Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions |
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| Affiliation: | 1. Department of Geography, Seoul National University, Shilim-Dong, Kwanak-Gu, Seoul 151-742, Korea;2. Department of Geography, Kyung Hee University, Dongdaemun-Gu, Seoul 130-701, Korea;3. Center for Development Research (ZEF), University of Bonn, Walter-Flex-Str. 3, 53113, Germany;1. ARVALIS – Institut du Végétal, Station Expérimentale, 91720 Boigneville, France;2. ARVALIS – Institut du Végétal, Chemin des Bissonnets, 14980 Rots, France;3. ARVALIS – Institut du Végétal, Station Expérimentale de La Jaillière, 44370 La Chapelle Saint Sauveur, France;1. University of Nebraska-Lincoln, PO Box 830915, Lincoln, NE 68583-0915, USA;2. Montana State University, Northwestern Agricultural Research Center, 4570 MT Hwy 35, Kalispell, MT 59901, USA;3. University of Nebraska Clay County Extension Office, 111 W. Fairfield, Clay Center, NE 68933-1499, USA;4. Little Blue Natural Resources District, 106 N Juniper Ave, Davenport, NE 68335, USA;1. Department of Earth, Ocean and Atmospheric Sciences, The University of British Columbia, 2207 Main Mall, Vancouver, BC V6T 1Z4, Canada;2. Pacific Climate Impacts Consortium, University House 1, PO Box 1700 Stn CSC, University of Victoria, Victoria, BC V8W 2Y2, Canada;3. Earth Observation, Agriculture and Agri-Food Canada, Science and Technology Branch, 960 Carling Avenue, Ottawa, ON K1A 0C6, Canada;4. Agriculture Division, Statistics Canada, 170 Tunney''s Pasture Driveway Jean-Talon Building, 12th floor D2 Ottawa, ON K1A 0T6, Canada |
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| Abstract: | Decision-making processes in agriculture often require reliable crop response models to assess the impact of specific land management. While process-based models are often preferred over empirical ones in current modelling communities, empirical crop growth models can play an important role in identifying the hidden structure of crop growth processes relating to a wide range of land management options. This study investigates the potential of predicting crop yield responses under varying soil and land management conditions by applying three different adaptive techniques: general linear models (GLMs), artificial neural networks (ANNs), and regression trees (RTs). The crop yield data used in this research consist of 720 maize yield indices from 11 different land management trials in southern Uganda. GLM showed the poorest results in terms of modelling accuracy, prediction accuracy, and model uncertainty, which might suggest its inability to model the non-linear causal relationships present in complex soil–land and crop-management interactions. The other two non-parametric adaptive models show significantly higher prediction accuracy than GLM. RT is the most robust technique for predicting crop yield at the study site. ANN is also a promising tool for predicting crop yield and offers insight into the causal relationships through the use of sensitivity analyses, but the complex parameterization and optimum model structure require further attention. The three adaptive techniques compared in this research showed different advantages and disadvantages. When these methods are used together, valuable information can be provided on crop responses, and more reliable crop growth models may result. |
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