A decision-support model for monitoring nutrient balances under agricultural land use (NUTMON)

A quantitative model of the balance between inputs and outputs of nitrogen, phosphorus and potassium in African land use systems (NUTBAL) was recently developed at two scales: supra-national (38 sub-Saharan African countries) and regional (Kisii District, Kenya). Calculating inputs (mineral fertilizer, organic manure, wet and dry deposition, biological nitrogen fixation, sedimentation) and outputs (removal of above-ground crop parts, leaching, denitrification, water erosion) led to the conclusion that there are considerable net fertility losses in each growing period.

In this paper, NUTBAL is elaborated into a decision-support model (NUTMON) to monitor the effects of changing land use, and suggest interventions that improve the nutrient balance. As input and output determinants cannot all be quantified equally well, the model recognizes primary data, estimates, and assumptions. The NUTMON determinants are mostly scale-neutral and can therefore be used to monitor nutrient balances at farm, regional, national and supra-national level. This is essential since the hierarchical levels interact. A number of recent interventions at the regional level (Kisii District, Kenya) are elaborated, including national fertilizer and produce price policies, fertilizer supply in small packages, zero-grazing, agroforestry, soil conservation measures, and increasing fertilizer use efficiency. It is shown that a major nutrient conservation effort in Kisii reduces nutrient depletion by approximately 50%, but does not entirely redress the N and K balance. To achieve the latter without reducing crop production, 75% of the district would have to be converted to a rotation system of maize and green manure cover crops, whereas 25% can remain under tea.

NUTMON has the potential to become a dynamic tool for land use policies, geared towards a balanced nutrient status in African land use systems. It can assist decision makers in determining the effects of current and alternative land use scenarios, taking account of both the productivity as well as the long-term sustainability of agro-ecosystems.