle with a radius of
approximately 1.7145m. For some crops, especially fruits, it consists of specific
number of trees.
97
The plot size adopted for different food and non-food crops is listed in the table
below.
Name of the crop Shape Length (m) Breadth (m) Diagonal (m)
Paddy, wheat, sorghum, pearl
millet, maize, groundnut, tobacco,
sugarcane, green gram, chilly,
horse gram, black gram, chickpea,
sunflower
Square 5 5 7.07
Redgram, sesamum, caster, cotton Square 10 10 14.14
Selection of field
Field is a distinct piece of land where the crop is grown. It is clearly demarcated
on all its sides either by bunds or by patches of other crops or left uncultivated.
As per the existing methodology of estimation of yield rates of crops, two fields
of the crop are selected in each selected village and one experimental plot of the
crop is selected in each selected field (Sukhatme & Panse 1951). When
selecting two fields in each selected village, two random numbers are assigned
to the primary worker. The complete land of the selected village is divided into
fields. Each field has its own identification number called the survey number or
khasra number. The highest survey number in the selected village may be
greater than, equal to or less than the random number assigned for selection of
the field. If the assigned random number is equal to or less than the highest
survey number, the survey number corresponding to the random number is
selected and if it is greater than the highest survey number, the assigned random
number is divided by the highest survey number and the survey number
corresponding to the remainder is selected. In cases in which the remainder is 0,
the highest survey number is selected. If the crop is not grown in the selected
survey number, the next survey number is selected.
98
If the selected survey number is further divided into subdivisions, only one
subdivision is selected randomly. In cases in which the selected
survey/subdivision number contains more than one field where the crop is
grown, the field nearest to the south-west corner of the survey/subdivision
number is selected. The selected field must satisfy the following conditions:
a) The area of the selected field should be more than the area of crop
cutting experiment plot, so that the crop cutting experiment plot of the
recommended size fits in the selected field.
b) If the selected field is sown with mixed crops, the experimental crop
must constitute at least 10 percent of its crop area.
c) The experimental crop in the field is not meant for prize competition or
seed production or demonstration.
d) The experimental crop is not grown for fodder purpose.
The field must be considered for conducting a crop cutting experiment and the
yield obtained from the cross cutting experiment plot must be recorded, if the
a) Experimental crop has not germinated or has failed but its area is
recorded by the village accountant;
b) The field where the experimental crop is being grown is being grazed
by cattle or damaged partially or completely by wild animals;
c) The experimental crop is affected by pests/diseases or heavy
rainfall/inadequate rainfall;
d) The yield must be recorded as zero in case the experimental crop is
completely damaged.
The field need not be considered for selection for conducting a crop cutting
experiment, if the experimental crop has
a) Not germinated or has failed and its area is not recorded by the village
accountant.
b) Withered or dried up and another crop has been raised in its place in the
same season and the area of the new crop has been recorded by the
village accountant.
c) Substitution of fields is not allowed on concerns over poor growth or
prior harvest by cultivators without intimation to the primary worker or
due to a late visit by the primary worker. Furthermore, if a part or all of
the selected field has been already harvested, the experiment should not
be conducted in that field, and it has to be treated as lost.
99
Identification of south-west corner of the selected field
After the field is selected, the south-west corner of the field needs to be
identified. If one stands at the south-west corner facing north of the selected
field, the selected field will be in the front and at the right hand side of the
person. Fixing the south-west corner of the selected field has been made
mandatory to ensure similarity. It also helps the supervisor to identify the crop-
cutting experiment plot in the absence of an enumerator. In cases in which the
selected field is not exactly in north-south and east-west direction, the corner
that is approximately south-west may be taken as the south-west corner of the
selected field. The south-west corner of the -cutting experiment plot is
randomly located with reference to the south-west corner of the selected field.
Measurement of the length and breadth of the selected field
The method for of measuring the selected field differs for regular shaped and
irregular-shaped fields.
Regular shaped field: When the selected field is of a regular shape then the
longest side is measured as the length and the measurement of the shorter side
is the width in the steps from the south-west corner of the field (figure A2.1).
Figure A2.1. Field in regular shape
Irregular shaped field: In cases in which the selected field is irregular in
shape, the selected field needs to be enclosed in a regular shape by the outer
least possible dimensions for the purpose of locating the south-west corner of
the experimental plot in the selected field. The longest side should be measured
as the length and the shorter side measured as the breadth of the outer regular
shape of the irregular selected field in steps. The south-west corner of the
experimental plot should be fixed with reference to the south-west corner of the
outer regular shape of the irregular selected field (figure A2.2).
100
Figure A2.2. Field in irregular shape
Determination of the random number pair
To ensure that the whole experimental plot fits in the selected field, seven steps
have to be deducted from the length and the breadth of the selected field (7
steps are equal to approximately 5 meter).
Example:
Length of the selected field measured in steps = 120 Steps
Number of steps to be deducted from length = 7 Steps
Number of steps in length after deducting 7 steps = 113 Steps
Breadth of the selected field measured in steps = 70 Steps
Number of steps to be deducted from breadth = 7 Steps
Number of steps in breadth after deducting 7 steps = 63 Steps
Two random numbers, one for length and the other for the breadth are selected.
These selected random numbers should be less than or equal to the number of
steps obtained after deducting 7 steps from the length and 7 steps from breadth
of the selected field. The random number is selected from the column of the
random number table assigned to the enumerator/primary worker for
determination of the south-west corner of the experimental plot.
Column number 1 of random number table should be assigned to the primary
worker. First, a random number for the length needs to be selected and then the
same thing needs to done for the breadth. In the above example, 113 steps are
obtained after deducting 7 steps from the length of a selected field. This
comprises three digits. Therefore, by referring to the three digitized random
101
number table, a random number that is equal to or less than 113 is selected. By
referring to column 1 of the three digit random number table, the first random
number is 058. Therefore, random number 058 is selected for length. The
second random number is selected for breadth. After deducting 7 steps from the
breadth of the selected field, the remainder is 63 steps. Since, 63 comprises two
digits, by referring to the column number, one of the two digit random number
table, a random number which is equal to or less than 63 is selected. By
referring to column 1 of the two digit random number table, the first random
number is 51. Accordingly, random number 51 is selected for the breadth. The
pair (58, 51) is the pair of random numbers selected for locating the south-west
corner of the experimental plot in the selected field. If the assigned column of
the random number table is exhausted during the process of selecting the
random numbers, the next column on the right hand side is the reference
column. If all of or part of the experimental plot goes beyond the boundary of
the field, owing to the irregular shape of the field, the pair of random numbers
is rejected and a new pair of random numbers is selected until all of the
experimental plot is accommodated within the field.
Marking of the experimental plot
The selected random number for the length is 58. Therefore, it is necessary to
move 58 steps along the length of the field from the south-west corner of the
field and from the point reached by measuring 58 steps, and then moving 51
steps perpendicular to the length and parallel to breadth of the field. The point
reached is the south-west corner of the experimental plot shown as point “A” in
figure A2.3. The point “A” is also referred to as the key point of the
experimental plot. A peg at the key point of the experimental plot should be set.
Figure A2.3: Marking of experimental plot (Step-1)
102
Five meters along the length of the field from corner “A” needs to measure to
reach the next corner, which is the second corner of the experimental plot say
corner “B”. At corner “B”, a peg should be fixed (figure A2.4). The line joining
the point “A” and “B” is the base of the experimental plot.
Figure A2.4: Marking of experimental plot (step-2)
To mark the third and fourth corner of the experimental plot, the right angle
triangle method should be applied. To mark the third corner, the first person
should stand at corner “A” while holding the measuring tape at the zero meter
mark on the measuring tape and second person should stand at corner “B” while
holding the same measuring tape at 12.07, namely the 7.07+5.0 meter mark.
The third person holding the measuring tape at 7.07 [square root of (52 + 52)]
meter mark should stretch the measuring tape in the inner side in the direction
of the breadth of the field. The point reached shall be the third corner of the
experimental plot say corner “C”. A peg should be fixed at corner “C” (figure
A2.5). Corner “C” is 5.0 meter away from corner “B” and 7.07 meter (diagonal)
from corner “A”.
Figure A2.5. Marking of experimental plot (step-3)
To locate the fourth corner of the experimental plot, the third person standing at
corner “C” should hold the measuring tape at 5.0 meter mark and the stretch it
103
towards inner side in the direction of the breadth of the field, the point reached
is the fourth corner of the experimental plot referred to as corner “D”. A peg
should then be fixed at corner “D” (figure A2.6). The corner “D” is5.0mfrom
corner “A” and 7.07 m from corner “B”.
Figure A2.6. Marking of experimental plot (step-4)
A, B, C and D are the four corners of the experimental plot. The distance
between A and B, B and C, C and D, A and D should be checked. The distance
between A and B, B and C, C and D, A and D should be equal to 5.0 m. The
distance between both the diagonals AC and BD should also be checked. The
distance of each diagonal should be equal to 7.07 m (figure A2.7). It is
important that the pegs should be tall, straight and firmly fixed on the ground.
Figure A2.7. Marking of experimental plot (step-5)
104
Harvesting of experimental plot
A well-stretched string should be tied around the pegs. The string should be
lowered gradually to the ground level. The position of the string on the ground
demarcates the boundary of the experimental plot. The decision about whether
the plants lie within the experimental plot is based on the position of their roots.
The plants on the boundary of the experimental plot should be harvested only if
the roots are more than half inside the experimental plot. Care should be taken
to collect all the harvested plants and no ear heads should be left in the
experimental plot. Farmers should be requested not to harvest the field until the
whole experimental plot is harvested and harvested crop is gathered and
brought to the threshing floor. The bundle of the harvested crop should be
marked/tagged to prevent it from be mixed with other crops.
Threshing and winnowing of the harvested experimental crop
A piece of Hessian cloth should be used for drying and threshing the
experimental crop. The harvested experimental crop should be spread on the
cloth at the threshing floor for drying and threshed carefully as per the usual
method. All the grains of the threshed experimental crop should be separated by
winnowing. The clean grains should be weighed at the nearest possible
weighing unit. After being weighed, the produce should be returned to the
farmer. If the produce has more moisture, a sample of the recommended
quantity of the produce has to be taken and kept in a cloth bag until the
moisture is dried up.
Driage
It is necessary to carry out drying experiments to obtain final estimate of yield
in terms of dry produce, the experiments for different crops should be
conducted at the district level by the district statistical officer. Crop-cutting
experiments supervised by the district statistical supervisor must be selected for
drying. The drying experiments are conducted in respect of 15 percent of the
experiments planned for the specific crops or subject to a minimum of four
experiments per crop. Generally, a one kilogram sample of harvested produce
should be taken at random for drying by the district statistical supervisor. If, the
produce obtained from the experimental plot is less than one kilogram, the
entire produce is to be taken. With regard to sugarcane, the final produce should
be expressed in terms of cane only. In the case of cotton, the final produce
should be expressed in terms of lint. The cotton is converted into lint by using
ginning percentage (cotton to lint), which is obtained from the ginning
factories. A required sample of the produce is taken in a small bag and kept for
drying by the usual method for a specified period. The dry weight should be
105
taken at the nearest possible weighing unit after the moisture of the produce has
been completely eliminated.
106
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