An interaction regression model for crop yield prediction

Statistical and machine learning models for location-specific crop yield prediction using weather indices

Crop yield prediction gains growing importance for all stakeholders in agriculture. Since the growth and development of crops are fully connected with many weather factors, it is inevitable to incorporate meteorological information into yield prediction mechanism. The changes in climate-yield relationship are more pronounced at a local level than across relatively large regions. Hence, district or sub-region-level modeling may be an appropriate approach. To obtain a location- and crop-specific model, different models with different functional forms have to be explored. This systematic review aims to discuss research papers related to statistical and machine-learning models commonly used to predict crop yield using weather factors. It was found that Artificial Neural Network (ANN) and Multiple Linear Regression were the most applied models. Support Vector Regression (SVR) model has a high success ratio as it performed well in most of the cases. The optimization options in ANN and SVR models allow us to tune models to specific patterns of association between weather conditions of a location and crop yield. ANN model can be trained using different activation functions with optimized learning rate and number of hidden layer neurons. Similarly, the SVR model can be trained with different kernel functions and various combinations of hyperparameters. Penalized regression models namely, LASSO and Elastic Net are better alternatives to simple linear regression. The nonlinear machine learning models namely, SVR and ANN were found to perform better in most of the cases which indicates there exists a nonlinear complex association between crop yield and weather factors.

Reviewing the essential roles of remote phenotyping, GWAS and explainable AI in practical marker-assisted selection for drought-tolerant winter wheat breeding

Marker-assisted selection (MAS) plays a crucial role in crop breeding improving the speed and precision of conventional breeding programmes by quickly and reliably identifying and selecting plants with desired traits. However, the efficacy of MAS depends on several prerequisites, with precise phenotyping being a key aspect of any plant breeding programme. Recent advancements in high-throughput remote phenotyping, facilitated by unmanned aerial vehicles coupled to machine learning, offer a non-destructive and efficient alternative to traditional, time-consuming, and labour-intensive methods. Furthermore, MAS relies on knowledge of marker-trait associations, commonly obtained through genome-wide association studies (GWAS), to understand complex traits such as drought tolerance, including yield components and phenology. However, GWAS has limitations that artificial intelligence (AI) has been shown to partially overcome. Additionally, AI and its explainable variants, which ensure transparency and interpretability, are increasingly being used as recognised problem-solving tools throughout the breeding process. Given these rapid technological advancements, this review provides an overview of state-of-the-art methods and processes underlying each MAS, from phenotyping, genotyping and association analyses to the integration of explainable AI along the entire workflow. In this context, we specifically address the challenges and importance of breeding winter wheat for greater drought tolerance with stable yields, as regional droughts during critical developmental stages pose a threat to winter wheat production. Finally, we explore the transition from scientific progress to practical implementation and discuss ways to bridge the gap between cutting-edge developments and breeders, expediting MAS-based winter wheat breeding for drought tolerance.

A data-driven crop model for maize yield prediction

Accurate estimation of crop yield predictions is of great importance for food security under the impact of climate change. We propose a data-driven crop model that combines the knowledge advantage of process-based modeling and the computational advantage of data-driven modeling. The proposed model tracks the daily biomass accumulation process during the maize growing season and uses daily produced biomass to estimate the final grain yield. Computational studies using crop yield, field location, genotype and corresponding environmental data were conducted in the US Corn Belt region from 1981 to 2020. The results suggest that the proposed model can achieve an accurate prediction performance with a 7.16% relative root-mean-square-error of average yield in 2020 and provide scientifically explainable results. The model also demonstrates its ability to detect and separate interactions between genotypic parameters and environmental variables. Additionally, this study demonstrates the potential value of the proposed model in helping farmers achieve higher yields by optimizing seed selection.

Spatial and temporal pattern of deficient Indian summer monsoon rainfall (ISMR): impact on Kharif (summer monsoon) food grain production in India

Despite a significant increasing trend in historical food grain production (FGP) in India, deficient Indian summer monsoon rainfall (ISMR) often causes a reduction in FGP. The present study was carried out to understand temporal and spatial variations in deficient rainfall (drought) and their impact on national and regional FGP of India. Long-term (1901-2020) percentage departure in rainfall and drought areas over the country showed nonsignificant and significant trends, respectively. Subdivisional rainfall showed significant decreasing and increasing trends in 4 and 5 subdivisions, respectively. Drought years of high frequency (once in 3-4 years) and 4 to 5 consecutive drought years (once in 120 years) occurred in northwest and western subdivisions of India. Departure in de-trended production of All India Kharif food grains from its normal (DDP) showed significant quadratic relationship with departure in ISMR from its normal (DRF). Besides the quadratic equation, another multiple regression model taking de-trended crop area, DRF, and drought area as predictor variables was developed for predicting DDP. Both these models, with high R² (0.8-0.88) between observed and predicted data and low RMSE (2.6-2.7%), can be employed for advanced estimation of DDP of the country and for taking country-level policy decisions by the Indian Government. For the first time, models were formulated to estimate state-wise departure in FGP (DP). In these models, novel indices viz., (i) rainfall departure and irrigation index (RDII) and (ii) physical and socio-economic index (PSEI), were used as predictor variables. These models, with R² (0.71-0.75) and RMSE of 11.8-14.2(< SD of observed data), hold promise for advance estimation of production loss in states, useful for regional-level planning by the Government of India, and testing them in other countries.