Crop yield response functions in nutrient application: A review

Abstract

The question of the optimal nutrient application in agricultural production continues to gather attention because of its relevance in increasing the profitability of crop production and protecting the environment. Economically optimal nutrient application decision rules are commonly derived by fitting crop yield functions to nutrients. The choice of the appropriate yield functional form thus is critical for estimating unbiased and more precise nutrient recommendations. This review explores recent developments in yield response functions to the nutrient application. We first introduce the concept of the economic theory of production and yield response functions. We then explain, with mathematical equations, some commonly used functional forms: quadratic response, Mitscherlich-Baule, deterministic, and stochastic plateau functions. We also summarize the strengths and weaknesses of each functional form and its implications for nutrient management strategies from an economic perspective. In recent studies, researchers have suggested that stochastic plateau functional forms, both linear and quadratic, could explain yield response to the nutrient application in a better way. Finally, we recommend fitting stochastic plateau functions in conjunction with carryover information in a dynamic optimization setting may yield more accurate optimal nutrient levels. Future research should continue to investigate whether these functional forms perform better in different crop and soil environments.

Synergizing Microbial Enriched Municipal Solid Waste Compost and Mineral Gypsum for Optimizing Rice-Wheat Productivity in Sodic Soils

“…A similar trend was recorded in the case of wheat straw. The soil nitrogen level influencing its uptake is a function of several factors [43,44]. The phosphorus concentration in both grain and straw was higher in treatment T 6 where combined use of organic and inorganic amendments was applied but the difference between T 5 and T 6 was not statistically significant.…”

Investigating Contribution Factors of Grain Input to Output Transformation for the Inner Mongolia Autonomous Region in China

“…Agriculture inputs (fertilizer, pesticide, agriculture films, machinery inputs, and irrigation) [10] and the adverse impact of natural hazards [12] impact yield. Production functions such as stochastic plateau functions, quadratic response, Mitscherlich-Baule, and deterministic methods can used to fit crop yield to agricultural inputs, for example, nutrients for crops in a specific geographic region, and help to estimate the optimal inputs to ensure food security [14]. Most studies have focused on the effect of typical drivers, such as nitrogen [60,61] and potassium [62], on yield from an agronomy point-of-view.…”

“…Controllable factors, including agricultural inputs (fertilizer, pesticide, agricultural film, machinery, and irrigation), non-controllable meteorological factors [10], and agrometeorological hazards [11][12][13][14] have also had significant influence on yield. Using Cobb-Douglas production function [15], which is used to present the relationships between production, capital, and labor, Yang et al [10] demonstrated that a 1% increase in fertilizer, irrigation, and precipitation can lead to 0.39%, 0.04%, and 0.21% increases in maize yield, respectively, in the ten main maize-producing provinces of China.…”

Spectral Soil Analysis for Fertilizer Recommendations by Coupling with Quefts for Maize in East Africa: A Sensitivity Analysis

“…Consequently, soil testing has been widely accepted and used as a tool for rational fertilizer use, helping to achieve optimal yields with high nutrient use efficiency and minimized losses to the environment. Moreover, soil-test and crop response based empirical models such as the QUEFTS model (Janssen et al, 1990;Haefele and Wopereis, 2005;Sattari et al, 2014;Yang et al, 2017) or yield response functions (Dhakal and Lange, 2021) have been developed for many cropping systems, using general soil characteristics, nutrient availability tests and/or observed crop responses to applied nutrients as input parameters. These approaches progressed overtime to assess the soil fertility status and recommend suitable and economic nutrient rates through inorganic fertilizer and organic manure for different crops and cropping systems.…”