Estimation of spatial variability in pearl millet growth with non-destructive methods

Growth variability in pearl millet (Pennisetum glaucum) over short distance s is a severe constraint on thf interpretation of agricultural experiments in the West African Sahel. The purpose of this study, therefore, was to com pare different non-destructive methods to estimate, spatially, millet growt h and final yields. Aerial photography. georeferenced radiometric measureme nts and a chlorophyll meter were tested during three rainy seasons in a nit rogen rate x density x genotype experiment in western Niger. For the radiom etric measurements, normalized difference vegetation indices (NDVI) obtaine d and calibrated for individual millet hills spaced 1.5 in apart were aggre gated for the entire experiment with 6000 samples per hectare. A simple cal ibration procedure was used to correct for variation in soil background ref lectance and incident light. For NDVI measurements of individual planting h ills, the correlation between plant total dry matter (TDM), leaf weight, le af area and NDVI was high (r(2) = 0.89-0.91) and regression parameters were genotype-specific. Aggregated georeferenced NDVI measurements at the plot level correlated with grain and TDM at harvest (r(2) = 0.40-0.87). The anal ysis of true-colour and infrared aerial photographs permitted the monitorin g of millet growth and the quantitative evaluation of treatment responses t hroughout the growing season. The infrared images were the most efficient i n the detection of vegetation followed by the normalized green band of true -colour images. The red band was the least effective because of the influen ce of soil albedo and image vignetting. Although chlorophyll meter measurem ents reflected relative differences in plant nitrogen status between treatm ents, their interpretation required destructive sampling and proved unsuita ble to predict millet yields. The results demonstrate the potential of geor eferenced radiometric data and aerial photographs to improve soil sampling strategies, sequential plant growth monitoring and the statistical design a nd analysis of experiments. By, providing intermediate data sets, the teste d tools can also help in the upscaling of ground truth to satellite data in yield prediction studies.