Using remote sensing to forecast forage quality for cattle in the dry savannas of northeast Australia

Abstract

In the dry savannas of northeast Australia, forage quality is just as important for cattle production as forage quantity. The seasonal trend of forage quality is broadly predictable by land managers, but it is more difficult to predict the point when quality—which depends on local climate, management, and pasture condition—falls below the requirement for animal maintenance. In this study we use statistical modelling to forecast how forage quality might change at the crucial time of year, i.e., as the summer wet season transitions to the dry winter. We do this with the aid of historical information associated with a long-term cattle-grazing trial in the dry savannas. We combined multiple years of two measures of forage quality (dietary crude protein and in vivo dry-matter digestibility; respectively DCP and DMD) and ground cover information (specifically the ratio of ‘green grass’ cover to ‘dead (i.e., non-photosynthetic) grass’ cover, derived from an archive of Landsat satellite imagery) into a linear mixed model that explicitly considered the correlations with time and between variables. DCP and DMD were estimated by near-infrared spectroscopy of fresh faecal samples; values did not have to be temporally coincident with the satellite imagery. With the end of May considered a nominal decision-point, we forecast monthly averages of forage quality for June to August, over a 12-year period at the study site. Over all months and all years, the median absolute error of the forecasts was DCP = 0.86%, and DMD = 0.95%. The remote sensing information served as a correlated, oft-sampled covariate that helped to guide the forecasts of forage quality. We propose summarising the forecasts (and their uncertainty) as a near-real-time graphical tool for decision-support. Such a product could potentially benefit cattle-grazing enterprises in the northeast of Australia, enabling more timely management of herds through the dry season.