Explaining rice yields and yield gaps in Central Luzon,Philippines: An application of stochastic frontier analysis and crop modelling |
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| Institution: | 1. Plant Production Systems, Wageningen UR, Wageningen, The Netherlands;2. Social Sciences Division, IRRI, Los Baños, Laguna, Philippines;1. National Key Laboratory of Crop Genetic Improvement, MOA Key Laboratory of Crop Ecophysiology and Farming System in the Middle Reaches of the Yangtze River, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan, Hubei 430070, China;2. International Rice Research Institute, DAPO BOX 7777, Metro Manila, Philippines;1. Plant Production Systems Group, Wageningen University, PO Box 430, NL-6700 AK Wageningen, The Netherlands;2. Department of Agronomy and Horticulture, University of Nebraska—Lincoln, PO Box 830915, Lincoln, NE 68583-0915, USA;3. International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), PO Box 39063, 00623 Nairobi, Kenya;4. Soil Geography and Landscape Group, Wageningen University, PO Box 47, NL-6700 AA Wageningen, The Netherlands;5. Alterra, Wageningen University and Research Centre, PO Box 47, NL-6700 AA Wageningen, The Netherlands;6. Africa Rice Center, 01 BP 2031 Cotonou, Benin;1. International Rice Research Institute, PO Box 7777, Metro Manila, Philippines;2. Rothamsted Research, Harpenden, United Kingdom;3. Philippine Rice Research Institute, Central Experiment Station, Muñoz, Maligaya, Nueva Ecija, Philippines;4. Institute for Sustainable Agro-ecosystem Services, The University of Tokyo, Japan;1. Instituto de Investigaciones en Ciencias Agrarias de Rosario - CONICET, Facultad de Ciencias Agrarias, Universidad Nacional de Rosario, S2125AA Zavalla, Santa Fe, Argentina;2. Estación Experimental Agropecuaria Oliveros, Instituto Nacional de Tecnología Agropecuaria, C2206 Oliveros, Santa Fe, Argentina;3. Asociación Argentina de Consorcios Regionales de Experimentación Agrícola, C1041AAZ Ciudad Autónoma de Buenos Aires, Argentina;1. Department of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE, USA;2. AgroParisTech, UMR Agronomie 211 INRA AgroParisTech Université Paris-Saclay, F-78850 Thiverval-Grignon, France;3. South Australian Research and Development Institute, Waite Campus, Adelaide 5001, Australia;4. Faculty of Veterinary and Agricultural Sciences, University of Melbourne, Victoria 3010, Australia |
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| Abstract: | Explaining yield gaps is crucial to understand the main technical constraints faced by farmers to increase land productivity. The objective of this study is to decompose the yield gap into efficiency, resource and technology yield gaps for irrigated lowland rice-based farming systems in Central Luzon, Philippines, and to explain those yield gaps using data related to crop management, biophysical constraints and available technologies.Stochastic frontier analysis was used to quantify and explain the efficiency and resource yield gaps and a crop growth model (ORYZA v3) was used to compute the technology yield gap. We combined these two methodologies into a theoretical framework to explain rice yield gaps in farmers’ fields included in the Central Luzon Loop Survey, an unbalanced panel dataset of about 100 households, collected every four to five years during the period 1966–2012.The mean yield gap estimated for the period 1979–2012 was 3.2 ton ha?1 in the wet season (WS) and 4.8 ton ha?1 in the dry season (DS). An average efficiency yield gap of 1.3 ton ha?1 was estimated and partly explained by untimely application of mineral fertilizers and biotic control factors. The mean resource yield gap was small in both seasons but somewhat larger in the DS (1.3 ton ha?1) than in the WS (1.0 ton ha?1). This can be partly explained by the greater N, P and K use in the highest yielding fields than in lowest yielding fields which was observed in the DS but not in the WS. The technology yield gap was on average less than 1.0 ton ha?1 during the WS prior to 2003 and ca. 1.6 ton ha?1 from 2003 to 2012 while in the DS it has been consistently large with a mean of 2.2 ton ha?1. Varietal shift and sub-optimal application of inputs (e.g. quantity of irrigation water and N) are the most plausible explanations for this yield gap during the WS and DS, respectively.We conclude that the technology yield gap explains nearly half of the difference between potential and actual yields while the efficiency and resource yield gaps explain each a quarter of that difference in the DS. As for the WS, particular attention should be given to the efficiency yield gap which, although decreasing with time, still accounted for nearly 40% of the overall yield gap. |
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| Keywords: | Rice Yield variability Yield gap Philippines Stochastic frontier analysis Crop modelling |
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