Design of on‐farm precision experiments to estimate site‐specific crop responses

• Published in: Agronomy journal Vol. 113; no. 2; pp. 1366 - 1380
• Main Authors: Alesso, Carlos Agustín, Cipriotti, Pablo Ariel, Bollero, Germán Alberto, Martin, Nicolas Federico
• Format: Journal Article
• Language: English
• Published: 01-03-2021
• ISSN: 0002-1962; 1435-0645
• DOI: 10.1002/agj2.20572

Site‐specific prescriptions require estimating response functions to controllable inputs across the field. The methodology of applying geographically weighted regression to on‐farm precision experimentation studies opens new opportunities to study site‐specific responses to inputs in farmers' fields by locally estimating the regression coefficients. However, the effect of the experiment's spatial layout, such as plot dimensions and randomization, and spatial structure of the yield response on the experiment performance are yet to be studied. Detailed information about these effects is needed to improve trial design to detect site‐specific responses. A simulation study was conducted using 14,400 fields of 37 ha and 9‐m resolution. Coefficients from a spatial variable response function were drawn from five random fields generated by unconditional Gaussian geostatistical simulations. Four levels of nitrogen were assigned to plots using 18 systematic and randomized chessboard designs with different plot sizes. Simulated yield data was obtained by combining the coefficients, treatment, and random error. The effect of spatial structure and the designs was assessed with measures of agreement between the true and estimated maps of regression coefficients. The ability to capture or approximate the true spatial pattern of the response function increased as the underlying response function's spatial structure increases. Overall differences in performance between design were observed across the spatial structure tested, mostly related to randomization and plot dimensions. In general best results were achieved by systematic designs with small or intermediate plot sizes (r = 0.54 ± 0.05, MAE = 0.005 ± 0.0005, SDR = 0.81 ± 0.06, and CP = 0.50 ± 0.04). Our methodology provides a path for testing designs under different spatial variability scenarios.