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Water availability and crop growth at the crop plot level in South Africa modelled from satellite imagery

Published online by Cambridge University Press:  08 April 2014

E. BLANC*
Affiliation:
Massachusetts Institute of Technology, Joint Program on the Science and Policy of Global Change, 77 Massachusetts Avenue, Cambridge, MA 02139-4307, USA
E. STROBL
Affiliation:
Ecole Polytechnique, Department of Economics, Cedex 91128, Palaiseau, France
*
*To whom all correspondence should be addressed. Email: eblanc@mit.edu

Summary

Although the effect of weather on crop growth has been studied widely, the contribution of other water sources has been less well studied, mainly due to data limitation. To address this gap, the current analysis considers the importance of water availability on crop growth by taking advantage of crop field boundaries and information on South Africa's four major grain producing provinces (Northwest, Mpumalanga, Free State and Gauteng) provided by the Agricultural Geo-referenced Information System dataset. To capture crop growth along the crop growing cycle at the plot level, the MODIS's MOD13Q1 dataset of 16-day normalized difference vegetation index (NDVI) was used. To estimate the determinants of crop growth, weather effects were considered and represented by rainfall and reference evapotranspiration satellite derived data provided by the National Oceanic and Atmospheric Administration's RFE and GDAS dataset, respectively. Hydrologic and irrigation determinants were estimated based on the HYDRO1K river network dataset produced by the US Geological Survey. The results show that although weather is an important explanatory factor, other sources of water, such as irrigation, proximity to perennial and ephemeral rivers, and stream flow are also influential. Taking into account the interaction effects between weather and water availability related factors is also important to determine the effect of water availability on crop growth.

Type
Crops and Soils Research Papers
Copyright
Copyright © Cambridge University Press 2014 

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References

Allen, R. G., Pereira, L. S., Raes, D. & Smith, M. (1998). Crop Evapotranspiration – Guidelines for Computing Crop Water Requirements. FAO Irrigation and Drainage Paper no. 56. Rome: FAO.Google Scholar
Artan, G. A., Asante, K., Smith, J., Pervez, S., Entenmann, D., Verdin, J. P. & Rowland, J. (2008). Users’ Manual for the Geospatial Stream Flow Model (GeoSFM). U.S. Geological Survey Open-File Report 2007–1440. Reston, Virginia, USA: U.S. Geological Survey.Google Scholar
Azam-Ali, S. N. & Squire, G. R. (2002). Principles of Tropical Agronomy. Wallingford, UK: CABI Publishing.Google Scholar
Benedetti, R. & Rossini, P. (1993). On the use of NDVI profiles as a tool for agricultural statistics: the case study of wheat yield estimate and forecast in Emilia Romagna. Remote Sensing of Environment 45, 311326.Google Scholar
Blanc, E. & Strobl, E. (2013). The impact of climate change on cropland productivity: evidence from satellite based products at the river basin scale in Africa. Climatic Change 117, 873890.Google Scholar
Das, D. K., Mishra, K. K. & Kalra, N. (1993). Assessing growth and yield of wheat using remotely-sensed canopy temperature and spectral indices. International Journal of Remote Sensing 14, 30813092.Google Scholar
Doraiswamy, P. C. & Cook, P. W. (1995). Spring wheat yield assessment using NOAA AVHRR data. Canadian Journal of Remote Sensing 21, 4351.Google Scholar
Driscoll, J. C. & Kraay, A. C. (1998). Consistent covariance matrix estimation with spatially dependent panel data. Review of Economics and Statistics 80, 549560.Google Scholar
Eidenshink, J. C. (1992). The 1990 conterminous US AVHRR data set. Photogrammetric Engineering and Remote Sensing 58, 809813.Google Scholar
FAO (1996). Major Climates of Africa. Rome: FAO GeoNetwork. Available from: http://www.fao.org/geonetwork/srv/en/metadata.show?id=6&currTab=simple (accessed 19 February 2014).Google Scholar
FAO (2011). Digital Soil Map of the World. Rome: FAO. Available from: http://www.fao.org/geonetwork/srv/en/metadata.show?id=14116 (accessed 12 February 2014).Google Scholar
Ferreira, S. L., Newby, T. & du Preez, E. (2006). Use of remote sensing in support of crop area estimates in South Africa. In SPRS Archives XXXVI-8/W48 Workshop Proceedings: Remote Sensing Support to Crop Yield Forecast and Area Estimates (Eds Baruth, B., Royer, A. & Genovese, G.), pp. 5152. Ispra, Italy: Joint Research Centre.Google Scholar
Groten, S. M. E. (1993). NDVI-crop monitoring and early yield assessment of Burkina Faso. International Journal of Remote Sensing 14, 14951515.Google Scholar
Gupta, R. K., Prasad, S., Rao, G. H. & Nadham, T. S. V. (1993). District level wheat yield estimation using NOAA/AVHRR NDVI temporal profile. Advances in Space Research 13, 253256.Google Scholar
Hayes, M. J. & Decker, W. L. (1996). Using NOAA AVHRR data to estimate maize production in the United States Corn Belt. International Journal of Remote Sensing 17, 31893200.Google Scholar
Hochheim, K. P. & Barber, D. G. (1998). Spring wheat yield estimation for Western Canada using NOAA NDVI data. Canadian Journal of Remote Sensing 24, 1727.Google Scholar
Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X. & Ferreira, L. G. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment 83, 195213.Google Scholar
Jönsson, P. & Eklundh, L. (2002). Seasonality extraction by function fitting to time-series of satellite sensor data. IEEE Transactions on Geoscience and Remote Sensing 40, 18241832.Google Scholar
Jönsson, P. & Eklundh, L. (2004). TIMESAT – a program for analysing time-series of satellite sensor data. Computers and Geosciences 30, 833845.CrossRefGoogle Scholar
Karnieli, A., Agam, N., Pinker, R. T., Anderson, M., Imhoff, M. L., Gutman, G. G., Panov, N. & Goldberg, A. (2010). Use of NDVI and land surface temperature for drought assessment: merits and limitations. Journal of Climate 23, 618633.Google Scholar
Koller, M. & Upadhyaya, S. K. (2005). Prediction of processing tomato yield using a crop growth model and remotely sensed aerial images. Transactions of the ASAE 48, 23352341.Google Scholar
Labus, M. P., Nielsen, G. A., Lawrence, R. L., Engel, R. & Long, D. S. (2002). Wheat yield estimates using multi-temporal NDVI satellite imagery. International Journal of Remote Sensing 23, 41694180.Google Scholar
Malo, A. R. & Nicholson, S. E. (1990). A study of rainfall and vegetation dynamics in the African Sahel using normalized difference vegetation index. Journal of Arid Environments 19, 124.Google Scholar
Milesi, C., Samanta, A., Hashimoto, H., Krishna Kumar, K., Ganguly, S., Thenkabail, P. S., Srivastava, A. N., Nemani, R. R. & Myneni, R. B. (2010). Decadal variations in NDVI and food production in India. Remote Sensing 2, 758776.Google Scholar
Monfreda, C., Ramankutty, N. & Hertel, T. W. (2009). Global agricultural land use data for climate change analysis. In Economic Analysis of Land Use in Global Climate Change Policy (Eds Hertel, T. W., Rose, S. & Tol, R. S. J.), pp. 3348. Abingdon, Oxon, UK: Routledge.Google Scholar
Moran, P. A. P. (1950). Notes on continuous stochastic phenomena. Biometrika 37, 1723.Google Scholar
van Niekerk, L. (2012). Pocket Guide to South Africa 2011/2012. Pretoria, South Africa: GCIS.Google Scholar
Nuarsa, I. W., Nishio, F. & Hongo, C. (2011). Relationship between rice spectral and rice yield using MODIS data. Journal of Agricultural Science (Canada) 3, 8088.Google Scholar
Potdar, M. B. (1993). Sorghum yield modelling based on crop growth parameters determined from visible and near-IR channel NOAA AVHRR data. International Journal of Remote Sensing 14, 895905.Google Scholar
Prasad, A. K., Chai, L., Singh, R. P. & Kafatos, M. (2006). Crop yield estimation model for Iowa using remote sensing and surface parameters. International Journal of Applied Earth Observation and Geoinformation 8, 2633.CrossRefGoogle Scholar
Quarmby, N. A., Milnes, M., Hindle, T. L. & Silleos, N. (1993). The use of multi-temporal NDVI measurements from AVHRR data for crop yield estimation and prediction. International Journal of Remote Sensing 14, 199210.Google Scholar
Schultz, P. A. & Halpert, M. S. (1993). Global correlation of temperature, NDVI and precipitation. Advances in Space Research 13, 277280.Google Scholar
StataCorp (2011). Stata Statistical Software: Release 12. College Station, TX, USA: StataCorp LP.Google Scholar
USGS (2011). HYDRO1k Dataset. Washington, DC: U.S. Geological Survey. Available from: https://lta.cr.usgs.gov/HYDRO1K (accessed 12 February 2014).Google Scholar
Weissteiner, C. J. & Kühbauch, W. (2005). Regional yield forecasts of malting barley (Hordeum vulgare L.) by NOAA-AVHRR remote sensing data and ancillary data. Journal of Agronomy and Crop Science 191, 308320.Google Scholar
Yang, W., Yang, L. & Merchant, J. W. (1997). An assessment of AVHRR/NDVI-ecoclimatological relations in Nebraska, U.S.A. International Journal of Remote Sensing 18, 21612180.CrossRefGoogle Scholar