1 agricab final meeting antwerp, march 24, 2015 use case: agricultural statistics david remotti,...

1AGRICAB final meetingAntwerp, March 24, 2015

Use case: Agricultural Statistics

David Remotti, Laura Monaci, Michele Downie - Consorzio ITA

Objectives of this task:

• to introduce rigorous sampling methods to achieve reliable crop

area estimates, with known accuracy, for all major crops in 3

different countries (Kenya, Senegal, Mozambique)

• to assess the feasibility of a georeferenced sampling approach to

agricultural statistics, in the African context

• to startup a capacity building process in this field: all activities

has been carried out by the local partner with the technical

assistance of Consorzio ITA

In all the use cases, annual agricultural statistics are built upon the results of a General Census: in particular a list of farms is developed during the Census and this list is the “population” from which a “sample” is periodically extracted and visited to get information about crop areas and production

Agricultural statistics:

This list-based approach has several problems:• incompleteness and rapid outdating of the list, even more significant in case of a remarkable agricultural transformations in the country,• unreliability of the results based only on interviews with farmers, that are not objective,• impossibility of a Quality Control based on objective criteria

The proposed georeferenced approach on the contrary is supported by totally objective procedures. This approach does not imply in any way a direct contact with the farmers: the ground survey, is made by surveyors who has been specifically trained and who apply objective methods.


Georeferenced approach:

The implementation of our approach involves the following steps:

1. Creation of a sampling frame whose units are represented by regularly spaced points. Each point is characterized for the landuse through photointerpretation of high-resolution satellite images freely available on Google Earth. This information are used to stratify the sampling frame in homogeneous sub-populations

2. Allocation and extraction of sampling units. In the context of AGRICAB it was possible to carry out only one ‘round’ of surveys in the 3 use case countries. In the absence of data on the population variance, the calculation of the sample size was determined based on previous experiences of ITA. The same sample size can now be optimized based on the data collected in the AGRICAB survey.


3. Preparation of the ground survey, including training of personnel from the Administrations involved in the project, and production of the needed materials (maps, data entry programs, logistics etc.

Georeferenced approach:


4. Ground survey carried out during the agricultural season, by surveyors appropriately trained in the use of GPS, topographic maps, and crop identification.

5. Quality control to determine the degree of completeness of the recognition of the crops and their proper labelling.

6. Acquisition and processing of high-resolution satellite images. The aim is to assess the capability of the Earth Observation data in reducing the variance of the crop acreage estimations.

7. Data elaboration to calculate statistical estimates of crop surface in the whole reference area: this is done through our specific software STAT_AGRI. The software is owned by ITA and made available free of charge within the AGRICAB project. In this regard, a specific workshop was programmed and implemented in each of the three countries. This was with the aim of making the experts from respective organisations, able to independently manage the software.


3 use cases:

Senegal

Mozambique

Kenya


3 use cases:

COUNTRY DEPARTMENT POINTS SURFACE (ha)

Kenya Kagamega, Butere, South Meru

13 743 343 000

Senegal Nioro du Rip 9 004 225 000

Mozambique Inharrime 10 991 275 000

In each country the grid spacing of the sampling frame was 500 meters; approximately 1000 sample points has been extracted and visited on the ground

In Kenya the use case has been extended to a 3° department (south Meru) thanks to the integration with another project (eAgri)


Sampling frame construction


Stratification (use case: Kenya)

The stratification of the working area has been done by joining some of the landcover classes to obtain only 3 strata:

• Str. 1 (agriculture) is made of landcover classes 2-3-4

• Str. 2 (natural) is made of landcover class 5-6-8

• Str. 3 (builtup) is made of landcover class 1

landcover class 7 (bare soil) has been excluded

District / strata not agr nat urb Tot

Butere 7 2.517 862 402 3.788

Kakamega 36 3.132 1.844 588 5.600

Meru S 20 1.311 2.788 226 4.345

Tot 63 6.960 5.494 1.216 13.733


Dept / str agr nat urb Tot

Nioro 1002 72 10 1084

Sampling ratio 0.14 0.05 0.035 0.12

sample allocation (use case: Senegal)


Ground Survey


Estimates at department level

Estimations are calculated with the Stat_Agri software who implements the stratified sampling model. The surface estimates for the main crops, their reliability in terms of coefficient of variations, and the intervals of confidence, are provided.

Estimates for Department Nioro duRip (hectares)crop Surface StdDev CV interval of confidenceArachide 82.119 3.015 3.67 76.208 88.030Mil 59.311 2.744 4.63 53.932 64.690Mais 22.414 1.874 8.36 18.740 26.088Jachere 18.495 2.177 11.77 14.227 22.764Sorgho 2.084 580 27.85 946 3.222

All other crops have been estimated, but with an insufficient reliability.


Estimates by rural community

Estimations are possible also at a greater level of disaggregation, but of course their uncertainty is much more high than the department level.In fact the number of observed points in each community is too low to ensure good precision.Anyway some indications could be taken from these data, if you are cautious in understanding their limitations

Estimates for crop: Arachide Name Surface StdDev CVKayemor 5.652 739 13.09Medina Sabakh 6.865 859 12.52Ngayene 5.211 897 17.21Gainte Kaye 8.655 771 8.92Paos Koto 14.515 1.222 8.42Prokhane 8.259 805 9.76Taiba Niassene 4.255 664 15.61Keur Maba Diakhou 9.541 899 9.43Keur Madongo 1.980 436 22.03Ndrame Escale 5.034 669 13.31Wack Ngouna 7.513 812 10.81


Comparison with other official estimates: the case of Senegal

Comparing these results with DAPSA estimates shows some important differences

The big difference in the total arable land surface, suggests that DAPSA method could have some underestimation in the expansion mechanism.Infact the total surface of Nioro du Rip is 225.000 hectares and landuse maps show that the potentially agricultural area is over 200.000 hectares

crop AGRICAB DAPSA Arachide 82.119 61.226Millet 59.311 45.235Maize 22.414 18.914

total arable land 188.784 127.748cv 1.27 5.76

In the DAPSA estimates the precision is only reported at the total level: the AGRICAB reliability at the same level is much better


Comparison with other official estimates: the case of Senegal

Precision at crop level in terms of variation coefficient is reported in a study of the University of Michigan concerning the 2010 data

University of Michigan 2010crop Surface StdDev CVArachide 67.922 4.757 7.00Millet 57.915 5.135 8.90

AGRICAB 2013crop Surface StdDev CVArachide 82.119 3.015 3.67Millet 59.311 2.744 4.63

The better performance achieved in AGRICAB is due to a higher number of points in the sample, and to the greater efficiency of the point frame


Using the classification of satellite images to get better estimates

Satellite (Rapid-Eye) images covering the whole area have been acquired and classified, in order to identify maize areas.

The image classification provides some important information that could be used to get better estimates of crop surface; in fact the availability of extra-information, known for the whole of the population and not only for the sample units, can be used to build a stratification of the population in homogeneous groups where the variance of the estimates could be significantly lower than in the population as a whole.

Unfortunately the classification results are not good enough to achieve this result, as expressed by this confusion matrix (this is for Kenya, similar results was obtained also in Senegal)

Maize Sugar cane other % correct

Maize 60 46 14 0.5

Sugar cane 46 58 7 0.52

Total 171 155 48

% correct 0.35 0.37

As a consequence, no gain in terms of reliability is added using this classification, and the estimates are not modified


Technical workshops

Kenya:1.Sampling frame production april 8-26, 20132.Preparation of field survey may 27 – june 14, 20133.Image classification november 18-29, 20134.Image classification february 15-22, 20145.Statistical analysis july 28 – august 1, 2014

Senegal:1.Sampling frame production may 27-31, 20132.Preparation of field survey july 23 – august 2, 20133.Image classification september 15-27, 20144.Statistical analysis october 13-17, 2014

Mozambique:1.Sampling frame production may 27-231, 20132.Preparation of field survey july 23 – august 2, 20133.Statistical analysis october 20-24, 2014

1 agricab final meeting antwerp, march 24, 2015 use case: agricultural statistics david remotti,...

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