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Impacts of Droughts and Floods on Agricultural Productivity in New Zealand as Measured from Space

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Listed:
  • Elodie Blanc
  • Ilan Noy

Abstract

This study estimates the impact of excess precipitation (or the absence of rainfall) on productivity of agricultural land parcels in New Zealand. This type of post-disaster damage assessments aims to allow for quantification of disaster damage when on-the-ground assessment of damage is too costly or too difficult to conduct. It can also serve as a retroactive data collection tool for disaster loss databases where data collection did not happen at the time of the event. To this end, we use satellite-derived observations of terrestrial vegetation (the Enhanced Vegetation Index – EVI) over the growing season. We pair this data at the land parcel level identifying five land use types (three types of pasture, and annual and perennial crops) with precipitation records, which we use to identify both excessively dry and excessively wet episodes. Using regression analyses, we then examine whether these episodes of excess precipitation had any observable impact on agricultural productivity. Overall, we find statistically significant declines in agricultural productivity that is associated with both floods and droughts. The average impact of these events, averaged over the affected parcels, however, is not very large; usually less than 1%, but quite different across years and across regions. This average hides a heterogeneity of impacts, with some parcels experiencing a much more significant decline in the EVI.

Suggested Citation

  • Elodie Blanc & Ilan Noy, 2022. "Impacts of Droughts and Floods on Agricultural Productivity in New Zealand as Measured from Space," CESifo Working Paper Series 9634, CESifo.
  • Handle: RePEc:ces:ceswps:_9634
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    References listed on IDEAS

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    1. Elodie Blanc & Eric Strobl, 2013. "The impact of climate change on cropland productivity: evidence from satellite based products at the river basin scale in Africa," Climatic Change, Springer, vol. 117(4), pages 873-890, April.
    2. Kensuke Goto & Takehiro Goto & Jephtha Nmor & Kazuo Minematsu & Keinosuke Gotoh, 2015. "Evaluating salinity damage to crops through satellite data analysis: application to typhoon affected areas of southern Japan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 75(3), pages 2815-2828, February.
    3. Soma Sarkar, 2021. "Rapid assessment of cyclone damage using NPP-VIIRS DNB and ancillary data," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 106(1), pages 579-593, March.
    4. Puyu Feng & Bin Wang & De Li Liu & Hongtao Xing & Fei Ji & Ian Macadam & Hongyan Ruan & Qiang Yu, 2018. "Impacts of rainfall extremes on wheat yield in semi-arid cropping systems in eastern Australia," Climatic Change, Springer, vol. 147(3), pages 555-569, April.
    5. Elodie Blanc & Eric Strobl, 2016. "Assessing the Impact of Typhoons on Rice Production in the Philippines," Post-Print hal-01446210, HAL.
    6. Farnaz Pourzand & Ilan Noy & Yiğit Sağlam, 2020. "Droughts and farms’ financial performance: a farm‐level study in New Zealand," Australian Journal of Agricultural and Resource Economics, Australian Agricultural and Resource Economics Society, vol. 64(3), pages 818-844, July.
    7. Mohammad Shamim Hasan Mandal & Tetsuro Hosaka, 2020. "Assessing cyclone disturbances (1988–2016) in the Sundarbans mangrove forests using Landsat and Google Earth Engine," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 102(1), pages 133-150, May.
    8. Md Shahinoor Rahman & Liping Di, 2020. "A Systematic Review on Case Studies of Remote-Sensing-Based Flood Crop Loss Assessment," Agriculture, MDPI, vol. 10(4), pages 1-30, April.
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    More about this item

    Keywords

    satellite-derived data; crop productivity; drought; flood;
    All these keywords.

    JEL classification:

    • Q15 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Agriculture - - - Land Ownership and Tenure; Land Reform; Land Use; Irrigation; Agriculture and Environment
    • Q54 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics - - - Climate; Natural Disasters and their Management; Global Warming
    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models

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