Prediction of Nitrogen Application in Maize Based on the Normalised Difference Vegetation Index (NDVI)

Abstract

Aims: To determine the quantity of nitrogen required for vegetative, yield and yield components of maize and to predict these attributes using the normalised difference vegetative index. Study Design: Experiment was carried out in randomized complete block design five treatments and replicated three replicates. Place and Duration of Study: Field experiment was carried out at Ladoke Akintola University of Technology at Teaching and Research Farm, Ogbomoso (latitude 8º10' N, longitude 40º10' E and elevation 1,286 m), Nigeria, during the raining season between June and October of 2015. Methodology: Seeds of nine maize hybrids were obtained from the International Institute of Tropical Agriculture, Ibadan, and submitted to five nitrogen rates (0, 80, 100, 120 and 140 kg          Nha-1). Seeds were sown in two 5 m row plots with 0.75 m space between rows and 0.5 m within rows. Data of NDVI at 2, 3 and 4 weeks after planting (WAP), number of leaves, plant height, cob weight, grain weight, harvest index of cob and grain were collected and subjected to analysis of variance (ANOVA). Results: The ANOVA indicated significant variation (P ≤ 0.05) among nitrogen treatments for all growth and yield parameters. There was a significant correlation between NDVI values and grain yield per hectare indicating that NDVI can be used to predict maize performance. As a result of the magnitude of the correlation coefficient between NDVI at 4 WAP and grain yield (GY), regression analysis was computed between these two parameters; for every possible change in the value of NDVI 4 WAP, the corresponding equation: GY = 2592.5 + 9653.5 x (NDVI 4WAP). Conclusion: The study concluded that application of 140 kg Nha-1 nitrogen fertiliser improves yield and yield components of maize. Moreover, judicious application of remote sensing (based on the NDVI) can be used to predict maize performance, thereby enhancing nutrient and other resource management in maize and ensuring high grain yield production.