Biophysical modelling and NDVI time series to project near‐term forage supply: spectral analysis aided by wavelet denoising and ARIMA modelling

Point‐based biophysical simulation of forage production coupled with 1‐km AVHRR NDVI data was used to determine the feasibility of projecting forage conditions 84 days into the future to support stocking decision making for livestock production using autoregressive integrated moving average (ARIMA) with Box and Jenkins methodology. The study was conducted at three highly contrasting ecosystems in South Texas over the period 1989–2000. Wavelet transform was introduced as a mathematical tool to denoise the NDVI time series. The simulated forage production, NDVI and denoised NDVI (DeNDVI) were subject to spectral decomposition for the detection of periodicities. Spectral analysis revealed bimodal vegetation growth patterns in Southwestern Texas. A yearly cycle (364 days) of peak vegetation production was detected for the three study sites, another peak forage production was revealed by spectral analysis at 182 days following the first peak in vegetation production. A similar trend was found for the NDVI imageries sensing the study sites. Wavelet denoising of NDVI signal was effective in revealing clear periodicities in one study site where maximum variability of NDVI was noted. †Dr Stuth passed away on 24 April 2006. This paper is dedicated to his memory. The Box and Jenkins ARIMA modelling approach was used as a forecasting method for near‐term forage production to assist range managers in proactive operational stocking decisions to mitigate drought risk. Using denoised NDVI provided forage projections with the lowest standard error prediction (SEP) throughout the forecast 84‐day periods. However, acceptable SEP was only achieved up to 6 weeks into a projection for the forage‐only based forecasts. The ARIMA forecasting methodology appears to offer a new approach to help managers of livestock production through the creation of near real‐time early warning systems. Using satellite‐derived NDVI data as a covariate improved the forecast quality and reduced the standard error of forecast in three highly contrasting sites. Denoising the NDVI data using wavelet methods further improved the forecast quality in all study sites. The integration of AVHRR NDVI data and biophysical simulation of forage production appears a promising approach for assisting decision makers in a positive manner by assessing forage conditions in response to emerging weather conditions and near real‐time projection of available forage for grazing animals.

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