Linking eartly actions on the ground to generate co-benefits through Climate-Smart Agriculture

Professor Paul Mapfumo

Soil Fertility Consortium for Southern Africa (SOFECSA) &

Department of Soil Science & Agricultural Engineering UNIVERSITY OF ZIMBABWE

University of Zimbabwe

� � Africa is a food deficit global zone but economies

remain primarily hinged on agriculture (includes forestry and fisheries)

� Crop and livestock production and natural ecosystems are intricately inter-dependent (but declining & challenging institutional and policy norms)

� Communities responding to multiple stress factors

plus climate change and climate variability

Introduction: Africa’s climate dependent production & livelihood systems


� � What does this imply:

�  Increased external inputs (fertilizers, chemicals, mechanization: how different from agricultural growth pathways of the past e.g. in Europe & Asia)

� Accelerated deforestation, loss of grasslands to cropping, encroachment into fragile ecosystems and broad scale natural resources depletion

�  Increased emissions and less carbon sinks??? � How have farmers responded and do they perceive a

scope for alternative growth pathways

Introduction: Africa requires accelerated agricultural growth


� � New paradigms in the architectural (structural and functional)

designs of crop and livestock production systems � New regimes of ecosystems services � Socio-ecological transformations providing win-win-win agro-

ecological systems (agricultural intensification-sustainable natural resources management - diversified livelihood options)

� Need to expand opportunity horizons for enhanced adaptive capacity and livelihood benefits beyond farms (e.g. communities living beyond the reach of markets, limiting scope for science, technology assimilation and innovation

Increasing calls for more productive, efficient and resilient farming systems


� � Consider core CSA elements (FAO): increasing productivity;

resilience (adaptation), greenhouse gases mitigation, and enhanced achievement of national food security and development goals

� Conservation Agriculture (CA) -based technologies �  Integrated Soil Fertility Management (ISFM)- based technologies � Agroforestry-based technologies (fertilizer trees) � Community Based Natural Resource Management (CBNRM) � How can these be adapted and integrated into farming systems

to yield greater benefits (SOFECSA’s emerging concept of Learning Centres)

Responding through CSA: What is on the ground?


�

Demand for CSA: Do farmers perceive a changing climate?

� Shortening rainfall seasons: late start and/or early end of the season

� Increasing number of hot days in a season � Worsening within season rainfall distribution � Farmers relying more on indigenous knowledge for

agricultural decision-making but indicators often also climate sensitive

� Poor access to climate information presents a major challenge for adaptation

What has been measured?

Please cite this article in press as: Rurinda, J., et al., Managing soil fertility to adapt to rainfall variability in smallholder cropping systemsin Zimbabwe. Field Crops Res. (2013), http://dx.doi.org/10.1016/j.fcr.2013.08.012

ARTICLE IN PRESSG ModelFIELD-6059; No. of Pages 15

J. Rurinda et al. / Field Crops Research xxx (2013) xxx–xxx 7

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(a) (b) Period: 1962-2010

(c) Period: 1962 - 1989 (d) Period: 1990 - 2010

Fig. 4. (A) Rainfall analysis outputs for 48 years (1962–2009) in Hwedza: (a) variation in total annual seasonal rainfall (tau-b = −.021, P = .831), (b) number of rain days perseason (tau-b = −.234, P = .017), (c) date of start of season (using 48 mm in at least two rainy days out of ten consecutive days) (tau-b = .107, P = .296), and (d) relationshipbetween date of the start of rainy season and length of rainy season (tau-b = −.365, P = .003). (B) Rainfall analysis outputs for 48 years (1962–2010) in Hwedza: (a) number ofrainy pentads per growing season (16 pentads indicated by the dashed line is the threshold for a good rainfall distribution within a season, and also is the long-term averagefor Hwedza), (b) probability of dry spells per growing season for period 1962–2010, (c) probability of dry spells per growing season for period 1962–1989, and (d) probabilityof dry spells per growing season for period 1990–2010.

•  Decreasing rainy days per season

•  Late start of the rains

•  Decreasing season length

•  No significant change in total rainfall

•  Consistent with farmer observations

Rurinda et al., 2013

CA Options: Both tillage and soil fertility management options significant: no interaction

26 kg P ha-1 + 90 kg N ha

-1

7 t ha-1 cattle

manure + 26 kg P ha-1 + 90 kg N ha

-1

45 kg N ha-1 + 14 kg P ha

-1

26 kg P ha-1 + 90 kg N ha

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7 t ha-1 cattle

manure + 26 kg P ha-1 + 90 kg N ha

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45 kg N ha-1 + 14 kg P ha

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(a) Degraded field

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4(b) Relatively fertile field

Conventioanl Ripping Basins

Soil fertility option P=0.04*Tillage P=0.02*Soil fertility option x Tillage P=0.39ns

Soil fertility option P=0.02***Tillage P=0.03**Soil fertility option x Tillage P=0.51ns

!(Source: ABACO Project)


�

Farmers indirectly challenging CA principles

Some key CA lessons, with farmers in Zimbabwe

i.  CA is mainly practised by farmers without cattle or draught power.

ii.  The main objective of farmers in practising CA is to increase crop productivity in the face of climate change

iii.  Farmers seeking to increase fertilization rates in response to declining soil productivity

iv.  Men prefer use of herbicides compared to women farmers who mainly gave the disadvantages of herbicides

v.  Legumes are not compatible with farming basins vi.  Farmers consider CA as mainly basins, but prefer ripping

and other less labour demanding forms of CA


�

Introducing ISFM:Change in farmer-defined window for planting staple

crops



4 J. Rurinda et al. / Field Crops Research xxx (2013) xxx–xxx

Fig. 1. Farmer defined crop planting windows based on perceived long-term seasonal rainfall types and season’s rainfall quality in Makoni and Hwedza: (1) Planting windowsbased on long-term recall, (2) planting windows for the 2009/2010 season and (3) planting windows for the 2010/2011 season.

rainfall distribution is generally considered good when the numberof rainy pentads exceeds 15 (Lineham, 1983).

2.3. Potential agronomic adaptation options in response torainfall patterns

Informed by participatory analysis of rainfall patterns, aresearcher-managed field experiment was conducted in the2009/10 and 2010/11 cropping seasons in Makoni and Hwedzato assess the effects of maize cultivar, planting date, and fertiliza-tion. In each study site, the field selected for the experiment waspreviously left fallow prior to the establishment. Three maize cul-tivars, three planting dates, and three fertilization rates were laidout in a split-plot block design with three replications per treat-ment. Planting date was assigned to the main plot, and fertilizationrate × maize cultivar sub-plots were randomized within the mainplot.

The three maize cultivars were: SC 403 (131 days to maturity),SC 513 (137 days to maturity) and SC 635 (142 days to maturity).The three planting dates were chosen based on farmers’ plant-ing windows as investigated in earlier studies (Mtambanengweet al., 2012). In both seasons, however, the start of the rainy seasonwas delayed, differing from farmers’ long-term recall by about twoto four weeks (Fig. 1). Consequently, the planting windows werechanged in each season in consultation with farmers so as to matcheach season’s rainfall pattern (Fig. 1). In the 2009/10 season theearly planting was delayed by three days, whereas in the 2010/11season by about two weeks (Fig. 1). Although the rain started earlierduring the 2010/11 season than in the 2009/10 season, it was char-acterized by very early season dry spells (false start to the season)(Fig. 2). In Makoni, the actual planting dates were 23 November forearly planting date, 14 December for normal, and 7 January for late,in the 2009/10 season. In the 2010/11 season the planting dateswere 5 December for early planting date, 18 December for nor-mal, and 8 January for late. In Hwedza, the actual planting dateswere 24 November for early planting date, 15 December for nor-mal, and 6 January for late, in the 2009/10 season. In the 2010/11season, the planting dates were 4 December, 17 December and 7January.

The three fertilization rates were: a control (unfertilized), lowrate (35 kg N ha−1, 14 kg P ha−1, 3 t ha−1 manure, on a dry weightbasis) and high rate (90 kg N ha−1, 26 kg P ha−1, 7 t ha−1 manure, ona dry weight basis). Fertilization rates were derived based on cropdemand for a target maize yield for farmers of different resourceendowment (Mtambanengwe and Mapfumo, 2009). The lowestrate represents typical rates used by resource-constrained farm-ers, and highest rate is for yield maximization sometimes used bythe better resource-endowed farmers.

Following farmers’ common practices, the land was ploughed toa depth of 0.20 m and ridged using draught animals at the start of

the rainy season for all planting windows. The experimental areawas almost bare at ploughing in each site. Weeds were removedat normal and late plantings using a hand hoe. Each sub-plot hada gross area with dimensions measuring 4.5 m × 7 m and a plantspacing of 0.75 m × 0.30 m. Basal compound D fertilizer with com-position: 7% N, 14% P2O5, 7% K2O, was applied at planting. Nitrogen,topdressing was applied as ammonium nitrate (34.5% N) in twosplits, at 4 weeks (40%) and 6 weeks (60%) after emergence. Weed-ing was done twice manually using a hand hoe. No major pests ordiseases were observed throughout the experiment. Daily rainfallwas recorded at each site using a rain gauge within a range of 300 mof field experiment in Makoni, and 100 m in Hwedza.

2.4. Laboratory and field measurements

2.4.1. Soil and manure analysisFive soil samples (0–20 m) were randomly collected from the

experimental area before the experiment was established. Soilswere bulked to make a composite sample, air-dried, and sievedthrough a 2 mm sieve for analysis of soil texture (hydrometermethod), pH (0.01 M CaCl2), organic carbon (Walkley Black), totalnitrogen (micro-Kjeldahl), available phosphorus (modified Oslen)as described by Anderson and Ingram (1993) (Table 1). The totalnitrogen in manure used in the experiment was 0.8% in Makoni,and 0.7% in Hwedza. The available P in manure was 0.3% in Makoni,and 0.26% in Hwedza.

2.4.2. Maize development, grain and stover yieldsLeaf area index (LAI) was estimated at 3, 6, 9 and 12 weeks after

crop emergence. Five plants were randomly selected and taggedfrom two rows in each of the sub-plots. Individual leaf area was esti-mated non-destructively from leaf length (l, cm), from the collar tothe tip of fully expanded leaves, and from where a leaf could be seenin the whorl of expanding leaves to the tip; and leaf width (w, cm)at the widest point. Senesced leaves (50% or more of leaf having lostgreen colour) were not measured. Total plant leaf area was calcu-lated by summing the products (l × w) of each leaf from a plant andmultiplying the total by 0.73 (leaf area = !(l × w)(0.73)) (Mckee,1964). Green LAI was then calculated as the sum of the areas ofgreen leaves per unit area occupied by the plants (m2 leaf m−2 ofland).

Maize grain and stover yields were determined at physiologicalmaturity from net-plots of 3 rows × 4.6 m. Grain yield was calcu-lated at 12.5% moisture content. Stover samples were oven dried at70 ◦C to constant weight before dry matter yield was determined.

2.5. Calculation of gross margins

Gross margins were calculated to assess the financial benefitsof different adaptation options. Costs of production included in the

(Mtambanengwe et al., 2012; Rurinda et al., 2013)

ISFM: Increasing farmer access to nutrient inputs will broaden scope for adaptation



J. Rurinda et al. / Field Crops Research xxx (2013) xxx–xxx 9

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Fig. 6. Maize grain yield in response to cultivar, planting date, and fertilization rate for (a) 2009/10 and (b) 2010/11 seasons in Makoni; and for (c) 2009/10 and (d) 2010/11seasons in Hwedza. Error bars represent SED for a = time of planting, b = fertilization rate, c = crop cultivar.

of effective rains led to a delay in planting of the early crop. Thensevere waterlogging for two weeks was followed by a prolongedmid-season dry spell of 3 weeks in the second half of the crop-ping season (Fig. 2). In the 2009/10 season in Hwedza, the longestdry spell of 19 days occurred in January, which farmers consid-ered common. But in the 2010/11 season the longest dry spell of24 days in Makoni and 21 days in Hwedza occurred in February(Fig. 2), coinciding with the critical flowering period of maize. Anal-ysis of dry spells over 48 years indicated that 15 out of 48 years hadsimilar rainfall patterns to 2010/11 season characterized mainlyby a prolonged dry spell of >20 days in February. This suggests

that farmers experience such rainfall patterns once in every threeyears.

The financial returns were positive for both seasons whenhigh amount of fertilizer was used although greater returns wereobtained in a relatively good rainfall season (Table 4). Analysis of thelong-term yield estimates indicated that without fertilization theprobability of achieving household food self-sufficiency was low,less than 0.05, even when the crop was planted early (Table 5).With fertilization the probability of achieving food self-sufficiencyincreased to about 0.6 for the low rate and 0.8 for the high rate forearly and normal plantings (Table 5). When planting was delayed

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(c) 2009/10 season, Hwedza (d) 2010/11 season, Hwedza

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Fig. 7. Maize dry stover yield in response to cultivar, planting date, and fertilization rate for (a) 2009/10 and (b) 2010/11 seasons in Makoni; and (c) 2009/10 and (d) 2010/11seasons in Hwedza. Error bars represent SED for a = time of planting, b = fertilization rate, c = crop cultivar.

Rurinda et al., 2013

Effectiveness of PAR & Learning Centres:

centres emerged as most significant (Table 5). This was most likely due to theirinteractive nature. Marsh et al. (2000) showed the merit of reducing information costsassociated with new technologies, citing increased contact with extension as animportant factor. In this study, learning meetings associated with the learning centreapproach enhanced interaction of farmers not only with extension but among eachother and other key service providers. Notable was the consistent participation of thesocially disadvantaged members of the community (the resource-constrained), whoare otherwise commonly known to exclude themselves from such initiatives. Theinteractions enabled farmers to access relevant technical support on ISFM, openingopportunities for innovation. Farmer experience was significant (Table 5), implyingfarmers depended more on experiential learning and that learning centres could playan important role in accelerating information access and providing windows ofexposure for practical use of such information. Contrary to other adoption studies inAfrica (for example, Mugwe et al., 2009), age and education level of household head

Agriculture shows, 14%

Learning-based farmer

meetings, 29%

Farmer exchange visits, 8%

Field days, 24%

Learning centres, 20%

Field demonstration centres, 5%

Figure 3. Knowledge and information sharing platforms on integrated soil fertility manage-ment used by smallholder farmers in Wedza district, Zimbabwe

Table 5. Logit regression analysis of factors influencing farmer access to ISFM informationand knowledge in Goto and Dendenyore wards, Wedza district, Zimbabwe

Access Coef. Std. Err. Z P!NZN

Age 0.0101347 0.237709 0.43 0.670Resource group 0.856826 0.4114688 0.21 0.835Education 0.2703595 0.3263016 0.83 0.407Farmer experience !0.0329374 0.0182999 !1.80 0.072*Learning centre visits 1.709311 0.6834812 2.50 0.012**Access to extension services 1.500532 0.7202555 2.08 0.037**-Cons !0.9114378 1.972156 !0.46 0.644

Notes: Log likelihood"!32.807761, LR Chi2 (6)"16.87, Prob!Chi2"0.0098, Pseudo R2"0.2045. Key:Significant at *10% and **5%

Access to ISFM Information in Rural Zimbabwe 11

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Promoting Integrated Soil Fertility Management (ISFM) as an adaptation option

Gwandu et al., 2013


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Figure 1. Identified sources of integrated soil fertility management information by farmers inDendenyore and Goto wards, Wedza district, Zimbabwe

Figure 2. Farmer prioritised sources of integrated soil fertility management information inDendenyore and Goto wards, Wedza district, Zimbabwe

8 T. Gwandu et al.

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Gwandu et al., 2013

Putting ISFM to work: Enhanced knowledge sharing around Learning Centres

AfJARE Vol 8 No 1 Mashavave et al.

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Note: Black circles indicate sources of ISFM information and knowledge, while grey squares indicate platforms for access to and sharing ISFM information)

Figure 1: Social network structural layout for learning alliance participants in Chinyika, Makoni District, Zimbabwe

The most outwardly projected platforms were exchange visits with local farmers (exch_visit_local,; external workshops (ext_workshops) and extension facilitated meetings (extn_meetings), suggesting that these platforms for access to and sharing of ISFM information were less preferred among these farmers. Learning centre-based meetings (lc_based_meetings) for joint learning on ISFM and climate change included participatory action planning (PAP) meetings, which usually are conducted during the pre-season months of September or early October, followed by the implementation of planned activities as the season commenced. Joint monitoring and evaluation then followed during the mid-season and post-season periods, from January to July/August. The activities generally proceeded through an iterative cycle of learning–action–reflection. Studies have shown that repeated interactions strengthen social coherence and trust within the group (e.g. Borgatti & Cross 2003). It is also much cheaper and easier to organise training and agricultural extension services for groups of farmers than for individual farmers (Hagmann et al. 1998). Field days drew participation from diverse groups within and outside the community, where ISFM and climate change information and knowledge were shared through poetry, songs and drama. Key informant interviews revealed that, as a result of depressed agricultural production, field days and agricultural shows had become non-existent before the inception of SOFECSA initiatives in the year 2007, and this could be attributed to declining soil fertility. In particular, maize grain yields were less than 2 t/ha. Beside field days and learning centres, the farmers also interacted through seed fairs, exchange visits with local farmers and agricultural shows. Recurrent interactions within learning alliances were also found to foster broader comprehension of key ISFM issues, improved collaboration for better solutions, as well as promote a market-oriented culture among smallholder farmers. These collective actions provided an opportunity for the farmers to pool scarce resources, as well as to reduce transaction costs in the acquisition of requisite ISFM inputs by cutting out middlemen. In addition, the collective acquisition of inputs increases the farmers’ bargaining power, which can contribute to lower production costs.

AfJARE Vol 8 No 1 Mashavave et al.

7

The network structure for non-participant farmers (Figure 2) had fewer dense connections (ties) compared to that for participant farmers (see Figure 1), suggesting weak collaboration within the network. Still, intergenerational knowledge was the most outwardly projected source of information. Within this network type, farmers’ knowledge is usually viewed as ‘know-how’ and not acceptable by scientific research standards (Hagmann et al. 1998). Other less common sources of information were universities and direct research, as evidenced by their projection and few connections. Extension was viewed as the medium through which research-based knowledge was passed on to farmers, as evidenced by the lack of direct links between the farmer and research. Innovation dissemination was mainly through national/private extension agents (Roux et al. 2006) or farmer unions. There were few spaces along the value chains where farmers, extension agents and other agro-service providers met regularly to collectively develop/share new knowledge and strategies. This subsequently limited the farmers’ ability to realise economies of scale and hindered the farmers from developing stable relationships with suppliers or traders. Currently, exchanges with local farmers were mostly incidental and consisted of informal dialogue devoid of sufficient information to constitute ‘purposeful interaction’. Generally, farmer-to-farmer interactions were along dimensions such as age, religion and gender, a characteristic known as homophily in social network analysis (McPherson et al. 2001; Leonard et al. 2008).

Note: Black circles indicate sources of ISFM information and knowledge, while grey squares indicate platforms for access to and sharing ISFM information) Figure 2: Structural layout for non-participant smallholder social network in Chinyika,

Makoni District, Zimbabwe Besides extension meetings, other identified platforms for access to and sharing of information included field days, agricultural shows, external workshops and Master Farmer Training Programmes being run by the national extension agency, AGRITEX. However, Master Farmer Training Programmes were the most isolated platform for access to and sharing of information and knowledge. Information on extension meetings would be conveyed through village chairpersons by verbal communication, mobile phones and/or school children. The composition of participants at field days was mostly farmers from within the community, with very few outsiders, hence such activities were rarely conducted in this particular area. Farmers in this network failed to organise themselves towards production and

Without Innovation Platforms

With Innovation Platforms

Mashavave et al., 2013

Current land use changes as farmers respond to multiple stress factors

Land cover changes in Dendenyore, Zimbabwe; a) 1972, b) 1989, c) 2011 •  Wetlands drying •  Land degradation •  Loss of productivity

Chagumaira et al.

Current land use changes as farmers respond to multiple stress factors

Land cover changes in Ushe Ward, Wedza, Zimbabwe; a) 1972, b) 1989, c) 2011 •  Expansion into woodland areas •  Land degradation •  Loss of non-timber products

Chagumaira et al.


�

Mazhanje (Kg fresh weight) per year

0 100 200 300 400

Resource Endowed

Intermediate

Resource Constrained

Consumed (SED =6.81) Sold (SED=13.95)

The resource constrained households rendered most vulnerable as the natural

resource base diminishes

Increased vulnerability of the socially disadvantaged

Increasing productivity as a major source of adaptation

Lear

ning

Cen

tres

: pla

tform

s fo

r inf

orm

atio

n, k

now

ledg

e sh

arin

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inno

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Improved yields

Targeting mechanisms for

reaching the vulnerable

Crop diversification

Timely access

to inputs

Household food security

Improved natural resource

management

Strategic household

grain reserves

Enhanced resilience to climate change & variability

Enhanced soil productivity

through ISFM technologies

Mechanisms for buffering communities from food insecurity

shocks: (i) Traditional - “Zunde

ramambo” concept (ii) Grain storage

systems

Output markets

Mapfumo et al., 2013

Reclaiming degraded lands: Where are the starting points

40

48 Figure 2. 49

Makoni

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'Green-start'

'Fertilizer-start'

'Manure-start'

'Soya-start'

'Litter-start'

Farmers' 'Rich' fie

ld

Farmers' 'Poor' fi

eld

Continuous unfertilized maize

Continuous unfertilized soyabean

'Green-start'

'Fertilizer-start'

'Soya-start'

'Manure-start'

'Litter-start'



'Green-start'

'Fertilizer-start'

'Manure-start'

'Soya-start'

'Litter-start'



Farmers' 'Rich' fie

ld

Farmers' 'Poor' fi

eld

'Green-start'

'Fertilizer-start'

'Soya-start'

'Manure-start'

'Litter-start'



(a) (b)

(c) (d)

a b c d

e

a

a a

b

b bc

c

c

d

d d

ee

e

50 51

52

Nezomba et al., 2014


� � Understanding the breath and depth of CSA at the practical level (beyond academic/research walls)

� Huge knowledge gaps between farmers and national policy makers versus research/development practitioners

�  What is Climate Smart? �  Applicability to Africa’s diverse farming systems? �  Effectiveness in reducing vulnerability of the rural poor? �  Improving resilience of socio-ecological systems?

� CSA suite of technologies/interventions as an external shock to current production systems, however poor (e.g increased emissions).

� Changing ‘landscapes’ of conflict versus collaboration; socio-economic alliances; land and natural resource use patterns

Responding through CSA: What are the challenges


� � There are a range of practices currently undertaken by farmers and promoted by diverse practitioners: these still need to be adapted to be really climate smart at scale’

� As policy makers open up to the CSA concept, there is a danger for policy before evidence: huge knowledge gaps still exist BIG OPPORTUNITY

� Currently demands for food and nutrition security by communities in Africa call for extraordinary measures to balance sustainable (but transformed) agricultural production systems and ecosystem services that yield benefits on climate change mitigation

� Africa will therefore require strategic CSA solutions that promote mitigation within an adaptation framework

Concluding remarks


� Thank You

Environment

Linking eartly actions on the ground to generate co-benefits through Climate-Smart Agriculture