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The effect of nitrogen-fertilizer and optimal plant population on the profitability of maize plots in the Wami River sub-basin, Tanzania: A bio-economic simulation approach

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  • Kadigi, Ibrahim L.
  • Richardson, James W.
  • Mutabazi, Khamaldin D.
  • Philip, Damas
  • Mourice, Sixbert K.
  • Mbungu, Winfred
  • Bizimana, Jean-Claude
  • Sieber, Stefan

Abstract

Maize (Zea mays L.) is the essential staple in sub-Saharan Africa (SSA) and Tanzania in particular; the crop accounts for over 30% of the food production, 20% of the agricultural gross domestic product (GDP) and over 75% of the cereal consumption. Maize is grown under a higher risk of failure due to the over-dependence rain-fed farming system resulting in low income and food insecurity among maize-based farmers. However, many practices, including conservation agriculture, soil and water conservation, resilient crop varieties, and soil fertility management, are suggested to increase cereal productivity in Tanzania. Improving planting density, and the use of fertilizers are the immediate options recommended by Tanzania's government. In this paper, we evaluate the economic feasibility of the improved planting density (optimized plant population) and N-fertilizer crop management practices on maize net returns in semi-arid and sub-humid agro-ecological zones in the Wami River sub-Basin, Tanzania. We introduce a bio-economic simulation model using Monte Carlo simulation procedures to evaluate the economic viability of risky crop management practices so that the decision-maker can make better management decisions. The study utilizes maize yield data sets from two biophysical cropping system models, namely the APSIM and DSSAT. A total of 83 plots for the semi-arid and 85 plots for the sub-humid agro-ecological zones consisted of this analysis. The crop management practices under study comprise the application of 40 kg N-fertilizer/ha and plant population of 3.3 plants/m2. The study finds that the use of improved plant population had the lowest annual net return with fertilizer application fetching the highest return. The two crop models demonstrated a zero probability of negative net returns for farms using fertilizer rates of 40 kg N/ha except for DSSAT, which observed a small probability (0.4%) in the sub-humid area. The optimized plant population presented 16.4% to 26.6% probability of negatives net returns for semi-arid and 14.6% to 30.2% probability of negative net returns for sub-humid zones. The results suggest that the application of fertilizer practices reduces the risks associated with the mean returns, but increasing the plant population has a high probability of economic failure, particularly in the sub-humid zone. Maize sub-sector in Tanzania is projected to continue experiencing a significant decrease in yields and net returns, but there is a high chance that it will be better-off if proper alternatives are employed. Similar studies are needed to explore the potential of interventions highlighted in the ACRP for better decision-making.

Suggested Citation

  • Kadigi, Ibrahim L. & Richardson, James W. & Mutabazi, Khamaldin D. & Philip, Damas & Mourice, Sixbert K. & Mbungu, Winfred & Bizimana, Jean-Claude & Sieber, Stefan, 2020. "The effect of nitrogen-fertilizer and optimal plant population on the profitability of maize plots in the Wami River sub-basin, Tanzania: A bio-economic simulation approach," Agricultural Systems, Elsevier, vol. 185(C).
    • Handle: RePEc:eee:agisys:v:185:y:2020:i:c:s0308521x2030809x
    DOI: 10.1016/j.agsy.2020.102948
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    References listed on IDEAS

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    2. Tang, Ruoling & Supit, Iwan & Hutjes, Ronald & Zhang, Fen & Wang, Xiaozhong & Chen, Xuanjing & Zhang, Fusuo & Chen, Xinping, 2023. "Modelling growth of chili pepper (Capsicum annuum L.) with the WOFOST model," Agricultural Systems, Elsevier, vol. 209(C).
    3. Videla-Mensegue, H. & Caviglia, O.P. & Sadras, V.O., 2022. "Functional crop types are more important than diversity for the productivity, profit and risk of crop sequences in the inner Argentinean Pampas," Agricultural Systems, Elsevier, vol. 196(C).

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