Abstract
AbstractIn recent years, planting machinery that enables precise control of the planting rates has become available for corn (Zea mays L.) and soybean (Glycine max L.). With increasingly available topographical and soil information, there is a growing interest in developing variable rate planting strategies to exploit variation in the agri‐landscape in order to maximize production. A random forest regression‐based approach was developed to model the interactions between planting rate, hybrid/variety, topography, soil characteristics, weather variables, and their effects on yield by leveraging on‐farm variable rate planting trials for corn and soybean conducted at 27 sites in New York between 2014 and 2018 (57 site‐years) in collaboration with the New York Corn and Soybean Growers Association. Planting rate ranked highly in terms of random forest regression variable importance while explaining relatively minimal yield variation in the linear context, indicating that yield response to planting rate likely depends on complex interactions with agri‐landscape features. Random forest models explained moderate levels of yield within site‐years, while the ability to predict yield in untested site‐years was low. Relatedly, variable importance measures for the predictors varied considerably across sites. Together, these results suggest that local testing may provide the most accurate optimized planting rate designs due to the unique set of conditions at each site. The proposed method was extended to identify the optimal variable rate planting design for maximizing yield at each site given the underlying conditions, and empirical validation of the resulting designs is currently underway.
Highlights
In agricultural production systems, site-specific management involves the development of crop management strategies at a finer spatial scale than that of the whole field area (Plant, 2001)
Corn yields were lowest and most variable during the 2015 growing season, which was characterized by extremely wet conditions during May and June followed by dry conditions from early July through mid-September (Fig. 2)
Random forest regression is among a suite of established and emerging techniques in machine learning that are becoming more widely used in agricultural research to model complex systems (Henderson et al, 2005; Häring et al, 2012; Xiong et al, 2014; Chlingaryan et al, 2018; Liakos et al, 2018)
Summary
Site-specific management involves the development of crop management strategies at a finer spatial scale than that of the whole field area (Plant, 2001). Compared to early farming, which was performed by hand and thereby facilitated site-specific management, the advent of mechanization in farming ushered in an era of uniform application of inputs on large areas of cropland. The growing costs of agricultural inputs (USDA NASS, 2017) and negative environmental impacts of intensive production practices (Tilman et al, 2002) threaten the long-term economic and environmental sustainability of uniform crop management. Variable rate application systems enable growers to apply inputs at a range of user-defined levels within a field area. Variable rate as it applies to planting is not a new concept. The first variable rate planting systems emerged in the United States during the
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