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Quantifying production losses due to drought and submergence of rainfed rice at the household level using remotely sensed MODIS data

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  • Mottaleb, Khondoker Abdul
  • Gumma, Murali K.
  • Mishra, Ashok K.
  • Mohanty, Samarendu

Abstract

Combining remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) data with Bangladesh Household Income and Expenditure Survey (HIES) data, this study estimates losses in rainfed rice production at the household level. In particular, we estimated the rice areas affected by drought and submergence from remotely sensed MODIS data and rice production from Household Income and Expenditure Survey (HIES) data for 2000, 2005 and 2010. Applying two limit Tobit estimation method, this study demonstrated that both drought and submergence significantly affected rice production. Findings reveal that on average, a one percent increase in drought affected area at district level reduces Aman season rice production by approximately 1382 kilograms per household on average, annually. Similarly, a one percent increase in drought area reduces rainfed Aus season rice production by approximately 693 kilograms per household, on average, annually. Based on the findings the paper suggests disseminating and developing drought and submergence tolerant rice and also short duration rice varieties to minimize loss caused by drought and submergence in Aus and Aman rice seasons.

Suggested Citation

  • Mottaleb, Khondoker Abdul & Gumma, Murali K. & Mishra, Ashok K. & Mohanty, Samarendu, 2015. "Quantifying production losses due to drought and submergence of rainfed rice at the household level using remotely sensed MODIS data," Agricultural Systems, Elsevier, vol. 137(C), pages 227-235.
  • Handle: RePEc:eee:agisys:v:137:y:2015:i:c:p:227-235
    DOI: 10.1016/j.agsy.2014.08.014
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    References listed on IDEAS

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    Cited by:

    1. Zeenatul Islam & Mohammad Alauddin & Md. Abdur Rashid Sarker, 2017. "Farmers’ perception on climate change-driven rice production loss in drought-prone and groundwater-depleted areas of Bangladesh: An ordered probit analysis," Discussion Papers Series 579, School of Economics, University of Queensland, Australia.
    2. Feng, Puyu & Wang, Bin & Liu, De Li & Yu, Qiang, 2019. "Machine learning-based integration of remotely-sensed drought factors can improve the estimation of agricultural drought in South-Eastern Australia," Agricultural Systems, Elsevier, vol. 173(C), pages 303-316.
    3. Anu Susan Sam & Azhar Abbas & Subash Surendran Padmaja & Harald Kaechele & Ranjit Kumar & Klaus Müller, 2019. "Linking Food Security with Household’s Adaptive Capacity and Drought Risk: Implications for Sustainable Rural Development," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 142(1), pages 363-385, February.

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    More about this item

    Keywords

    Rainfed rice; Farm household; Drought; Submergence; Remote Sensing; MODIS data;
    All these keywords.

    JEL classification:

    • J24 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Human Capital; Skills; Occupational Choice; Labor Productivity
    • Q12 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Micro Analysis of Farm Firms, Farm Households, and Farm Input Markets
    • Q57 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Ecological Economics
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming

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