How generalisable are empirical models to estimate high resolution spatial indicators of crop performance at the regional scale?

Lyle, G.; Arbon, K.; Clarke, K.; Summers, D.; Ostendorf, B.; Piantadosi, J.; Anderssen, R.S.; Boland, J.; International Congress on Modelling and Simulation

How generalisable are empirical models to estimate high resolution spatial indicators of crop performance at the regional scale?

2013

Climate change mitigation and adaptation responses in agricultural areas will be a balance between strategies such as the allocation of land to different land uses and changes to current land management. Identification of the spatial variability of production is fundamental to both strategies if efficient area-based decisions are to be made which minimise on-farm economic losses and food security and sustainability issues. Also critical to these strategies is that the information created must be at a resolution high enough to enable on-farm decisions and at extents large enough to guide targeted governmental policy. This paper aims to address these needs by developing a method for mapping spatial variation in yield at a high resolution across the cropping areas in South Australia. We developed regression models between satellite-derived Normalised Difference Vegetation Index (NDVI) and yield measured through precision agriculture technologies across three years and compared their predictions with regional yield statistics. Observations and predictions of district average grain yields ranged from 0.5 to 3.3 t/ha during the study period. The best model efficiency criterion (Nash-Sutcliffe) was 65% in 2005. Our results demonstrate that empirical relationships between high resolution spatial datasets are spatially representative. That is, a model based on the yield-NDVI relationship developed in a very small area can be used to accurately predict regional yield.

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Lyle, G., Arbon, K., Clarke, K., Kilpatrick, A., Summers, D. and Ostendorf, B.

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http://www.mssanz.org.au/modsim2013/index.html

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