Predicting stem borer density in maize using RapidEye data and generalized linear models |
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| Institution: | 1. African Insect Science for Food and Health (ICIPE), P.O. Box 30772, 00100, Nairobi, Kenya;2. Department of Agronomy, Faculty of Agriculture, University of Khartoum, Khartoum North 13314, Sudan;3. School of Biological Science, College of Physical and Biological Sciences, University of Nairobi, Nairobi 00100, Kenya;4. IRD/CRNS UMR IRD 247 EGCE, Laboratoire Evolution Génomes Comportement et Ecologie, CNRS, Gif sur Yvette Cedex, France;5. Unversité Paris—Sud 11, 91405 Orsay cedex, France;1. International Centre of Insect Physiology and Ecology (icipe), PO Box 30772-00100, Nairobi, Kenya;2. Unit for Environmental Sciences and Management, North-West University, Private Bag X6001, Potchefstroom 2520, South Africa;1. Kenyatta University, Chemistry Department, School of Pure and Applied Sciences, P.O. Box 43844-00100, Nairobi, Kenya;2. International Centre of Insect Physiology and Ecology (ICIPE), P.O. Box 30772-00100, Nairobi, Kenya;3. Department of Organic Chemistry, Faculty of Science, University of Yaoundé 1, P.O. Box 812 Yaoundé, Cameroon;4. Institute of Organic Chemistry, BMWZ, Leibniz University, Schneiderberg 38, 30167 Hannover, Germany;1. International Centre of Insect Physiology and Ecology, P.O. Box 30772-00100, Nairobi, Kenya;2. Department of Crop Protection, Faculty of Agricultural Sciences, University of Gezira, P.O. Box 20, Wad Medani, Sudan;3. Department of Horticulture, Jomo Kenyatta University of Agriculture and Technology, P.O. Box 62000-00200, Nairobi, Kenya;4. International Maize and Wheat Improvement Center (CIMMYT), ICRAF House, P.O. Box 1041, Nairobi, Kenya;5. CIRAD, UPR Bioagresseurs, P.O. Box 30677-00100, Nairobi, Kenya;6. Bioagresseurs, Univ Montpellier, CIRAD, Montpellier, France;1. Eco-environmental Protection Research Institute, Shanghai Academy of Agricultural Sciences, Shanghai Key Laboratory of Protected Horticultural Technology, 1000#, Jinqi Road, Fengxian District, Shanghai 201403, China;2. Ministry of Education Key Laboratory for Biodiversity Science and Ecological Engineering, Institute of Biodiversity Science, Fudan University, 2005#, Songhu Road, Shanghai 200438, China;3. Shanghai SIIC Modern Agriculture Development Company Limited, 188#, Tuanwang Middle Road, Dongwangsha, Chongming District, Shanghai 202183, China;4. Applied Agricultural Micro-organism Research, Jiangxi Academy of Agricultural Sciences, 3602#, Nanlian Road, Nanchang City, Jiangxi Province, Nanchang 330200, China;1. School of Biological Sciences, University of Canterbury, Private Bag 4800, Christchurch 8140, New Zealand;2. International Centre of Insect Physiology and Ecology, Thomas Odhiambo Campus, P.O. Box 30, Mbita Point, Kenya |
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| Abstract: | Average maize yield in eastern Africa is 2.03 t ha?1 as compared to global average of 6.06 t ha?1 due to biotic and abiotic constraints. Amongst the biotic production constraints in Africa, stem borers are the most injurious. In eastern Africa, maize yield losses due to stem borers are currently estimated between 12% and 21% of the total production. The objective of the present study was to explore the possibility of RapidEye spectral data to assess stem borer larva densities in maize fields in two study sites in Kenya. RapidEye images were acquired for the Bomet (western Kenya) test site on the 9th of December 2014 and on 27th of January 2015, and for Machakos (eastern Kenya) a RapidEye image was acquired on the 3rd of January 2015. Five RapidEye spectral bands as well as 30 spectral vegetation indices (SVIs) were utilized to predict per field maize stem borer larva densities using generalized linear models (GLMs), assuming Poisson (‘Po’) and negative binomial (‘NB’) distributions. Root mean square error (RMSE) and ratio prediction to deviation (RPD) statistics were used to assess the models performance using a leave-one-out cross-validation approach. The Zero-inflated NB (‘ZINB’) models outperformed the ‘NB’ models and stem borer larva densities could only be predicted during the mid growing season in December and early January in both study sites, respectively (RMSE = 0.69–1.06 and RPD = 8.25–19.57). Overall, all models performed similar when all the 30 SVIs (non-nested) and only the significant (nested) SVIs were used. The models developed could improve decision making regarding controlling maize stem borers within integrated pest management (IPM) interventions. |
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| Keywords: | RapidEye Maize Stem borer density Generalized linear models Africa Crop productivity |
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