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SEQUENCING INTEGRATED SOIL FERTILITY MANAGEMENT OPTIONS FOR SUSTAINABLE CROP INTENSIFICATION BY DIFFERENT CATEGORIES OF SMALLHOLDER FARMERS IN ZIMBABWE

Published online by Cambridge University Press:  09 June 2014

H. NEZOMBA*
Affiliation:
Soil Fertility Consortium for Southern Africa (SOFECSA), Department of Soil Science and Agricultural Engineering, University of Zimbabwe, P.O. Box MP167, Mount Pleasant, Harare, Zimbabwe
F. MTAMBANENGWE
Affiliation:
Soil Fertility Consortium for Southern Africa (SOFECSA), Department of Soil Science and Agricultural Engineering, University of Zimbabwe, P.O. Box MP167, Mount Pleasant, Harare, Zimbabwe
R. CHIKOWO
Affiliation:
Department of Crop Science, University of Zimbabwe, P.O. Box MP167, Mount Pleasant, Harare, Zimbabwe
P. MAPFUMO
Affiliation:
Soil Fertility Consortium for Southern Africa (SOFECSA), Department of Soil Science and Agricultural Engineering, University of Zimbabwe, P.O. Box MP167, Mount Pleasant, Harare, Zimbabwe
*
‡Corresponding author. Email: pmapfumo@agric.uz.ac.zw
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Summary

Research has proved that integrated soil fertility management (ISFM) can increase crop yields at the field and farm scales. However, its uptake by smallholder farmers in Africa is often constrained by lack of technical guidelines on effective starting points and how the different ISFM options can be combined to increase crop productivity on a sustainable basis. A 4-year study was conducted on sandy soils (<10% clay) on smallholder farms in eastern Zimbabwe to assess how sequencing of different ISFM options may lead to incremental gains in soil productivity, enhanced efficiency of resource use, and increase crop yields at field scale. The sequences were primarily based on low-quality organic resources, nitrogen-fixing green manure and grain legumes, and mineral fertilizers. To enable comparison of legume and maize grain yields among treatments, yields were converted to energy (kilocalories) and protein (kg) equivalents. In the first year, ‘Manure-start’, a cattle manure-based sequence, yielded 3.4 t ha−1 of maize grain compared with 2.5 and 0.4 t ha−1 under a woodland litter-based sequence (‘Litter-start’) and continuous unfertilized maize control, respectively. The ‘Manure-start’ produced 12 × 106 kilocalories (kcal); significantly (p < 0.05) out-yielding ‘Litter start’ and a fertilizer-based sequence (‘Fertilizer-start’) by 50%. A soyabean-based sequence, ‘Soya-start’, gave the highest protein production of 720 kg against <450 kg for the other sequencing treatments. In the second year, the sequences yielded an average of 5.7 t ha−1 of maize grain, producing over 19 × 106 kcal and 400 kg of protein. Consequently, the sequences significantly out-performed farmers’ designated poor fields by ~ fivefold. In the third year, ‘Soya-start’ gave the highest maize grain yield of 3.7 t ha−1; translating to 1.5 and 3 times more calories than under farmers’ designated rich and poor fields, respectively. In the fourth year, ‘Fertilizer-start’ produced the highest calories and protein of 14 × 106 kcal and 340 kg, respectively. Cumulatively over 4 years, ‘Manure-start’ and ‘Soya-start’ gave the highest calories and protein, out-performing farmers’ designated rich and poor fields. Sunnhemp (Crotalaria juncea L.)-based sequences, ‘Green-start’ and ‘Fertilizer-start’, recorded the highest gains in plant available soil P of ~ 4 mg kg−1 over the 4-year period. Assessment of P agronomic efficiencies showed significantly more benefits under the ISFM-based sequences than under farmers’ designated rich and poor fields. Based on costs of seed, nutrients and labour, ‘Soya-start’ gave the best net present value over the 4 years, while ‘Fertilizer-start’ was financially the least attractive. Overall, the ISFM-based sequences were more profitable than fields designated as rich and poor by farmers. We concluded that ISFM-based sequences can provide options for farm-level intensification by different categories of smallholder farmers in Southern Africa.

Information

Type
Research Article
Copyright
Copyright © Cambridge University Press 2014 
Figure 0

Table 1. Integrated soil fertility management regimes prioritized by farmers differing in resource endowment on smallholder farms in Zimbabwe.

Figure 1

Table 2. Sequencing framework of integrated soil fertility management (ISFM) options on smallholder farms in Zimbabwe.

Figure 2

Table 3. A summary of the conceptual rationale used for applying different rates of mineral N and P fertilizer under the ISFM sequences.

Figure 3

Table 4. Physical and chemical characteristics of soils (0–20 cm) at establishment of experiments in Makoni and Hwedza smallholder farming areas in Zimbabwe.

Figure 4

Figure 1. Cumulative daily rainfall received in (a) Makoni and (b) Hwedza during the study period.

Figure 5

Table 5. Input and output prices used in gross margin analyses under different ISFM sequences in Makoni and Hwedza smallholder farming areas in Zimbabwe. Prices are averages of 2008/2009, 2009/2010 and 2010/2011 cropping seasons.

Figure 6

Figure 2. Energy (kcal ha−1) (a and b) and protein (kg ha−1) (c and d) derived from maize and soyabean grain produced under different ISFM sequences in Makoni and Hwedza smallholder farming area, Zimbabwe (‘rich’ and ‘poor’ fields refer to farmers’ designated most and least productive fields, respectively). Error bars represent standard error of the difference of means (SEDs) for a = Year 1, b = Year 2, c = Year 3, d = Year 4 and e = Overall.

Figure 7

Figure 3. Changes in plant available soil P (0–20 cm) under different ISFM sequences on smallholder farms in Makoni, Zimbabwe. Error bars represent standard error of the difference of means (SEDs).

Figure 8

Figure 4. Relationship between maize yields in the third year and pre-season plant available soil P (0–20 cm) under different ISFM sequences in Hwedza smallholder farming area, Zimbabwe (‘rich’ and ‘poor’ fields refer to farmers’ designated most and least productive fields, respectively).

Figure 9

Figure 5. Agronomic N (a) and P (b) efficiencies in the fourth year of sequencing in Makoni and Hwedza smallholder farming areas, Zimbabwe. Error bars represent standard error of the difference of means (SEDs) for a = Makoni and b = Hwedza.

Figure 10

Table 6. Net present values (US$ ha−1) of ISFM sequences over four seasons in Makoni and Hwedza smallholder farming areas in Zimbabwe (‘rich’ and ‘poor’ fields refer to farmers’ designated most and least productive fields, respectively).

Figure 11

Figure 6. Gross margins (US$ ha−1) of ISFM sequences over four seasons in (a) Makoni and (b) Hwedza smallholder farming area in Zimbabwe.