Yield loss prediction models based on early estimation of weed pressure |
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| Institution: | 1. Australian Herbicide Resistance Initiative, School of Plant Biology, University of Western Australia, 35 Stirling Hwy, WA 6009, Australia;2. IFEVA-CONICET, Facultad de Agronomía, Universidad de Buenos Aires (UBA), Argentina;1. College of Engineering, China Agricultural University, Beijing, China;2. Australian Centre for Field Robotics, The Rose Street Building J04, The University of Sydney, NSW 2006, Australia;1. School of Plant Biology and Institute of Agriculture, The University of Western Australia, Crawley, WA 6009, Australia;2. Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Toowoomba, Queensland 4350, Australia;1. ICRISAT Development Center (IDC) & International Rice Research Institute (IRRI), International Crops Research Institute for Semi Arid Tropics, Building # 303, ICRISAT, Patancheru 502324, Hyderabad, India;2. International Maize and Wheat Improvement Center (CIMMYT), Texcoco de Mora, Mexico;3. The University of Queensland, Queensland Alliance for Agriculture and Food Innovation, Room # 130, Building 8103, Post Box 1218, Gatton 4343, Queensland, Australia;4. Asia Program, International Crops Research Institute for Semi Arid Tropics (ICRISAT), Patancheru 502324, Hyderabad, India |
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| Abstract: | Weed control thresholds have been used to reduce costs and avoid unacceptable yield loss. Estimation of weed infestation has often been based on counts of weed plants per unit area or measurement of their relative leaf area index. Various linear, hyperbolic, and sigmoidal regression models have been proposed to predict yield loss, relative to yield in weed free environment from early measurements of weed infestation. The models are integrated in some weed management advisory systems. Generally, the recommendations from the advisory systems are applied to the whole field, but weed control thresholds are more relevant for site-specific weed management, because weeds are unevenly distributed in fields. Precision of prediction of yield loss is influenced by various factors such as locations, yield potential at the site, variation in competitive ability of mix stands of weed species and emergence time of weeds relative to crop. The aim of the review is to analyze various approaches to estimate infestation of weeds and the literature about yield loss prediction for multispecies. We discuss limitations of regression models and possible modifications to include the influential factors related to locations and species composition in context of their implementation in real time patch spraying. |
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| Keywords: | Weed management Relative leaf area models Weed patches Weed infestation |
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