A modeling approach to quantify the effects of spatial soybean yield limiting factors

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Paz, J. O.
Batchelor, W. D.
Tylka, G. L.
Hartzler, R. G.
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Tylka, Gregory
Morrill Professor
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Plant Pathology and Microbiology
The Department of Plant Pathology and Microbiology and the Department of Entomology officially merged as of September 1, 2022. The new department is known as the Department of Plant Pathology, Entomology, and Microbiology (PPEM). The overall mission of the Department is to benefit society through research, teaching, and extension activities that improve pest management and prevent disease. Collectively, the Department consists of about 100 faculty, staff, and students who are engaged in research, teaching, and extension activities that are central to the mission of the College of Agriculture and Life Sciences. The Department possesses state-of-the-art research and teaching facilities in the Advanced Research and Teaching Building and in Science II. In addition, research and extension activities are performed off-campus at the Field Extension Education Laboratory, the Horticulture Station, the Agriculture Engineering/Agronomy Farm, and several Research and Demonstration Farms located around the state. Furthermore, the Department houses the Plant and Insect Diagnostic Clinic, the Iowa Soybean Research Center, the Insect Zoo, and BugGuide. Several USDA-ARS scientists are also affiliated with the Department.
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Plant Pathology and MicrobiologyAgronomyAgricultural and Biosystems Engineering

Spatial yield variability is a complex interaction of many factors, including soil properties, weather, pests, fertility, and management. Crop models are excellent tools to evaluate these complex interactions and provide insight into causes of spatial yield variability. The goal of this study was to use a soybean crop growth model to determine the contribution of three factors that cause spatial yield variability and to test several calibration and validation strategies for yield prediction. A procedure was developed to calibrate the CROPGRO–Soybean model and to compare predicted and measured soybean yields, assuming that water stress, soybean cyst nematodes (SCN), and weeds were the dominant yield–limiting factors. The procedure involved calibrating drainage properties and rooting depth over three seasons for each grid. These procedures were tested on 77 grids (0.2 ha in size) in the McGarvey field in Perry, Iowa, for 1995, 1997, and 1999. Predicted soybean yields were in good agreement (r2 = 0.80) with measured yield after calibrating three model parameters. The calibrated model was used to quantify the effects of three yield–limiting factors on soybean. The maximum soybean yield potential in 1997 was estimated by running the calibrated model with no water, SCN, or weed stress. The model was then run for 1997, turning each yield–limiting factor off to assess its relative impact on yield reduction. Average estimated yield loss due to the combined effects of water stress, SCN, and weeds in each grid was 842 kg ha–1. Soybean yields were significantly reduced by an average of 626 kg ha–1 as a result of water stress. The presence of SCN in several grids accounted for an average yield reduction of 105 kg ha–1. The effects of weeds on soybean yield were not significant.


This article is published as Paz, J.O., W.D. Batchelor, G.L. Tylka, and R.G. Hartzler. 2001. A modeling approach to quantify the effects of spatial soybean yield limiting factors. Transactions of the ASAE 44:1329-1334, doi: 10.13031/2013.6423. Posted with permission.

Mon Jan 01 00:00:00 UTC 2001