Abstract
Seasonal forecasts of crop yield are important components for agricultural policy decisions and farmer planning. A wide range of input data are often needed to forecast crop yield in a region where sophisticated approaches such as machine learning and process-based models are used. This requires considerable effort for data preparation in addition to identifying data sources. Here, we propose a simpler approach called the Analogy Based Crop-yield (ABC) forecast scheme to make timely and accurate prediction of regional crop yield using a minimum set of inputs. In the ABC method, a growing season from a prior long-term period, e.g., 10 years, is first identified as analogous to the current season by the use of a similarity index based on the time series leaf area index (LAI) patterns. Crop yield in the given growing season is then forecasted using the weighted yield average reported in the analogous seasons for the area of interest. The ABC approach was used to predict corn and soybean yields in the Midwestern U.S. at the county level for the period of 2017–2019. The MOD15A2H, which is a satellite data product for LAI, was used to compile inputs. The mean absolute percentage error (MAPE) of crop yield forecasts was <10% for corn and soybean in each growing season when the time series of LAI from the day of year 89 to 209 was used as inputs to the ABC approach. The prediction error for the ABC approach was comparable to results from a deep neural network model that relied on soil and weather data as well as satellite data in a previous study. These results indicate that the ABC approach allowed for crop yield forecast with a lead-time of at least two months before harvest. In particular, the ABC scheme would be useful for regions where crop yield forecasts are limited by availability of reliable environmental data.
Highlights
Within season, crop yield forecasts are valuable for supporting stakeholder decision making including individual farmers, governments and non-profit international organizations [1,2,3]
This analogy-based cropyield (ABC) prediction approach depends on the assumption that the knowledge of crop growth patterns in the past would allow for accurate prediction of crop yield
In the Analogy Based Crop-yield (ABC) scheme, the temporal yield trend was determined at the regional scale, which represented advances in agricultural technology
Summary
Crop yield forecasts are valuable for supporting stakeholder decision making including individual farmers, governments and non-profit international organizations [1,2,3]. The crop yield forecasts are typically based on field surveys and measurements. The National Agricultural Statistics Service (NASS) of the United States Department of Agriculture (USDA) provides corn and soybean yield forecasts starting from. August using data obtained from Objective Yield Surveys [4]. This approach requires considerable effort and resources especially for large geographic areas, accurate predictions of crop yield using this method can be made [5,6]. Satellite data have been used to predict crop yield over large areas using fewer resources compared with the field surveys [7,8].
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