Integration of point biophysical modeling and NDVI data to improve forecasting of near term forage conditions in Texas

Point based biophysical simulation of forage production coupled with 1-km AVHRR-NDVI data was used to determine the feasibility of projecting forage condition 84 days into the future to support stocking decision making for livestock production using autoregressive integrated moving average (ARIMA) of Box and Jenkins methodology. The study was conducted at three highly contrasting ecosystems in South Texas representing the period 1989–2000. The simulated forage production and corresponding NDVI data exhibited considerable correspondence with emerging weather conditions. Five to six dry periods were detected during this 12-year period for each study site. Some sites exhibited noisy NDVI signals following the erratic vegetative growth patterns as well as the inherent nature of NDVI data. Therefore, wavelet transform was introduced as a mathematical approach to denoise the NDVI time series. The simulated forage production, NDVI and denoised NDVI (DeNDVI) were subjected to spectral decomposition and frequency domain analysis searching for the presence of periodicity among and cross the time series. Spectral analysis revealed bimodal vegetation growth patterns in South Texas. Wavelet denoising of NDVI signal was effective in revealing clear periodicities in one study site where maximum variability of NDVI was noted.; Given the high correspondence between modeled forage and denoised NDVI, the Box and Jenkins ARIMA modeling 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 creation of near real-time early warning systems in the future.