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Conservation Agriculture (CA) and Integrated Soil Fertility Management (ISFM) have been promoted in Sub Saharan Africa as a means to improve soil quality. A four season research (March, 2017 to March, 2019 was conducted to evaluate CA-based treatment, no tillage with residue retention (NTR), ISFM-based treatment, conventional tillage with use of manure (CTM), a combination of CA ? ISFM, no tillage with residue retention and use of manure (NTRM) and a control, (C) on soil quality attributes. In the two locations (sub-humid and semi-arid) the effect of soil fertility gradients (high and low) were considered. Trials were set out using a one farm one replicate randomized design. In either high or low fertility fields, soil chemical and physical properties were significantly different between the control and NTR, CTM and NTRM with no significant differences between NTR, CTM and NTRM. SOC was higher under NTR and NTRM practices, which consequently had higher hydraulic conductivity, air permeability, mean weight diameter and available phosphorus. For all the treatments and in both locations, the low fertility fields had significantly lower agronomic use efficiency (AUE) compared to the high fertility fields. In both soil types, plant available water capacity and relative water capacity values were below the recommended thresholds indicating low soil water uptake, suboptimal microbial activity and consequently low nutrient uptake which explains the observed low AUE.

African agriculture is adversely impacted by arable soil compaction, the degree of which is affected by the speed at which the tractor is maneuvered on the fields, which affects the degree of soil compaction. However, there is no reliable, existing mathematical correlation between the extent of compaction on the one hand, and the tractor speed/s and soil moisture levels on the other. This paper bridges this gap in knowledge by resorting to the artificial neural networks (ANNs) method to predict the effects of tractor speed and soil moisture on the state of soil compaction. The models were ‘trained’ with penetration resistance (CPR) and bulk density test data obtained from field measurements. The resulting correlation coefficient (R = 0.9) showed good compliance of the prediction made with the ANN models with on-field data. It follows, thereby, that the model developed by the authors in this study can be effectively used for predicting the effects of speed, soil density, and moisture content on compaction of alluvial, poorly developed soil with much greater precision, thereby providing guidance to farmers around the world.