Yield forecast is essential to agriculture stakeholders and can be obtained with the use of machine learning models and data coming from multiple sources. Most solutions for yield forecast rely on NDVI (Normalized Difference Vegetation Index) data, which is time-consuming to be acquired and processed. To bring scalability for yield forecast, in the present paper we describe a system that incorporates satellite-derived precipitation and soil properties datasets, seasonal climate forecasting data from physical models and other sources to produce a pre-season prediction of soybean maize yield---with no need of NDVI data. This system provides significantly useful results by the exempting the need for high-resolution remote-sensing data and allowing farmers to prepare for adverse climate influence on the crop cycle. In our studies, we forecast the soybean and maize yields for Brazil and USA, which corresponded to 44 of the world's grain production in 2016. Results show the error metrics for soybean and maize yield forecasts are comparable to similar systems that only provide yield forecast information in the first weeks to months of the crop cycle.
In studying dryland maize in South Africa, Estes @cite_7 developed three empirical models that were compared against the CERES-maize model of the DSSAT platform @cite_19 . Two of the empirical models were based on maximum entropy (MAXENT) @cite_34 : one trained on all national crop distributions points and the other trained with the top producing localities. The third method used a generalized additive model (GAM) trained with yield data derived from NDVI @. GAM and CERES results showed linear correlation to measured yield (R @math = 0.75 and 0.37, respectively) as did the MAXENT model trained with high-productivity points (R @math = 0.62).
@cite_19 @cite_34 @cite_7
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Aim Intercomparison of mechanistic and empirical models is an important step towards improving projections of potential species distribution and abundance. We aim to compare suitability and productivity estimates for a well-understood crop species to evaluate the strengths and weaknesses of mechanistic versus empirical modelling. Location South Africa. Methods We compared four habitat suitability models for dryland maize based on climate and soil predictors. Two were created using maximum entropy (MAXENT), the first based on national crop distribution points and the second based only on locations with high productivity. The third approach used a generalized additive model (GAM) trained with continuous productivity data derived from the satellite normalized difference vegetation index (NDVI). The fourth model was a mechanistic crop growth model (DSSAT) made spatially explicit. We tested model accuracy by comparing the results with observed productivity derived from MODIS NDVI and with observed suitability based on the current spatial distribution of maize crop fields. Results The GAM and DSSAT results were linearly correlated to NDVI-measured yield (R 2 = 0.75 and 0.37, respectively). MAXENT suitability values were not lin An important issue for agricultural planning purposes is the accurate yield estimation for the numerous crops involved in the planning. Machine learning (ML) is an essential approach for achieving practical and effective solutions for this problem. Many comparisons of ML methods for yield prediction have been made, seeking for the most accurate technique. Generally, the number of evaluated crops and techniques is too low and does not provide enough information for agricultural planning purposes. This paper compares the predictive accuracy of ML and linear regression techniques for crop yield prediction in ten crop datasets. Multiple linear regression, M5-Prime regression trees, perceptron multilayer neural networks, support vector regression and k-nearest neighbor methods were ranked. Four accuracy metrics were used to validate the models: the root mean square error (RMS), root relative square error (RRSE), normalized mean absolute error (MAE), and correlation factor (R). Real data of an irrigation zone of Mexico were used for building the models. Models were tested with samples of two consecutive years. The results show that M5Prime and k-nearest neighbor techniques obtain the lowest average RMSE errors (5.14 and 4.91), the lowest RRSE errors (79.46 and 79.78 ), the lowest average MAE errors (18.12 and 19.42 ), and the highest average correlation factors (0.41 and 0.42). Since M5-Prime achieves the largest number of crop yield models with the lowest errors, it is a very suitable tool for massive crop yield prediction in agricultural planning. Additional key words: regression trees; neural networks; support vector regression; k-nearest neighbor; multiple linear regression. Abstract Ukraine is one of the most developed agriculture countries and one of the biggest crop producers in the world. Timely and accurate crop yield forecasts for Ukraine at regional level become a key element in providing support to policy makers in food security. In this paper, feasibility and relative efficiency of using moderate resolution satellite data to winter wheat forecasting in Ukraine at oblast level is assessed. Oblast is a sub-national administrative unit that corresponds to the NUTS2 level of the Nomenclature of Territorial Units for Statistics (NUTS) of the European Union. NDVI values were derived from the MODIS sensor at the 250 m spatial resolution. For each oblast NDVI values were averaged for a cropland map (Rainfed croplands class) derived from the ESA GlobCover map, and were used as predictors in the regression models. Using a leave-one-out cross-validation procedure, the best time for making reliable yield forecasts in terms of root mean square error was identified. For most oblasts, NDVI values taken in April–May provided the minimum RMSE value when comparing to the official statistics, thus enabling forecasts 2–3 months prior to harvest. The NDVI-based approach was compared to the following approaches: empirical model based on meteorological observations (with forecasts in April–May that provide minimum RMSE value) and WOFOST crop growth simulation model implemented in the CGMS system (with forecasts in June that provide minimum RMSE value). All three approaches were run to produce winter wheat yield forecasts for independent datasets for 2010 and 2011, i.e. on data that were not used within model calibration process. The most accurate predictions for 2010 were achieved using the CGMS system with the RMSE value of 0.3 t ha −1 in June and 0.4 t ha −1 in April, while performance of three approaches for 2011 was almost the same (0.5–0.6 t ha −1 in April). Both NDVI-based approach and CGMS system overestimated winter wheat yield comparing to official statistics in 2010, and underestimated it in 2011. Therefore, we can conclude that performance of empirical NDVI-based regression model was similar to meteorological and CGMS models when producing winter wheat yield forecasts at oblast level in Ukraine 2–3 months prior to harvest, while providing minimum requirements to input datasets. Monitoring crop condition and yields at regional scales using imagery from operational satellites remains a challenge because of the problem in scaling local yield simulations to the regional scales. NOAA AVHRR satellite imagery has been traditionally used to monitor vegetation changes that are used indirectly to assess crop condition and yields. Additionally, the 1-km spatial resolution of NOAA AVHRR is not adequate for monitoring crops at the field level. Imagery from the new MODIS sensor onboard the NASA Terra satellite offers an excellent opportunity for daily coverage at 250-m resolution, which is adequate to monitor field sizes are larger than 25 ha. A field study was conducted in the predominantly corn and soybean area of Iowa to evaluate the applicability of the 8-day MODIS composite imagery in operational assessment of crop condition and yields. Ground-based canopy reflectance and leaf area index (LAI) measurements were used to calibrate the models. The MODIS data was used in a radiative transfer model to estimate LAI through the season. LAI was integrated into a climate-based crop simulation model to scale from local simulation of crop development and responses to a regional scale. Simulations of corn and soybean yields at a 1.6×1.6-km2 grid scale were comparable to county yields reported by the USDA–National Agricultural Statistics Service (NASS). Weekly changes in soil moisture for the top 1-m profile were also simulated as part of the crop model as one of the critical parameters influencing crop condition and yields.