Raising productivity of maize-based cropping systems in eastern and southern Africa: Step-wise intensification options

C H A P T E R

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Raising productivity of maizebased cropping systems in eastern and southern Africa: Step-wise intensification options John Dimes, Daniel Rodriguez, Andries Potgieter Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Toowoomba, QLD, Australia

1 INTRODUCTION

erosion which results in generally low yields of approximately 1 t ha−1 for the staple grains (Rockstrom and Falkenmark, 2000). In some cases, land degradation is irreversible. With growth of population and incomes, the demand for maize is projected to increase approximately 3–4% annually over the next 10 years, requiring at least 40% more maize grain to be accessed in a very short time horizon. Rain-fed, smallholder agricultural systems prevail in ESA and sub-Saharan Africa, and farmers are highly vulnerable to biotic and abiotic stresses and price fluctuations. Intensification of these cropping systems is challenging, and much of the past growth in agricultural production has been through expansion of area cultivated, having severe environmental consequences. In these circumstances, increasing the productivity

Close to 400 million people live in eastern and southern Africa (ESA), with more than half living in extreme poverty and 75% residing in rural areas (Mulugetta et al., 2011). Food security is a major concern, as the region is barely selfsufficient in food grains with net imports of 10% if South Africa is excluded (FAOSTAT, 2009). Maize is the main and preferred food staple in ESA, with per capita consumption averaging 44 kg year−1 in Ethiopia and exceeding 100 kg year−1 in Malawi. In years of surplus, maize is also an important source of income to many farmers. Approximately 65% of the agricultural land in sub-Saharan Africa suffers from degradation (UNEP/ISRIC.1991; GEF, 2003) including low nutrient content, physical degradation and Crop Physiology. DOI: 10.1016/B978-0-12-417104-6.00005-4 Copyright © 2015 Elsevier Inc. All rights reserved

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and resource-use efficiency of agriculture is an essential component to achieve food secured households. Together with the lack of functional markets, insidious nutrient depletion and significant season-to-season variation in weather, farmers find it difficult to justify the investment of scarce resources in new technologies. The aims of this chapter are to describe key constraints to the sustainable intensification of maize-based cropping systems in ESA, and discuss the need for stepping-stone approaches to the increase in farmers’ investment in technology.

2  MAIZE-BASED FARMING SYSTEMS IN EASTERN AND SOUTHERN AFRICA 2.1  Maize cropping systems Maize production occupies between 40% (3.0 Mha,Tanzania) and 90% (1.2 Mha, Malawi) of cultivated lands across countries in ESA. Maize crops are intercropped, predominantly with legumes, but also with vegetable crops, generally in fields closer to farm homesteads. Intercropping is favored because of small farm size, manual systems of land preparation in association with animal draught power and problems of weed control. The majority of rural households pursue mixed farming enterprises and maize residues are an important feed source for cattle and goats during the dry seasons, either grazed in situ or stored close to night pens. In higher rainfall regions where dairy production is undertaken, green maize leaves are also an important feed source (Herrero et al., 2013). The legume species vary across the region, although the most common species are beans (Phaseolus vulgaris), cowpeas (Vigna unguiculata), pigeon pea (Cajanus cajan), groundnuts (Arachis hypogea), chickpea (Cicer arietinum) and soybeans (Glycine max). Maize intercropped with beans, cowpeas and pigeon peas are more common in small land-holdings, whereas sole

legume crops such as groundnuts and soybeans in rotation with maize are more common where there is less pressure on the land. The legumes are an important source of protein and are often valuable ‘women crops’, both for household consumption and for cash income.

2.2 Climate We have examined maize production in five countries of ESA – Ethiopia, Kenya, Tanzania, Malawi and Mozambique, and the main analysis relates to 14 locations across the region (Table 5.1). These sites are the current focus of a maize– legume intensification project led by CIMMYT (Mulugetta et al., 2011). They have varying extents of climate similarity in the sub-region (Fig. 5.1a) and are highly representative of its most severe and persistent food insecurity (Fig. 5.1b). Semiarid sites (Bulawayo and Katumani), where maize production is commonly practiced, are included in the analysis to compare maize response in drier cropping environments. Average annual rainfall for the 14 locations is generally high, ranging from 750 to 1900 mm (Table 5.1). Malawi, Mozambique and Zimbabwe sites experience a well-defined unimodal rainfall pattern, with >94% of rainfall between October and May. In contrast, the Kenya sites experience a bimodal pattern in which both seasons (3–4 months rainfall duration) are fully exploited for maize production and together receive 81–87% of annual rainfall. In Tanzania and Ethiopia, there is substantial rainfall (20–44% of annual) outside the main cropping periods that is generally considered too unreliable for maize production. Average in-crop rainfall for the 16 siteseasons range from 395 mm at Selian, Tanzania to 858 mm for the long rains season at Kakamega in western Kenya (Fig. 5.1). The in-crop rainfall could be considered mostly reliable (11 of 16 site-seasons have CV% ≤30%) and adequate for rain-fed maize. The variability of rainfall for bimodal seasons at Embu (CV = 35% and 42%)

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2  Maize-based farming systems in eastern and southern Africa

TABLE 5.1  Climate characteristics of sites analyzed in this study. Sites in Kenya have a bimodal rainfall pattern, whereas the remaining sites have a unimodal pattern with >94% annual rain concentrated in a single season from October to May

Country

For period of maize growth cycle

Region

Rainfall station

Climate record

Annual rainfall (mm)

Eastern

Embu

1983–2013

1286

20

18

Western

Kakamega

1980–2011

1927

25

21

Semi-arid tropics

Katumani

1957–1998

683

20

18

Western

Baco

1986–2012

1292

20

24

Rift Valley

Melkassa

1977–2011

812

21

19

Hawassa

1982–2011

1013

20

23

Mbulu

1981–2010

823

22

22

Selian

1992–2011

769

21

18

Eastern

Ilonga

1981–2011

1013

25

17

Mid-altitude

Chitedze

1949–2008

897

22

19

Kasungu

1947–1998

797

23

21

Low-altitude

Chitala

1947–1998

890

23

21

Manica Province

Sussundenga

1969–2005

1168

24

22

Chimoio

1951–2011

1081

24

21

Tete Province

Dedza (proxy for Angonia)

1958–1998

950

20

19

Semi-arid tropics

Bulawayo

1939–2007

577

22

24

Daily average Daily radiation temperature (°C) (MJ m−2)

BIMODAL Kenya

UNIMODAL Ethiopia

Tanzania

Malawi

Mozambique

Zimbabwe

Northern

and unimodal seasons at Mbulu (41%), Kasungu (37%) and Sussundenga (35%) suggest more frequent water deficits in maize crops. This is particularly the case at Kasungu and Sussundenga where deep (1.5 m), light textured sandy soils are more common. At the majority of sites, heavier textured clay loams to light clay soils predominate, with maize rooting depths of 0.9 to 1.5 m.

2.3  Maize management and crop performance For the countries under consideration, maize is generally the most important crop for farmers’ investment choices except in Ethiopia, where tef (Eragrostis tef), an indigenous species, is an equally important cash crop and food

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FIG. 5.1  (a) similarity of monthly temperature and rainfall patterns. (b) most severe clustering of food insecurity based on yield gap data (1999–2001) in 5 countries of eastern and southern Africa.

staple. Even so, average maize yields across the sub-region are typically 1–2 t ha−1, less than half the estimated attainable yield of 3–4 t ha−1 (Koo, 2012). Improved germplasm occupies less than 25% of the maize area and, although hybrids have been developed in the region since the 1960s, farmers’ uptake has been low (Denning et al., 2009). Farmers’ investment in soil fertility management has been even more limited (Wichelns, 2006), despite the widespread distribution of soils having inherently low soil organic matter content and associated low soil nutrient supply. Poor legume yield limits the

contribution of N biological fixation (Giller and Cadisch, 1995; Giller et al., 2009) and farmyard manure is mostly of low nutrient content (Probert et al., 1995, 2005). Maize crops typically experience substantial weed competition due to labor shortages delaying and restricting weeding frequency – some crops will be weeded only once, most are weeded twice, up to flowering. There is generally substantial weed biomass in fields at maize maturity and dry season weed control is almost non-existent. In relation to the in-crop rainfall (Fig. 5.2), farmers’ current agronomic practices result in chronically poor

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3  Sustainable intensification of sub-Saharan agriculture

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FIG. 5.2  Average in-crop rainfall and its coefficient of variation for the 16 site-seasons (solid symbols) examined in this study; semi-arid environments (open symbols) are shown for comparisons. Bimodal (squares) and unimodal (diamonds) cropping seasons are represented.

efficiency in the use of both rainfall and labor (Rockstrom et al., 2009).

3 SUSTAINABLE INTENSIFICATION OF SUBSAHARAN AGRICULTURE 3.1  A stepping-stone approach for adoption of complex technological packages Increasingly, conservation agriculture (CA) (Wall et al., 2013), defined as minimal or no soil disturbance, maintenance of a minimal soil cover (30% ground cover) and incorporating crop rotation, particularly with legumes, is being proposed as the basis of intensifying smallholder agriculture in Africa. Although its applicability in the African context has been contested by some (Giller et al., 2009), it does address the widespread issue of soil sustainability that was highlighted in the Introduction. The principles of CA have extremely wide applicability, being practiced on a wide range of environments and cropping systems (Wall, 2007). However, CA is

knowledge intensive, equipment and expertise sensitive and requires high cash inputs including herbicides, seed of improved varieties and fertilizers (Wall, 2007; Thierfelder and Wall, 2011). For a comparison, Chapter 3 outlines the experience in the deployment of conservation agriculture in China which shares a dominance of small-holdings with Africa, albeit in a context of more developed infrastructure and better opportunities for technology adoption. While significant energy and cost savings have been the basis of wide-scale adoption in mechanized systems, these are not so readily available in Africa’s largely manual-based cropping systems. Further, farming systems in developed world agriculture are geared to exploiting the gains in soil moisture obtainable under CA (Harrington and Erenstein, 2005; Wall, 2007), whereas this is not the case in Africa’s low input cropping systems where water productivity is perennially low (Rockstrom et al., 2009). Promotion of CA in Africa represents significant practice change for farmers in terms of their weed control and use of crop residues (Erenstein, 2002; Wall, 2007). It also assumes the financial capacity of farmers, along with well-developed

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market access and incentives, to invest in the requisite technologies of improved seed, herbicides and fertilizer. As Bolliger et al. (2006) noted, CA practices offer huge advantages for farmers with sufficient capital and opportunity, but this is a ‘fortunate few’ farmers in sub-Saharan Africa. There are further technical issues affecting adoption rates of CA in Africa. The most reliable effects of CA (reduced soil loss and increased soil organic matter content) and benefits to farmers’ yields accrue with long delays – mostly longer than 5 years (Derpsch, 2005; Erenstein et al., 2012). Giller et al. (2009) pointed out that farmers need immediate returns to investment when considering adoption of CA practices and therefore the long-term benefit is a major hurdle for adoption. More recently, Edmeades et al. (2012) observed that, in general, uptake of CA techniques has been quite limited in Africa, primarily because of competing uses for stover as forage, low stover production, external input requirements and lack of suitable machinery or machinery services. Earlier studies have suggested that CA increased labor inputs for weed control when herbicides are unavailable (Muliokela et al., 2001) and Andersson and Giller (2012) point out that, under these circumstances, CA shifts the peak labor demand from sowing to weeding. Conservation agriculture does not work if the residues are not retained (Theirfelder and Wall, 2011). Hence the strong interdependence between reduced tillage and mulching becomes an important constraint to the adoption of residue retention practices in Africa given the overwhelmingly low N status of most maizecropping systems. It has been acknowledged that better N management is required particularly in the first years of conversion from tillage-based agriculture to CA (Theirfelder and Wall, 2011). Wall (2007) estimated that an additional 20 kg N ha−1needs to be applied to CA crops to offset the reduction in available nitrogen in untilled soil, although this would decline as soil organic matter and N mineralization increases. However, Africa

is chronically low in fertilizer use, with an average annual below 10 kg N ha−1 (Wichelns, 2006), despite the generally infertile soils. Given the large diversity in levels of farmers’ endowment and livelihood strategies and the multiplicity of existing trade-offs (Giller et al., 2011), simplification and integration at the household or farming system is required. Here we propose that, depending on farmers’ present performance and investment, three basic steps could be identified together with supporting options for farmers to engage and participate in markets to sustainable intensity productivity, increase food production and reduce poverty. 3.1.1  Step 1: Improving agronomic practice The agronomic output of under-performing growers (‘hanging-in farmers’, Dorward et al., 2009) needs to be improved. This can be achieved by working with smallholder farmers so that they practice, learn and improve agronomic practices including the use of improved or good quality seed, improved sowing techniques, better match plant densities and arrangements to local conditions (rainfall, soil type, slope), the preparation and use of manures and composts using local ingredients, and in-crop weed control. These are just a few simple agronomic practices that would require no additional investment and are likely to lift yields and reduce yield variability and risk for approximately 30–40% of the population of farmers across eastern and southern Africa. Similar observations were made by Edmeades et al. (2012) who recommended that emphasis should be placed on promoting component technologies that provide increases in productivity and income in the short term, compared to technologies that are expected to provide benefits in the long term. 3.1.2  Step 2: Increasing farmers’ investment This step involves working with the better performing farmers (‘stepping up farmers’, Dorward et al., 2009) to evaluate the benefits and trade-offs (e.g. risks) from alternative

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4 Methods

investments given a limited availability of resources such as fertilizers, herbicides, stubble from previous crops. Gradually incorporating more CA component technologies might be an option for this group of farmers. This approach is likely to provide significant benefits to approximately 60–70% of the farming population across eastern and southern Africa. 3.1.3  Step 3: Identifying and supporting transformational changes and engagement with markets Step 3 consists of supporting leading farmers, i.e. those that already apply significant investment in their production system. This will include the promotion of collective bargaining arrangements that improve access to input and output markets, explore options to access credit to invest in transformational changes, for example, machinery, new and more innovative practices and specialization in crops and livestock activities, identifying new products or value adding to existing products that are likely to increase returns with minimal additional risk. Even though this is likely only to provide significant benefits to 5–10% of the farming population, this fraction of the population can be expected to have the required capital and motivation to initiate small agribusinesses that will benefit larger numbers of farmers.

4 METHODS 4.1  Participatory approaches to identify relevant and actionable interventions in stepping stone approaches: Case studies from Tanzania and Mozambique Following learnings from previous works (Carberry et al., 2004; Whitbread et al., 2010), surveys and participatory crop modeling workshops were conducted with farmers from three villages, one in Tanzania (Mandela), and two in Mozambique (Marera and Rotanda). Yields of crops from 13 recent cropping seasons (1999–2011

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in Mandela, 2001–2013 in Mozambique) were collected – i.e. farmers’ recall. At Mandela, 10 farmers provided a total of 90 maize yield estimates (five farmers all 13 seasons, three farmers, 5–7 seasons and two farmers, 4 and 3 seasons). At Marera, six farmers provided 67 maize yields (four farmers, 13 seasons) and at Rotanda, seven farmers provided 74 estimates (four farmers, 13 seasons). At each site, farmers cropped within an area no further away from each other than 10 km, therefore farmers’ yields could be related to in-crop rainfall records from nearby weather stations for each of the seasons, i.e. Ilonga Research Station, 50 km from Mandela, Chimoio airport, 30 km from Marera and Rotanda Met station at a nearby village. APSIM (Keating et al., 2003) was then used to simulate maize yields using representative management and soil descriptions obtained from farmers’ resource allocation maps (Defoer and Budelman, 2000) to parameterize the model. At Mandela, we used farmer’s management applied in 2011 to a maize field to calibrate the model to the farmer’s yield achieved in that season. The 2011 management was then used to simulate previous seasons back to 1999; this means we tested the same crop management across seasons and assumed that no significant changes have occurred in crop management during that period. Parameters for an improved open pollinated variety were used in all cases. Yield distributions from farmer surveys and model outputs were compared to evaluate the performance of the model. Similar steps were adopted at Marera and Rotanda (data not shown).

4.2  Scaling-out stepping-stone approaches for the sustainable intensification of agriculture across contrasting environments in eastern and southern Africa Given the large environmental variation (Waddington et al., 2010), APSIM was used to

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evaluate maize responses to alternative management interventions for 19 growing environments in Ethiopia, Kenya, Tanzania, Malawi, Mozambique and Zimbabwe. To assess farmers’ investments in a stepping-stone approach, five management interventions were simulated: 1. Weed competition removed through effective use of knock-down and pre-emergent herbicides. 2. Application of a low rate of N fertilizer as starter (50 kg of 12:24:12 NPK ha−1), and top-dressing of nitrogen at 9 leaves (25 kg urea ha−1). 3. Application of a recommended rate of N fertilizer as starter (100 kg ha−1 of 12:24:12 NPK), and a top-dressing of nitrogen at 9 leaves (75 kg urea ha−1). 4. Application of maize residues as a mulch (1500 kg ha−1) with moderate N content (0.66%N, C:N ratio = 60:1) at sowing, and the low rate of N fertilizer. This amount of residue provides approximately 40% groundcover at sowing, and decomposes during the 120-day crop cycle to less than 100 kg ha−1 at maturity. 5. Application of maize residues (1500 kg ha−1) with a moderate N content at sowing and the recommended rate of N fertilizer.

Fixed parameters included maximum rooting depth = 120 cm, maximum plant available water = 108 mm, soil organic carbon in the 0–10 cm layer = 1.1%, plant population = 4 plants m−2, row spacing = 75 cm and crop duration = 120 d. For each season and site, maize was sown on the earliest rainfall opportunity within nominated sowing windows. Currently, farmers favor improved open pollinated varieties (OPV) and even hybrids, with less frequent use of landraces. To capture the interaction of cultivar with management and rainfall, three sets of genetic parameters related to yield potential were used. Maximum grain number per ear was set to 400 for landrace, 450 for OPV and 550 for hybrid. Maximum grain growth rate was set to 6, 8 and 9 mg grain−1 d−1, respectively. These have been married to four sets of crop growth and development coefficients required for constant crop duration across the temperature and stress conditions of the various sites (Dimes et al., 2013).

5 RESULTS 5.1  Model performance Figure 5.3 compares the actual and simulated frequency distribution of maize yields for the period 1999–2011 at Mandela, Tanzania. FIG. 5.3  Maize yield distributions derived from farmer surveyed data and simulated yields using APSIM for the period 1999 to 2011 at Mandela, Tanzania.

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5 Results

Simulated and observed yields were also discussed with the participating farmers (Fig. 5.4). At Mandela, farmers agreed that the simulated maize yield was very close to the farmer’s yield in 2011, i.e. the year used for model calibration, and that the average for the field across years (solicited during the discussion, see asterisks in Fig. 5.4) was also close to the average of the simulated yields. Disagreements between simulated and actual yields were observed for some seasons. Possible causes of disagreement were discussed with the farmer, in particular for 1999, a year affected by a severe drought. Using the management described for 2011, the model simulated a higher than observed yield in 1999, and yield failure in 2000 (Fig. 5.4). The survey showed that two farmers from the same village recalled higher yields in 1999 than in 2000, while another two farmers reported the reverse, and one other farmer reported no difference between these seasons. We concluded that the discrepancy between the simulated and

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the farmer’s recalled yield in 1999 was probably management related (e.g. unfavorable sowing date, poor weed control, etc.). Figure 5.4 also shows modeled results for the alternative management practices where 50 kg urea ha−1 was applied in combination with two in-crop weeding events and a single weeding event scenario with no fertilizer applied. The combination of two in-crop weeding events and the application of urea increased maize yield across seasons by 140% compared to farmers’ practice. In contrast, when only a single in-crop weeding event was simulated (indicated as a practice for some by both farmers and extension officers during the meeting) maize yield was zero in 4 seasons, and about 75% lower than farmers’ practice across seasons. Before presenting results, the farmer was asked to estimate the yield in 2011 if he had applied the urea fertilizer. His response of between 2000 and 2250 kg ha−1, (8–9 bags acre−1 in Fig. 5.2) was in agreement with the simulated 2250 kg ha−1. This

FIG. 5.4  Maize grain yield (100-kg bags acre−1, bar chart) simulated for farmer’s field and management at Mandela. Numbers at the bottom show simulated yield (bags acre−1) for two alternative management practices: +Fert: 50 kg urea ha−1, and one weeding in crop. Management related to an OPV sown on February 10, 2011 in a clay loam soil, with manual weed control on March 10 and 31 and no applied fertilizer or manure. The asterisks indicate yield data obtained from the farmer after simulations were performed (except in 2011 which was obtained initially from the farmer and used to calibrate the model soil inputs). The cross-hatched bar is the mean yield of 13 seasons simulated.

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suggested that these farmers understood the yield benefits of fertilizer, but chose not to use it given cash constraints at the time of buying.

5.2  Farmers’ performance and steppingstone approaches for the sustainable intensification of agriculture Figure 5.5 shows the relationship between farmers’ yield estimates and seasonal rainfall for Mandela, Marera and Rotanda. Farmers at Mandela and Marera did not use fertilizer, whereas some growers used fertilizers in Rotanda. A farmer from Rotanda indicated seasons where he used and did not use fertilizer (highlighted in Fig. 5.5). Mandela in Tanzania had lower cropping season average rainfall (607 mm) and large seasonto-season variability (356 to 1033 mm). Marera and Rotanda in Mozambique had similar cropping season rainfall (i.e. 758 mm and 774 mm, respectively) and the lowest cropping season rainfall was in excess of 500 mm. Fifty-six percent of all the yield estimates (n = 231) in Figure 5.5 were below 1000 kg ha−1, and 73% showed rainfall water-use efficiencies (i.e. yield per unit seasonal rainfall) below 2 kg ha−1 mm−1. Depending on cropping areas by individual landholders, such low water productivity is a major factor contributing to food insecurity. The highest rainfall-use efficiency was 9.5 kg ha−1 mm−1 and was achieved at Rotanda where fertilizer use is common. The benefits of fertilizer use on rainfall-use efficiency are apparent also from the farmer that did use fertilizer in some seasons (2.1–4.7 kg ha−1 mm−1) but not in others (1.2–2.3 kg ha−1 mm−1). Yield–rainfall relationships in unfertilized crops on very similar soils in the same village were highly scattered (Fig. 5.6a). It is interesting to note that there are three distinct groups of farmers under the same agroecological conditions: (1) those who consistently achieve up to 4.1 kg grain ha−1 mm−1; (2) those that consistently fail to produce a crop; and (3) those that consistently achieve average yields. This highlights the

FIG. 5.5  Relationship between farmer yield estimates and rainfall for up to 13 consecutive cropping seasons at Mandela (eastern Tanzania), Marera and Rotanda (Mozambique). One farmer at Rotanda indicated seasons with and without fertilizer use. The line represents the maximum rainfall use efficiency (9.5 kg ha−1 mm−1).

benefits to be obtained from improving the performance of the poor and average performing farmers and from working with the better farmers to increase investment in inputs and manage any associated risk. Figure 5.6b shows the simulated maize yields using farmers’ management at Mandela including open pollinated varieties, allowing for weed competition, and lack of fertilizers. In these simulations, we used recommended plant populations and row configurations. The envelope lines are for the highest and ‘best of the rest’ efficiencies drawn in Figure 5.6a. As seasonal rainfall at Mandela extends to below 400 mm (Fig. 5.6), maize yields would benefit from moisture conservation practices such as mulching. However, application of maize residues with low N content depressed simulated yields significantly (Fig. 5.7), due to immobilization of soil N associated with decomposition of residues. In contrast, the same amount of legume residues (C:N ratio = 20:1) increased maize yields to above 2000 kg ha−1 in all seasons due to the input of organic N in these

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FIG. 5.6  Relationships between yield and seasonal rainfall at Mandela, eastern Tanzania. (a) Farmer yield estimates (1999– 2011, 10 farmers), (b) simulated yield (1986–2011). The lines represent the boundary functions for the relationship between grain yield and annual rainfall for the best farmer, and for the best of the remaining farmers. The rainfall water-use efficiencies are 4.1 kg ha−1 mm−1for highest and 2.5 kg ha−1 mm−1 for ‘best of the rest’.

unfertilized maize crops. Application of 80 kg N ha−1 along with maize residues further increased yield and rainfall-use efficiency (Fig. 5.7), also showing benefits from moisture conservation during the driest seasons. With a simulated yield ceiling of approximately 4 t ha−1achieved with maize residue and N fertilizer, the average rainfall-use efficiency was 7.1 kg ha−1 mm−1 (Fig. 5.7). At Rotanda, maximum yield was 6 t ha−1 and rainfall-use efficiency of 9.5 kg ha−1 mm−1. What are, therefore, the management steps to increase yield and rainfall-use efficiency to match those at Rotanda? Simulations indicate that weed control with herbicides could increase yield from 860 kg ha−1 (baseline) to 1670 kg ha−1 and rainfall-use efficiency from 1.6 to 3.1 kg ha −  -1 mm−1 (Fig. 5.8). In the absence of weed competition, hybrid seed can increase the yield and efficiency by a further 32% (1950 kg ha−1, 3.6 kg ha−1 mm−1), but a small dose of fertilizer (20 kg N ha−1) with open pollinated variety increased simulated yield and productivity by more than 50% (2620 kg ha−1 and 4.8 kg ha−1 mm−1) compared to open pollinated variety without fertilizer. If cash resources are not a constraint, a high dose of fertilizer (180 kg N ha−1) with hybrid seed

would more fully exploit the potential of the Mandela environment (yield mostly greater than 6000 kg ha−1 and rainfall-use efficiency = 10.7 kg ha−1 mm−1, Fig. 5.8). However, unlike any of the

FIG. 5.7  Relationship between simulated maize yield and seasonal rainfall at Mandela for baseline farmer management. Comparison of yield responses to residues with low C:N ratio and no fertilizer (maize, C:N ratio = 80:1), high C:N ratio (cowpea, C:N ratio = 20:1) and high C:N ratio plus fertilizer (maize + 80 kg N ha−1) Lines have slopes of 4.1 (dashed) and 2.5 (solid) kg ha−1 mm−1 as derived from Figure 5.6a.

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FIG. 5.8  Relationship between simulated maize yield and seasonal rainfall at Mandela for open pollinated varieties (OPV) and hybrids with and without weed competition and varying rates of N fertilizer (0, 20 and 180 kg N ha−1). Lines have slopes of 4.1 (dashed), 2.5 (solid) and 15.9 (short dash) kg ha−1 mm−1.

previous interventions, there are also seasonal yields much less than 6000 kg ha−1 and as low as 600 kg ha−1 in a 600 mm season, indicating higher yield variability and production risk at this level of investment. In summary, the simulations showed that small additional investments (small additions of N fertilizer or legume residues, improved weed control) can improve the productivity of farmers while maintaining yield stability across the seasonal conditions at Mandela. Higher productivity is possible with higher investments (hybrid seed and high fertilizer N) but at the expense of increasing seasonal variability of yield and therefore returns on investment.

5.3  Scaling out the stepping-stone approach for the sustainable intensification of agriculture across contrasting agroecologies in eastern and southern Africa 5.3.1  Maize yield responses across rainfall environments Maize response to yield improvement technologies interacts strongly with rainfall amount

and distribution during the growing season as demonstrated by simulation of the high investment strategy at Mandela. Here, we examine how the step-wise uptake of crop management technologies applied to landrace, OPV and hybrid maize interacts solely with rainfall patterns (i.e. soil, plant population and spacing and crop duration held constant) across a range of semiarid to wet humid tropical environments in eastern and southern Africa. The baseline management is maize OPV with weed competition, no fertility inputs and no surface residues. The average yield across sites simulated for the baseline crop management was 630 kg ha−1 for the landrace1, 940 kg ha−1 for the OPV and 1260 kg ha−1 for the hybrid (Fig. 5.9). These are in line with the yields reported by farmers at Mandela and Marera, where fertilizer use was almost non-existent. To be expected, magnitude of the maize yield response to improved weed control, fertilizer and mulch was much less in the semiarid environments (Katumani and Bulawayo) compared to the more humid sites – approximately 40% less at the highest yield. The average percentage increase above the baseline yields for the technology interventions was relatively constant across the three cultivars, except for the hybrid with N inputs, where proportional increases were lower because of the higher baseline yield and/or interactions with water supply limiting yield (Table 5.2). The interaction with water supply was more apparent in the drier environments where the proportional responses of the hybrid to N inputs are much lower than those of the lower yielding 1

For this analysis, yields of the unimproved landrace were underestimated because a grain growth rate normally associated with longer crop duration (typically 140 days or longer for landraces) was married to a shorter crop cycle. This was necessitated by operational aspects of simulating the bimodal cropping systems as well as for control of the rainfall and crop growth period sampled across sites. Hence the results of the landrace here are to show how a low-yielding cultivar will respond to the yield improvement technologies and should not be used to compare the yield benefits of the improved seed.

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5 Results

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FIG. 5.9  Simulated average maize yields for 19 site-seasons using landrace, improved OPV and hybrid germplasm descriptions and their response to increasing inputs – herbicides to eliminate weed competition, N fertilizer and maize residues. 1 & 2 refers to seasons in bimodal rainfall environments. The baseline management is weed competition, no fertility inputs and no crop residues. Low N is 18 kg N ha−1, high N is 81 kg N ha−1, mulch = 1500 kg maize stubble ha−1 at sowing.

cultivars. On the other hand, eliminating weed competition in the drier climate was more beneficial (52% increase) compared to the wetter climates (40% increases). More notably, the proportional yield response to low N rate was higher in the drier (160%) than in the wetter environments

(140%). This is explained by the interaction of water supply limiting soil N mineralization in the drier environments, thereby exacerbating the crop nitrogen deficit. The application of small N doses is therefore more beneficial in overcoming the N constraint in these environments.

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5.  RAISING PRODUCTIVITY OF MAIZE-BASED CROPPING SYSTEMS IN EASTERN AND SOUTHERN AFRICA

TABLE 5.2  Average yield change (%) relative to baseline management for maize cultivars and technology interventions in humid tropic and semi-arid environments Environment Humid tropic

Semi-arid

Technology

Landrace

OPV

Hybrid

Landrace

OPV

Hybrid

Herbicides

40

40

41

52

52

52

Low N

139

139

124

162

163

140

High N**

314

315

304

277

277

243

Low N + Mulch

66

66

65

106

107

102

High N + Mulch

305

305

285

318

316

268

The baseline management is weed competition, no fertilizer and no surface residues. Low N is 18 kg N ha−1, High N is 81 kg N ha−1, mulch = 1500 kg maize stubble ha−1 at sowing. ** Note the declining marginal response to fertilizer use – the high N rate is 4.5 times the input of the low rate, while the yield advantage is less than 3.2 times the yield of the baseline management (0N inputs).

Overall, the tested technologies increased simulated maize yields across the environments and the biggest source of the yield gain was nitrogen fertilizer. However, with low nitrogen, even in the drier semi-arid environments, maize residues reduced crop yields due to N immobilization. With high nitrogen input, there were small improvements due to moisture conservation at the semi-arid sites but these were largely absent (on average) in the more humid locations.

6  DISCUSSION AND CONCLUSIONS High water–nitrogen co-limitation is associated with smaller yield gaps and higher wateruse efficiencies in cereal crops in Mediterranean environments (Sadras, 2004; Cossani et al., 2010; Chapter 7). The basis of this concept is the work of Bloom et al. (1985) in developing the analogy that efficient use of resources in plant systems is akin to efficient allocation of capital resources in the economic sphere. The principle is that plant growth is maximized when resource limitations are balanced. For rain-fed systems

targeting high production and rainfall-use efficiency, this principle implies that nitrogen inputs need to be managed carefully to match water availability during crop growth cycle. Sinclair and Park (1993) also drew upon the work of Bloom et al. to explain why Liebig’s Law of the Minimum does not apply at higher production levels. The cropping systems analyzed by Sadras (2004), Cossani et al. (2010) and Chapter 7 typically have higher resource inputs and technical efficiencies (smaller yield gaps) than those of Africa’s smallholder maize cropping systems (Carberry et al., 2013). The water–N co-limitation index ranges from 1 when both stresses are balanced to zero when a single stress dominates (Box 7.1 in Chapter 7). We have not explored co-limitation, but our results suggest that for the humid tropic sites in this study the water–N colimitation index would be close to zero, i.e. crop growth is limited almost entirely by nitrogen availability. As Sinclair and Park (1993) concede, the limiting factor paradigm is applicable and useful where an extreme and obvious stress decreases yield. Hence, in the context of agricultural development in Africa’s low input low output

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6  Discussion and conclusions

cropping systems, Liebig’s Law of the Minimum does retain high relevance. The surveys at Mandela and Marera demonstrate the extent of farmers’ current low maize yields and rainfall-use efficiency in these wet environments. The benefits of fertilizer in raising efficiency are evident in the surveys from Rotanda where fertilizer use is more common, and in the simulations for Mandela. The broader simulation analysis across rainfall environments shows that, even in drier areas, maize will respond strongly to small N inputs in soils with organic carbon of 1.1% or lower. This is consistent with the empirical findings of Twomlow et al. (2010) in the semi-arid regions of southwest and western Zimbabwe. The modeled results suggest that, in these environments, soil N supply mediated by biological activity is limited by the frequent drying of the soil surface layers. Irrespective of germplasm and rainfall, application of small doses of N fertilizer is likely to result in the largest payoff to farmers. This was the basis of the small dose research reported by Twomlow et al. (2010) for the semi-arid tropic and these results suggest that it is applicable to ‘hanging in’ farmers’ fields in higher rainfall environments as well. However, improved weed management (and in many instances, plant stands) would need to be a first step, else the benefits of small fertilizer applications would be compromised. This is consistent with feedback from on-farm trial results, where farmers have responded to the labor saving and convenience of herbicides. Results reported here indicate that a 40% yield advantage could be possible if weed competition is largely eliminated in these N-starved cropping systems. Training of extension officers and farmers on effective use of herbicides and more widespread promotion of pre-emergent weedicide would be a priority in scaling-out this component of conservation agriculture. Although crop residue retention improves soil organic matter in the long term and protects the soil against erosion, maize crop residues also

107

have the capacity to immobilize soil nitrogen in the short term. The recent review by Grahmann et al. (2013) concluded that conservation agriculture has lower nitrogen-use efficiency than conventional systems, hence the need to adjust fertilization. The promotion of smaller doses of fertilizer without residues in Africa can be based on a number of logical arguments in pursuit of longer-term adoption of crop residue retention. First, resource-poor farmers are reluctant to invest in fertilizer; hence smaller doses will be more acceptable as a first step. Secondly, smaller doses of nitrogen and residue retention are incompatible in these N-poor cropping systems. Such negative effects and resultant lower returns on the fertilizer investment will discourage farmers from investing in this needed technology. Lastly, in low input mixed farming systems, stover production is low and there are competing uses for stover (Baudron et al., 2013). High yielding crops are therefore necessary to return adequate amounts of stover. Hence, it is logical that increasing the use of mineral fertilizers in Africa is the essential first step to wider adoption of residue retention. The alternative source of nitrogen would be biological fixation, but Africa’s smallholder farmers seemingly have even less incentive to invest in legumes as cropped areas are much less and yield gaps even larger than those of maize. Improved cultivars could raise farm production but, in the absence of fertilizer inputs, productivity gains will be limited (Fig. 5.8). Also, in rain-fed systems with yield above 3 t ha−1, the returns to higher investments become more uncertain across the range of seasonal rainfall in the drier environments (e.g. Mandela, Fig. 5.8, Table 5.2). For resource-poor farmers, hybrid seed is expensive, increases production risk and produces crops with high harvest index. Accordingly, maize hybrids would occupy a more distant role in intensification of current production systems and as a stepping-stone in the scaling-out of conservation agriculture in Africa (Fig. 5.10).

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5.  RAISING PRODUCTIVITY OF MAIZE-BASED CROPPING SYSTEMS IN EASTERN AND SOUTHERN AFRICA

FIG. 5.10  A possible technology sequence for testing with farmers for step-wise intensification of maize production systems in Eastern and Southern Africa.

Our analysis points to nitrogen inputs as the crucial step to raising the productivity of maize production in eastern and southern Africa. While benefits of conservation agriculture for crop production take up to 5 years and longer to emerge under well-managed conditions (Derpsch 2005; Erenstein et al., 2012), the FAO statistics reported by Tittonell and Giller (2013) show that fertilizer use in Africa has been almost static since the 1960s. Therein lays the real intensification challenge for most of subSaharan Africa because, as Keating et al. (2010) pointed out, unless ways are found to relieve the soil fertility constraint in Africa, efficient use of other natural and human resources will remain low.

Acknowledgements This work was part of the project “Sustainable Intensification of Maize-Legume Systems for Food Security in Eastern and Southern Africa (SIMLESA)”, funded by Australian Centre for International Agricultural Research (ACIAR). Daniel Rodriguez and Andries Potgieter are supported by the Queensland Alliance for Agriculture and Food Innovation (QAAFI), The University of Queensland, Australia.

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