Weather forecasting for agriculture

Weather forecasting is defined as prediction of the state of the atmosphere for a given location applying the principles of physics, supplemented by a variety of statistical and empirical techniques and by technology. In addition to predictions of atmospheric phenomena themselves, weather forecasting includes predictions of changes on Earth’s surface caused by atmospheric conditions (Cahir, 2013). Weather forecasts are important because they are issued to protect life and property, to save crops and to tell us what to expect in our atmospheric environment. Therefore, human beings have attempted to predict the weather informally for millennia and formally since the 19th century for a variety of public and private uses, and some authors believed that an economic value resides behind weather forecasts and tried to estimate it through different methods (Craft, 2010).

Public uses can range from severe weather alerts and advisories to protect lives and minimize losses, to air and marine traffics both very sensitive to the weather. Utility companies (electricity, gas) rely on weather forecasts to anticipate demand which can be strongly affected by the weather. Furthermore, weather forecasting is essential in forests’ management for preventing and controlling wildfires and for predicting conditions for the development of harmful insects. Weather forecasting has private uses as well. Increasingly, private companies pay for weather forecasts tailored to their needs so that they can increase their profits or avoid large losses. For example, supermarket chains may change the stocks on their shelves in different weather conditions.

Increasingly, private companies pay for weather forecasts tailored to their needs so that they can increase their profits or avoid large losses. For example, supermarket chains may change the stocks on their shelves in anticipation of different consumer spending habits in different weather conditions. Similar to the private sector, military weather forecaster’s present weather conditions to the war fighter community. For example, a mobile unit in the UK Royal Air Force (RAF), working with the UK Meteorological Office (Met Office), forecasts the weather for regions in which British and allied servicemen and women are deployed.

However, systematic weather records became available during the 17th century and were employed mainly in agriculture (Cahir, 2013) because farmers rely on weather forecasts to decide and plan farm activities. For instance, in the United States, national weather services provided by the Army Signal Corps beginning in 1870 were taken over by the Department of Agriculture in 1891 and by the early 1900s free mail service and telephone were providing forecasts daily to millions of American farmers, and by the 1920s radio broadcasts to agricultural interests were being made in most states.

Summary adaptation description

It is common practice in the UK, where crop economics, water resource and infra-structure allow, and where soil type may dictate, for water availability to a crop to be monitored and potentially supplemented through irrigation scheduling. This is most common in relation to potatoes and soft fruit (Suŝnik et al., 2006), and to a lesser extent to cereals and some salad crops (Wilks and Wolfe, 1998). Far less common, however, is the co-ordinated integration of weather forecasts into irrigation scheduling systems and decision-making.

Wider adoption of skilful weather forecasts, over time horizons of hours to months ahead, offers the potential for more efficient usage of scarce water resources in agricultural applications and also for economic benefit (Wilks and Wolfe, 1998; Freebairn and Zillman, 2002). The term skilful is taken to mean that the forecast method is more accurate than simply assuming future weather conditions will remain the same (persistence) or that weather conditions will simply be average for the time of day/year (climatology).

Modern-day skilful weather forecasts are based on the numerical weather prediction approach, involving computer-based modelling from a set of initial atmospheric and environmental conditions. These initial conditions are generally determined through an assimilation process which combines both real-time meteorological measurements (in-situ and remotely sensed) and a model “first guess” which is normally an earlier short-range forecast. Weather forecast accuracy can be very sensitive to the reality of the specified initial conditions, highlighting the importance of high quality observational data and its geographical coverage, both horizontally and vertically.

The predictability of weather conditions varies with time. That is to say that, in NW Europe for example, skilful weather forecasts at daily resolution are possible 10-14 days ahead, depending upon the type of atmospheric circulation regime. In other regimes, skill may actually be lost much earlier, in some instances only a few days ahead. For some applications, including irrigation scheduling, it isn’t always necessary to have daily forecast granularity. For example, having an indication of whether a week is likely to be wetter or drier than average, for each of the next four weeks, can also assist with irrigation decision-making, and skilful forecasting at this weekly timescale is sometimes demonstrated in so-called probability-based monthly forecasts in NW Europe (Inness and Dorling, 2013; Cai et al., 2011). Meanwhile seasonal forecasts, a few months ahead, are still in development in Europe and not at this stage widely adopted by agricultural users (although used in a limited way in energy and insurance sectors).

In the case of weather forecasts which relate to water usage in agriculture, forecasts of both precipitation amount (P) and evapotranspiration (E) are relevant, the latter being dependent upon forecasts of air temperature, humidity, surface solar radiation intensity and wind speed. The net figure (P-E) can naturally form the basis of decision-making with regard to irrigation scheduling and forecasts of cumulative (P-E) over a period of days or longer will normally represent the most relevant averaging period.

Evaporation is also dependent upon vegetation/crop type, and generic (P-E) forecast estimates can be made more locally relevant through incorporation of this information, preferably taking account of both crop type and its stage of development (which may be ahead of or behind schedule). In situations of limited supply of soil moisture, actual evapotranspiration may not in fact match potential evapotranspiration – forecasts of (P-E) may need to be adjusted to account for this and this may be handled through assessment of a so-called “soil moisture deficit”, either through on-farm soil moisture measurements or through computer modelling of soil water balance (Smith et al., 2006; Hough, 2003; Hough and Jones, 1997).

Current status and uptake

Most current operational irrigation scheduling systems comprise of in-field monitoring of soil moisture, coupled with a licensed software-driven alert system or advisory service provided by a consultant. Such systems are most commonly focused upon current/recent in-field conditions rather than forecast conditions. There are few integrated decision-support systems where a weather forecast is fully embedded in the irrigation scheduling system and a modest number of studies in the literature describing the potential benefits (Venäläinen et al., 2005; Cai et al., 2011). Rather, where attention is paid to forecast weather conditions, this is generally through either a separate online forecast link embedded within an irrigation scheduling software system

or, more informally, through ad-hoc grower monitoring of one or more (often free) independent channels of weather forecast information (e.g. TV, radio, website, mobile phone app etc.).

Significant water use efficiencies arising from weather forecast information are likely to require the widespread integration of skilful, fit for purpose forecasts into existing popular irrigation scheduling software systems and an increase in the trust placed by farmers in weather forecast information more generally (Hu et al., 2006; Wang and Cai, 2009). Communication of the user needs in agriculture to potential service providers still requires improvement (Sivakumar, 2006). The basic idea of weather prediction is to sample the state of the fluid at a given time and use the equations of fluid dynamics and thermodynamics to estimate the state of the fluid at some time in the future. Different techniques are used for this purpose such as:

Persistence

This is the simplest method of forecasting the weather. It relies upon today's conditions to forecast the conditions tomorrow. This method of forecasting can be useful in both short range forecasts and long range forecasts, but it strongly depends upon the presence of a stagnant weather pattern (Sauter, 2005).

Use of a barometer

Measurements of barometric pressure and the pressure tendency (the change of pressure over time) have been used in forecasting since the late 19th century. The larger the change in pressure, the larger the change in weather can be expected (The old farmer’s Almanac, 2013):

If the pressure drop is rapid, a low pressure system is approaching, and there is a greater

chance of rain;

Rapid pressure rises are associated with improving weather conditions.

Along with pressure tendency, the condition of the sky is a very important parameter used to forecast weather particularly in mountainous areas, and the use of the sky cover method to predict weather over the centuries has led to various weather lore. For instance, the prediction of rain in made through the observation of the thickening of cloud cover or the invasion of a higher cloud deck; or the high thin cirrostratus clouds at night can lead to halos around the moon, which indicates an approach of a warm front and its associated rain.

Use of forecast models

In the past the human forecaster was responsible for generating the entire forecast based on available observations; today model based forecast is taking place and the human input is generally confined to choosing a model based on various parameters, such as model biases and performance. Using a consensus of forecast models, as well as ensemble members of the various models, can help reduce forecast error. However, forecasters are required to interpret the model data into weather forecasts and compare the model predictions against actual observations. If necessary, they modify a forecast if it is going wrong (Bengtsson, undated).

Analog technique

The Analog is a complex technique of making a forecast, based on a previous weather event expected to be mimicked by an upcoming event. Its difficulty resides in the fact that there is rarely a perfect analog for an event in the future. Yet, it remains a useful method of observing rainfall and forecasting of future precipitation and distribution (Ben Daoud et al., 2011).

Satellites and weather forecasting

Satellites have been used for weather observations since 1959 when “Vanguard 2” was launched. They are use link to list some of the things that are observed from space. There are

two types of satellite orbit; polar orbiting and geostationary and both are useful for meteorology. Data from weather satellites are usually used where traditional data sources are missing. Satellite data has the advantage of global coverage compared with similar data from other techniques, still with lower accuracy and resolution.

Suitability and target use

There are benefits available across agriculture from the use of weather forecast services. While irrigation-fed farming is perhaps one of the most obvious, forecast weather conditions can also provide advanced indication of the output of on-farm renewable energy systems (e.g. wind, solar, and anaerobic digestion systems) and can also support the optimum scheduling of a wide range of on-farm activities in order to avoid wasteful use of resources and to protect the environment (for example the management of slurry and the application of crop protection products). Weather forecasts can also support the safe handling of farm animals, for example the management of temperature and humidity in chicken sheds, and the optimum storage conditions for crops. Good and reliable forecasts are also very important in the management of extreme events and their prevention on agricultural practices (e.g. extreme events such as frost or hail could have strong and costly effects on yield if not predicted and prevented).

In the UK, there are instances where forecasts 3-4 weeks ahead can provide reliable advice. Although weather forecast models now operate at higher spatial resolution than ever, biases between the output of a model and the local conditions actually experienced on-farm (due for example to very local topographic effects) can mean that it is worthwhile to “post-process” the raw forecasts in order to more faithfully reflect the actual farm climate. Such post-processing is ideally supported by the availability of on-farm weather station measurements to calibrate this adjustment process. There is significant opportunity for greater sharing of data between growers and forecast providers, particularly with respect to on-farm soil moisture measurements which are few and far between in official meteorological monitoring networks.

The WMO (2010) classified weather forecasting in different groups, however, the shorter the range the highest the predictability and the more important and suitable is the forecast for agriculture. The groups are as follows:

Now-casting (NC) A description of current weather variables and description of forecast weather variables for 0-2 hours. A relatively complete set of variables can be produced (air temperature and relative humidity, wind speed and direction, solar radiation, precipitation amount and type, cloud amount and type, and the like).

Very Short Range Forecast (VSRF) Description of weather variables for up to 12 hours. A relatively complete set of variables can be produced (same as in nowcasting).

Short Range Forecast (SRF) Description of weather variables for more than 12 hours and up to 72 hours. A relatively complete set of variables can be produced (same as in nowcasting).

Medium Range Forecast (MRF) A relatively complete set of variables can be produced (same as in nowcasting).

Long Range Forecast (LRF) From 12-30 days up to two years. Forecast is usually restricted to some fundamental variables (temperature and precipitation).

From another side, the elements of agricultural weather forecasts vary from place to place and from season to season, but they should refer to all weather elements, which affect farm planning and/or operations, and they ideally would include (WMO, 2010):

Sky coverage by clouds

Precipitation

Temperature (maximum, minimum and dew point)

Relative humidity

Wind Speed and direction

Extreme events (heat and cold waves fog, frost, hail, thunderstorms, wind squalls and gales,

low pressure areas, different intensities of depressions, cyclones, tornados, … )

Bright hours of sunshine

Solar radiation

Dew

Leaf wetness

Pan evaporation

Soil moisture stress conditions and supplementary irrigation for rainfed crops

Advice for irrigation timing and quantity in terms of pan evaporation

Specific information about the evolution of meteorological variables into the canopy layer

in some specific cases

Micro-climate inside crops in specific cases.

Investment cost

Free or at least very cheap weather forecast services are available via many communication channels (for example, TV, radio, websites, mobile phone apps etc). However these information feeds may conflict with each other with regard to the advice provided, may not look far enough into the future from a user perspective to aid decision-making or may not provide sufficient detail such as information concerning evapotranspiration.

More suitable weather forecast feeds are therefore likely to be in the form of an annual subscription to an e-mail or web-based information service (typical annual cost <£500/year) or a “talk-to-a-forecaster” telephone service on a premium-rate basis (£1.50 per minute plus network extras), the latter having the advantage of being charged through the ordinary telephone billing system. Weather forecasts which are fully integrated into irrigation-scheduling DSSs would be reflected by a modest increase in the annual license/subscription associated with such software/services.

It should be noted that “free” weather forecast services are also made available by some agricultural companies to their clients as a form of “bundled benefit”.

Design and management issues

The numerical weather prediction models (NWP) has made big improvement in the development of weather forecast reliability and accuracy. Yet predictability is currently limited to about 3 weeks for temperature and 2 weeks for precipitation and solar radiation, while precipitation forecasts over a month are encouraging (Calanca et al., 2011; WMO, 2010). Therefore, most research in meteorology is devoted to the improvement of such models and other empirical models and methods derived from experience (rules of thumb). The development of new powerful tools and models is constantly evolving in a way that humans may no longer be needed in the forecast process at some point in the future (Met Office, 2009). But this will involve additional efforts and a lot of investments in research to reach greater technological performances (faster super computers and greater coverage and the targeting of observations, monitoring tools that use satellites, networks of automatic weather stations, radars, and lightning detection systems).

“Nowcasting” is a very old technique for very short-range forecasting that dates back in the UK to the 1860s, but since the 1980s it is gaining in accuracy and reliability with the development of the NWP models and is expected to improve with time to deliver high reliable data for end users (Met Office, 2013; Wilson, 2006).

In the UK, the Met Office has been at the forefront of global weather and climate science for 150 years. It is in a continuous challenge to combine the latest science with ground breaking advances in technology and local understanding to deliver operational advantage. The “Weather Observations Website” (WOW) reflects one of his recent advances in technology and how weather observations can be made. The office is taking advantage of the growing world of social networking to coordinate a national observation platform and to get everyone involved and share the weather observations.

The latest innovation comes from the US with a smartphone application (mostly for Samsung that comes equipped with digital barometers), where a Canadian company with help from atmospheric scientists from the University of Washington have created an interface between the device and researchers to collect readings per hour from the app’s users to make it available to the national weather services and meteorologists (Greenemeier, 2013).

The proliferation of seasonally unusual weather conditions in recent years, compared to long-term averages, has presented UK growers with some of the most challenging farming conditions in living memory, largely negating recent advances provided by precision agriculture techniques. While there is very limited scope to change the weather conditions experienced on farm (save for limited protection from wind and frost risk for example), the wise use of a trustworthy forecast service provides the potential to reduce risks and optimize farm management and particularly for adaptation strategies of climate variability (Miller and Migliaccio, 2008; Weare, 1990). Furthermore, attention to reliable forecasts in other major growing regions around the world may, in today’s world markets, support growers in their decision-making when selling crops ‘ahead’.

Other issues (e.g. environmental, legal)

By routinely incorporating weather forecast advice into on-farm job scheduling, it is possible to reduce the chances of negatively impacting the local environment (such as may occur through the leaching process) and the threat of legal action (which can arise, for example, following the inappropriate scheduling of crop spraying when winds are too high). There is scope for water companies to further encourage farmers to utilise reliable weather forecast services in pursuit of more pro-active and cost-effective protection of watercourses in sensitive catchments.

Documented case studies

In general, government agencies provide forecasts to the public, and traditionally, newspaper, television, and radio have been the primary outlets for presenting them. Increasingly, the internet is being used due to the vast amount of specific information that can be found.

In the USA the California Farm Bureau Federation (CFBF) is a non-governmental, non-profit, voluntary membership whose purpose is to protect and promote agricultural interests throughout the state of California. It made available on the website the “California Farm Weather” providing reports for current conditions and five-day forecasts by location, plus temperature, humidity and wind maps all updated hourly, plus maps charting severe-weather warnings, soil moisture, drought index and snow cover. A summary of US weather conditions is updated twice daily and commodity highlights are posted each weekday (http://www.cfbf.com/weather/index.cfm). “California Farm Weather” delivers also radar and satellite maps updated every 30 to 60 minutes for each state and region; as well as degree day maps offering information of interest for main California production. Adhesion to the CFBF depends whether it is an agricultural membership (varies by county), an associate membership (US$ 72 per year), a community business (varies by county) or a collegiate member (US$ 25 per year).

In response to demand from the agricultural community, the Florida Automated Weather Network (FAWN) initiated after the National Weather Service discontinued their support for agricultural weather services in 1996, supported of various state agricultural organizations and industry associations (Miller and Migliaccio, 2008).

From another side, Farmers’ Almanac weather in the USA is the most trusted source for all things weather-related for nearly 200 years, offers weekly and long range weather forecasts for farmers, with now smartphone applications (http://www.farmersalmanac.com/weather/).

The weather channel dedicated a section for farmers (Farmers’ Forecast) providing agricultural forecasts, growing degree days calculator, seasonal outlooks and maps; also upon subscription an email and text alert service including soil moisture conditions, precipitation reports, wind speed direction (http://www.weather.com/outdoors/agriculture/forecast/).

Within the Managing Climate Variability (MCV) program, the Australian Bureau of Meteorology (BoM) has developed a new forecast system called POAMA switching from statistical to dynamical, or physics-based, forecasting the latest in improved weather-forecasting technologies, to increase the reliability of seasonal forecasts for growers to help with climate risk management. The program made climate data available for free through different public and private websites (www.bom.gov.au/watl; www.climatekelpie.com.au), and accessed as an iPhone app as well (GRDC, 2013).

In the UK many farm magazines dedicate now a space for weather forecasts which are also available online and in some cases smartphone applications. Examples are: Farmers’ weekly interactive magazine (www.fwi.co.uk), Farming online (http://www.farming.co.uk/weather/), AGWEB powered by farm journal (http://www.agweb.com/), and Farmers’ guide magazine (http://www.farmersguide.co.uk/index.aspx).

Additionally, the UK’s top agriculture, food and farming resource “UK Agriculture (http://www.ukagriculture.com/index.cfm) has a weather section on the website that provides live forecasts and predictions from Met Office and makes available other weather sources links (BBC weather, XC weather).

Moreover, “FarmersWeather” is a premium rate telephone service (£1.53 per minute) provided and owned by Farmers Guardian Ltd. It also get each daily forecast online on the web for 12:30 p.m. in some cases, when the forecast is particularly important and it may affect agricultural productivity. It provides weather update videos (http://www.farmersweather.co.uk/).

At the research level, using a lettuce growth model, Wilks and Wolfe (1998) estimated that optimal use of weather forecasts to schedule irrigations provides additional value of approximately US$1000 per hectare per year, some of which arises from the avoidance of crop damage due to excessive soil moisture.

Suŝnik et al. (2006) describe an operational forecast service in Slovenia in which both measurement and forecasting are used to power an irrigation decision system, for example applied to peach orchards. They suggested that savings of 20% in irrigation water usage could be achieved through the use of accurate and timely irrigation forecasts during vegetation periods.

Using a modelling approach, Wang and Cai (2009) quantify the positive impacts of incorporating 7-day weather forecast information into a corn irrigation scheduling system. When just using soil moisture monitoring and a SWAP model, 16% economic gains could be achieved, while incorporation of the 7-day forecast raised this to 21%. By using “perfect forecasts” in the form of actual weather measurement data, savings of 42-48% could be gained.

In practice, weather forecasts are quite widely used in agriculture to identify crop protection “spraying windows” and, for example, to identify a 5-day window of dry conditions suitable for hay making. The timing of fertiliser application is also carefully managed by some, for maximum effect, according to forecast temperature and rainfall information, especially in the current era

of high fertiliser prices. In periods of “catchy weather” especially, advanced warning of suitable conditions for the drilling and establishment of crops is also particularly valuable. By contrast, routinely utilising weather forecasts to help manage scarce water resources seems to be an under-developed application area.

Promotional needs for wider uptake

Many countries in Europe with USA and Australia have achieved major advances in weather forecasts and many other countries like Argentina, Brazil, Ethiopia and South Africa have on-going programmes supporting the use of forecasts by agricultural decision-makers. Nevertheless, for climate information and forecasts to be used effectively and to their optimal potential, four general requirements are identified (WMO, 2010):

Stakeholders must be able to obtain information (from forecasts or existing information) on factors or variables of direct interest to them and at lead times that allow for planning;

Paths to decisions, using this information, must be clear and practical; Stakeholders must be able to critically question the provided information to assess

appropriateness, and; Stakeholders must be convinced that such information, when used effectively, will indeed

make them better off than before.

Therefore, any promotional campaign or extension activity should be anticipated by the improvement of the NWP models to increase their reliability and accuracy to be appropriate for farmers to plan their activities. Further effort is required to demonstrate to growers the present-day reliability of state-of-the-art weather forecasting, especially with respect to forecast time horizons and to the adoption of probability forecast formats. Forecasting organisations and irrigation scheduling tool developers need to work together to integrate these components into one-stop-shop user-friendly systems.

Case studies of the economic benefits gained by growers who have fully integrated the leading forecast services into their operations would be very helpful. These could range from simple one-off savings (for example of a tank of spray not wasted as a result of a warning of a heavy shower risk) to the longer term savings derived, for example, from a water company not needing to retrospectively treat a drinking water resource which has been polluted with pesticide. Indeed, the effective uptake of weather forecast services reduces the risk of crop protection products being removed entirely from approved lists. A natural lead for the production of such case study evidence needs to be identified.

Acknowledgement

This brief was kindly produced by Dr Steve Dorling, Weatherquest (Norwich).

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