Abstract

In the coming decades, Sub-Saharan Africa faces challenges to sustainably increase food production while keeping pace with continued population growth. Conservation agriculture (CA) has been proposed to enhance soil health and productivity to respond to this situation. To increase maize yields, the main staple food in SSA, the selection of suitable genotypes has been explored using remote sensing tools. They may play a fundamental role in overcoming the limitations of data collection and processing in large scale phenotyping studies. We present the result of a study in which Red-Green-Blue and multispectral indexes were evaluated for assessing maize performance under conventional ploughing (CP) and CA practices. The measurements were conducted on seedlings at ground level and from an unmanned aerial vehicle platform. Most indexes were significantly affected by tillage conditions, increasing their values from CP to CA. Indexes derived from the RGB-images related to canopy greenness performed better at assessing yield differences, potentially due to the greater resolution of the RGB compared with the multispectral data, although this performance was more precise for CP than CA. The correlations of the multispectral indexes with yield were improved by applying a soil-mask derived from a NDVI threshold.

Highlights

  • Traditional practices of land preparation involve soil tillage through moldboard ploughing, to soften the seedbed, to ensure uniform germination, to remove weed plants, and to release soil nutrients through mineralization and oxidation

  • Crops have been grown on conventional tillage for many years, and genes governing the adaptation to Conservation agriculture (CA) either have been lost over time through untargeted selection or have become redundant [10]

  • CA management practices had a positive effect on increasing yields as compared to the conventional ploughing (CP)

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Summary

Introduction

Traditional practices of land preparation involve soil tillage through moldboard ploughing, to soften the seedbed, to ensure uniform germination, to remove weed plants, and to release soil nutrients through mineralization and oxidation. This mechanical disturbance is leading to a decline in organic matter, an increase of the loss of water by runoff, and, to soil erosion [1]. Sub-Saharan Africa (SSA) is expected to be vulnerable due to the range of projected impacts: e.g., multiple stresses and the low adaptive capacity of current cropping systems, as well as population increase [2]. The classical approach has involved the use of multispectral data for the development of numerous vegetation indexes to assess biomass (e.g., Normalized Difference Vegetation Index, NDVI), water content

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