involves a system of hierarchical operations that are in association with the earth’s political, social, ecological, and economic components. There are numerous challenges that farmers and other stakeholders face in regards to agricultural production. In most of the cases, certain multiplicity problems can interfere with the sustainability of the production systems in agriculture. Nevertheless, adopting various policies and practices in agriculture can could help in the acceleration of the progress. Forecasting yield is one of the significant approaches that can enhance the crop production progress and enhance the sustainability of processes and practices adopted in farming.
Reasons why yield forecast is important
The evaluation of the various strategies and practices adopted in diverse land management practices is important in that it determines the sustainability of the agricultural production process. Effective implementation of soil and long-term crop management experiments which may focus on socio-economic indicators, biogeomechamical fluxes, soil properties, and crop yields, are important in the development and the testing of sustainability as well as yield forecast methods all over the world. Finding the best soil and crop simulation models are important in the provision of essential information on the farm management strategies that can be adopted for reaching the set goals in sustainable crop production. As such, the experimental results provided through forecasting are useful in effective decision making.
Usually, various environmental factors affect agricultural production. For example, weather influence crop development and growth are and hence, lead to variability in large intra-seasonal yield. Furthermore, spatial variability in soil properties also interact weather causing variability in spatial yield. Crop agronomic management such as tillage, irrigation, fertilizer applications and planting, are useful in offsetting the yield loss. Resultantly, yield forecasting is ideally a significant tool that can be used in the optimization of crop yield as well as in the evaluation of insurance contracts in crop area.
Different modern yield forecast methods
Forecasting has become an important agricultural practice aimed at increased production and reduced cost of operations. The reason for this is the fact that forecasting helps a farmer to use the right amount of inputs in the appropriate area based on available insights. Traditionally, there were various methods of forecasting yields that were used by as technology advanced, more sophisticated methods that enhance precision and accuracy are now used.
Here are some of the common modern yields forecast methods:
Yield forecast methods are of two types –qualitative and quantitative techniques. The difference between the quantitative and qualitative yield forecast methods is that the qualitative ones are quite subjective. The qualitative methods find much use in their earlier stages of the life cycle of the product since it is at this period when there is less past data that can be used for the quantitative methods.
- The Delphi technique
This is an example of qualitative yields forecast method that comprises of a group of experts that give the forecast. Based on the problem at hand, each of the expert gives out their opinion. All the suggestions are shared after which, each of the expert makes a subsequent forecast. It is a repetitive process that is based on the views of each expert to influence the next forecast until there is a common agreement.
- Nominal group technique
The nominal group technique shares a lot with the Delphi method as it uses the same approach of group participants, who are experts in a certain subject. The responses from the questions are ranked in order of their importance before they ranking is aggregated for the group to reach a consensus based on the priorities of the issues ranked.
- Market research
Consumers are asked questions whereby the responses are used in establishing the potential demand. A market research involves the construction of questionnaire to gather marketing, economic, demographic, and personal data.
Quantitative yields forecast methods are quite objective. These types of techniques can either be based on associative models or time-series forecasts. In the case of the time-series forecasts, the involved individual examines the underlying behaviors of the time-series data, as well as the causal factors of such behaviors for decision purposes on yields.
- The naïve forecast model
The naïve forecast model is the simplest time-series model. It uses the actual demand based on past data to establish the demand for the future. The primary assumption here is the fact that there will be a repeat of the past demand and conditions.
- Use of averaging
The averaging method is useful where past data is available. The information obtained from past periods is summed periodically and divided by the number of periods covered. The averages method is quite effective for forecast involving short periods. There are different variations of the averaging which include the weighted average, the moving average, and the weighted moving average.
- Econometric forecasting
There are econometric methods like autoregressive integrated moving-average model that embrace sophisticated mathematical approaches in establishing the relationship between past variables and demand. Usually, the forecaster creates an equation tests and tunes it to finer details to represent the relationships that happened in the past, reliably. Afterwards, various variables such as prices and income are replaced in the equation for forecasting purposes.
- Remote sensing
Framers can, as well use remote sensing as a yields forecast method. There are different types of data that one can obtain through remote sensing such as electromagnetic energy distributions, acoustic wave distribution, or even variations in weight distributions. Such data is used in forecasting the expected yields, as well as factors that may influence the yields.
Usually, any yield forecasts approaches are aimed the provision of precise and reliable information about early in advance especially during the growing season, to determine the yields. Usually, the main aspects of consideration are climate and weather. Since there are various methods of forecasting crop yields, it is important to take the time and research on the best method that can be applied in a given situation. Considering the fact that various methods of forecasting yields might have different results, it is advisable to consider combining several approaches.