Soil fertility management on small farms in Africa: evidence from Nakuru District, Kenya

Food Policy 27 (2002) 159–170 www.elsevier.com/locate/foodpol

Soil fertility management on small farms in Africa: evidence from Nakuru District, Kenya S.W. Omamo a,∗, J.C. Williams b, G.A. Obare c, N.N. Ndiwa d b

a International Service for National Agricultural Research, The Hague, The Netherlands Department of Agricultural and Resource Economics, University of California, Davis, and the Giannini Foundation for Agricultural Economics, Berkeley, CA, USA c Egerton University, Njoro, Kenya d International Livestock Research Institute (ILRI), Nairobi, Kenya

Received 1 August 2001; received in revised form 21 March 2002; accepted 21 March 2002

Abstract This paper uses data from a 1998 survey of farming households in Nakuru District, Kenya to explore factors influencing soil fertility management decisions of smallholder farmers in Africa. The modeling strategy builds on results of research in soil science that point to the joint determination of inorganic and organic soil nutrient stocks and flows on-farm. Farmers’ decisions on levels of inorganic and organic fertilizer use are hypothesized to be similarly mutually dependent, and to be further influenced by various farmer-specific socioeconomic factors. Econometric estimations indicate that once the effects of cropping patterns, farm-tomarket transport costs, and labor availability are taken into account, smallholder applications of inorganic and organic fertilizers appear to be substitutes. Implications for research and policy are drawn.  2002 Elsevier Science Ltd. All rights reserved. Keywords: Soil fertility management; Fertilizers; Smallholders; Africa

Small-scale farmers in Africa typically use few improved inputs (Kherallah et al., 2000). Prominent among these underutilized inputs are inorganic (chemical) fertilizers (Heisey and Mwangi, 1996; Mwangi, 1996; Naseem and Kelley, 1999), apparently with serious implications for soil nutrient stocks on the continent. Between 1960 and 1990, an average of 660 kilograms per hectare (kg/ha) of nitrogen (N), 75 ∗

Corresponding author. Tel.: +256-41-321751. E-mail address: [email protected] (S.W. Omamo).

0306-9192/02/$ - see front matter  2002 Elsevier Science Ltd. All rights reserved. PII: S 0 3 0 6 - 9 1 9 2 ( 0 2 ) 0 0 0 0 9 - X

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kg/ha of phosphorus (P), and 450 kg/ha of potassium (K) were lost from 200 million hectares of cultivated land in 37 African countries (Smaling et al., 1997). Current annual rates of nutrient losses are estimated to be 4.4 million tons of N, 0.5 million tons of P, and 3 million tons of K. These losses swamp nutrient additions from chemical fertilizer applications, which equal 0.8, 0.26, and 0.2 million tons of N, P, and K, respectively (Sanchez et al., 1997). The steady fall in Africa’s stock of soil nutrients appears to be linked to soil fertility management practices that are ill-suited to the relatively recent imperative of continuous cultivation under increasing population pressure (Quinones et al., 1997). But growing population pressure need not imply soil nutrient depletion. For instance, population densities in much of Asia are considerably higher than they are in most African countries. A key difference appears to be soil fertility management regimes that feature high rates of adoption and use of inorganic fertilizers (Heisey and Mwangi, 1996; FAO, 1999; Mwangi, 1996). Not only do grain yields in Asia average three times those in Africa, they are growing while those in Africa stagnate (FAO, 1999). In addition to underutilization of chemical fertilizers, also common on Africa’s small farms are, subsistence-oriented output mixes (e.g., Mwanaumo, 1999; Tegemeo, 1998).1 These production choices have been linked to high farm-to-market transport costs in smallholder farming regions (Goetz, 1993; Jayne, 1994; Omamo, 1998a, b). The idea that farmers’ soil fertility management decisions reflect their production choices has long been recognized (e.g., Binswanger and Rosenzweig, 1986). Yet analyses of soil fertility management in Africa typically fail to account for likely links between soil fertility depletion and factors related to the subsistenceoriented production patterns that dominate the continent’s rural landscape (e.g., Naseem and Kelley, 1999; Sanchez et al., 1997; Van Duivenboode, 1992; Woomer and Swift, 1994). A recent farm household survey in Nakuru District in Kenya’s Rift Valley region produced data that permit empirical exploration of some of these links (Obare, 2000). This paper develops a model that exploits these data to investigate smallholder soil fertility management in the region, focusing on relationships among farmers’ soil fertility management decisions and various farmer-specific socioeconomic factors. The next section describes the farmer survey and highlights key descriptive statistics. This is followed by a description of the model, the econometric estimation method and the results of the estimations. Implications and conclusions are then drawn. Survey and data Structured questionnaires were administered to 227 randomly selected farming households in four divisions of Nakuru District over a period of six months in 1998 1 Such production choices allow farmers to substitute own production for food “imports” from markets (Omamo, 1998b).

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(Obare, 2000). The households were sampled in clusters defined by agroecological potential and access to regional trading and administrative centers. Missing observations and errors in data entry meant that complete records were available for only 129 farmers.2 Table 1 presents several descriptive statistics for the 129 farmers. Average land holdings in the study region of about 7 acres per household (2 acres per resident household member) are greater than are those in other parts of Kenya with comparable agroecological conditions (CBS, 1995; Tegemeo, 1998). However, as in those zones, cropped areas dominate holdings, leaving little scope for fallowing (“pasture area” in Table 1). Additions to soil nutrients thus are key to soil fertility management. Indeed, all 129 farmers in the dataset used fertilizer in 1998, but with wide variation in use-rates. Data on trends in soil nutrient stocks are not available for the study area. But the overall and per-acre rates of inorganic and organic fertilizer use—38 kg/acre and 319 kg/acre, respectively—fall well below those recommended for the major crops in the region (Jaetzold and Schmidt, 1984; KARI, 1998). Despite soil and climatic conditions that favor a range of high-value cash-crops (such as cabbage, kale, carrots, pyrethrum), food-crops with much lower market

Table 1 Selected farmer characteristics (n=129) Mean Farm size Cropped area (acres) Pasture area (acres) Household size (persons in residence) Acres per resident Total fertilizer used (kg) Fertilizer use/acre (kg) Total manure used (kg) Manure use/acre (kg) Share of land under food-crops Distance to nearest market center (km) Time to nearest market center (min) Access cost (Kshs/km)

6.80 4.52 1.42 8.08 2.05 168.01 38.27 1248.84 318.62 0.51 15.62 82.43 361.55

SD 5.39 3.13 1.86 2.97 2.01 165.96 22.21 1193.22 268.62 0.20 17.71 86.43 403.45

Min

Max

0.80 0.60 0.00 1.00 0.04 10.00 1.91 100.00 19.35 0.03 1.00 6.00 15.98

30.00 17.00 11.00 20.00 14.61 857.50 150.00 5000.00 1200.00 0.94 62.50 420.00 2133.33

Source: Unpublished survey data partially reported in Obare (2000).

2

Several farmers were unable or unwilling to answer certain questions. Errors in data entry were taken to be those with unreasonably high or low values, which, further, markedly changed summary statistics for the whole data set. Additional fieldwork to verify or correct these figures was not possible.

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values (such as maize and potatoes) are prominent in production patterns.3 The average farm is sited over 15 kilometers away from a regional market center. Accounting for distance, road type, and most common transport modes on those road types (and their direct and opportunity costs of usage) yields an average traveling time between homesteads and the nearest market center of well over one hour, and an average access cost of over 360 shillings per kilometer (which at that time was about $6/km).

Model Soil scientists have established that stocks and flows of inorganic and organic soil nutrients determine and reflect each other—i.e., they are jointly dependent (e.g., Palm et al., 1997; Woomer and Swift, 1994). These interactions occur in fields devoted to particular crops using given cultural practices (Lynam, 1994). Technologies that aim to enhance soil fertility by, say, increasing soil organic matter likely involve activities that alter sources and uses of both inorganic and organic nutrients. Biophysical models of cropping relate plant growth and yield to climate, soil, plant genotype, and management factors (Keating et al., 1992; Saka and Haque, 1993). Detailed sub-models of crop physiological responses to soil and environmental conditions capture the processes through which stable and labile (volatile) forms of organic and inorganic soil nutrients enter and leave root zones over the course of production cycles. A complete model of soil fertility management would integrate such biophysical models with models of farmer production, consumption, and trade in crop and animal products (e.g., Babu et al., 1995). Empirical estimation would require highly detailed time series data on a range of variables. For example, quantifying the carry-over effects of organic nitrogen added, say, through the tree component of an agroforestry system would require collection of cyclic information on humus and other indications of soil fertility, plus data on inorganic and organic fertilizers added to given areas within cropped fields, plus the associated impacts on yields and income (Babu et al., 1995). A simpler, more direct and less data-intensive approach was pursued here. Recent farmer-participatory research in Africa indicates that most farmers have accumulated significant knowledge and understanding of the fertility characteristics of the soils on their farms (e.g., Farrington, 1998; ILEIA, 2001; Peterson, 1998). Their decisions regarding adoption and use of inorganic and organic fertilizers reflect this knowledge (e.g., Peterson, 1998). These decisions thus can be viewed as “summary variables”

3 The distinction between “cash-crops” and “food-crops” follows that suggested in Omamo (1998a and b). “Cash-crops” are those that have small shares in household expenditures and high market values relative to farm-to-market transaction costs (in this case these are transport costs). For “food-crops,” the opposite relationships hold. Note, however, that a household expenditure survey was not carried out. Assessments of shares of given items in expenditures were based on answers to questions about quantities of given produced goods consumed on-farm relative to marketed quantities.

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for the numerous interacting biophysical factors and processes that they determine and reflect, allowing these factors and processes to be left outside formal models.4 Given that all households in the dataset were fertilizer users, the aim of the econometric estimation was not to explore the factors influencing fertilizer adoption but rather those affecting fertilizer utilization levels. The approach was further motivated by the finding that while there is a positive and significant direct relationship between levels of chemical fertilizer and organic manure use in the study area, the relationship is negative when corrected for farm-to-market transport costs (Obare, 2000). This finding pointed to a deeper relationship between inorganic and organic fertilizer use in the region, which likely depended not only on access costs but also on other household-specific variables. The strategy thus was to model inorganic fertilizer and manure use as jointly dependent (i.e., endogenous) “summary variables” of underlying biophysical processes, further influenced by a number of exogenous variables. In generalized form, the hypothesized model was as follows: FertilizerUse ⫽ F[ManureUse(FertilizerUse),exogenousvariables] If the suggested model were correct, parameter estimates emerging from ordinary least squares (OLS) estimation procedures that ignored the potential endogeneity (joint dependence) of fertilizer and manure use would be inaccurate.5 With the two hypothesized endogenous variables, two-stage least squares (2SLS) estimation would be more suitable.6

Results The dataset presented the following potential variables for inclusion as explanatory variables: household age distributions and education levels; cropping patterns; hired

4 Similar assumptions have been used to bring structure and tractability to empirical studies of livestock disease control in Africa (e.g., Omamo et al., 2001; Swallow et al., 1995). The technical effectiveness of alternative disease control technologies (e.g., chemotherapy, vector control, and breeding for disease resistance) varies according to interactions among such factors as vector and parasite species, topography, natural vegetation types, livestock breeds, livestock and human population distributions and densities, conditions in factor and product markets, and agricultural production systems. Rather than attempt to explicitly integrate these several factors into the analysis, one can assume that variables describing farmers’ perceptions of the risk and severity of diseases in their localities summarize these interacting factors. Combining these variables with others typically employed in analyses of farmer adoption of technologies permits specification of unified models of farmer adoption and use of disease control technologies. 5 Specifically, the OLS estimates would be subject to “simultaneity bias,” a condition in which one (or more) of the explanatory variables in a regression is correlated with the disturbance term in that equation (Gujarati, 1995). 6 In 2SLS estimation, the endogenous explanatory variable is replaced by a linear combination of truly exogenous variables. This combination is then used as the explanatory variable in lieu of the original endogenous variable (Gujarati, 1995).

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labor; access costs; altitude; non-farm occupation. Table 2 shows the results of two regressions (the third and fourth columns). The first is a direct OLS estimation of chemical fertilizer use on manure use and other variables, ignoring the potential endogeneity between chemical fertilizer and manure use. The second is a 2SLS regression that includes the same variables but allows for the possible endogeneity.7 Comparison of the two equations confirms that direct OLS estimation is inappropriate.8 The coefficient on manure use is insignificant in the OLS regression but significant in the 2SLS regression. In addition, based on the omitted variable version of the Hausman test, exogeneity of manure use can be rejected with 99 percent confidence.9 Table 2 Regression results (numbers in parentheses are standard errors; significance levels: ∗∗∗ p= 0.01, ∗∗ p= 0.05, ∗ p= 0.10) Mean

SD

OLS regression

Dependent variable = fertilizer use (natural log of kg applied)

3.65

3.10

Intercept

–

–

MANURE USE (log of kg applied)

6.71

0.95

FOOD CROP SHARE (area under food crops/cropped area) ACCESS (1 = access cost ⬎ sample median) FAMILY LABOR (No in HH between 16–45 years) HIRED LABOR (natural log of mandays) EDUCATION (1 = HH head more than primary) Adjusted R2 F statistic Number of observations

0.51

0.20

0.46

0.50

4.16

2.47

4.03

2.69

0.68

0.47

– –

– – 129

129

4.062∗∗∗ (0.617) 0.935 (0.810) –0.343 (0.389) –0.419∗∗∗ (0.147) 0.084∗∗∗ (0.029) 0.175∗∗∗ (0.028) –0.176 (0.149) 0.36 13.16∗∗∗ 129

2SLS regression

6.663∗∗∗ (1.208) –0.346∗∗ (0.164) –1.259∗∗ (0.628) –0.307∗ (0.165) 0.103∗∗∗ (0.031) 0.200∗∗∗ (0.032) –0.152 (0.158) 0.26 8.35∗∗∗ 129

7 Instruments (i.e., variables assumed to be correlated with manure use in the current season but not with the other regressors) included in the 2SLS regression are: non-farm occupation (1=yes); household members 0–7 years, 8–15 years, 46–61 years; farm sited above 2400 m above sea level (1=yes); farm sited between 1520 and 2400 m above sea level (1=yes). 8 Both models were corrected for potential heteroscedasticity, which refers to violation of the assumption that variable disturbances all have the same variance. The heteroscedasticity-corrected results did not differ from those reported in the table, either qualititatively or in significance levels. 9 The test involved estimating the reduced form for manure use and then including its fitted values in the 2SLS equation. The null hypothesis of exogeneity would be rejected if the coefficient on the regressor representing the fitted values was significant (Murkherjee et al., 1998).

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The 2SLS regression indicates that inorganic fertilizer use is significantly higher: the lower is manure use; the lower the share of food-crops in production patterns10 the lower are farm-to-market transport costs; the larger the quantity of family labor; and the greater the quantity of labor hired-in. The level of education of the head of household appears not to influence fertilizer use.11 All but the first of the significant effects conformed to prior expectations, based on theory and relevant empirical evidence. Soil scientists are in broad agreement that soil fertility replenishment in Africa requires increased use of both inorganic fertilizers and organic manures (e.g., Palm et al., 1997; TSBF, 1995); models of nutrient cycling on-farm often implicitly assume (embed) complementarity between the two sources of soil nutrients (e.g., Woomer and Swift, 1994). Although some plant nutrient requirements can be met through organic materials available in farming communities, such materials are usually insufficient to replenish plant nutrients removed from soils and thus sustain or expand crop yields (Pinstrup-Andersen, 1999). Many farmers appear to recognize these facts (ILEIA, 2001; Peterson, 1998). The sign on the coefficient on the MANURE USE variable was expected to be positive. However, the negative and significant coefficient on the variable points to a relationship featuring substitution (or even competition) between sources of inorganic and organic nutrients on small farms. Farmers in Kenya tend to apply fertilizer and other improved inputs to those crops in their production systems that have the highest market returns (Argwings-Kodhek et al., 1991; de Jager et al., 1998). Market returns to the food-crops grown by sampled farmers were considerably lower than were those to cash-crops (Obare, 2000). The sign on the coefficient on the FOOD CROP SHARE variable was expected to be negative. Basic economics predicts a negative relationship between input use and input price. Farmers in the study region faced similar market prices for chemical fertilizers (Obare, 2000).12 But given their widely differing distances from market centers, they likely faced very different farm-gate prices. Such farm-gate prices were not computed. But the ACCESS variable (a binary variable that took a value of unity when the household’s location relative to market centers and extant road types and transportation modes implied farm-to-market transport costs that were greater than the

10 An argument could be made for the endogeneity of this variable. However, following such authors as de Janvry et al. (1991) and Hunter (1973), output-mixes are taken to be “pre-determined” in any year, in the sense that they are more sluggish than are input applications. For instance, it is common for farmers in many parts of Kenya to leave cropping patterns unchanged while in some cases halving or in others doubling fertilizer (and seed) application rates in response to changing circumstances (Tegemeo, 1998). The Hausman test added credence to this assumption; exogeneity of the cropped area devoted to foodcrops could not be rejected. 11 Nor do any other variables capturing education levels in households—e.g., the total numbers of years of education in households, or the number of members with post-primary education. 12 There was no market for manure.

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sample median, and zero otherwise) was assumed to capture this variation.13 Its coefficient was expected to take a negative value. Fertilizer use is labor-intensive and in Kenya is very often positively influenced by availability of both domestic and external sources of labor (Mose et al., 1997; Nyangito et al., 1997). The coefficients on the FAMILY LABOR and HIRED LABOR variables were expected to be positive.

Implications Soil nutrient depletion is a dynamic process (Babu et al., 1995; TSBF, 1995). The regression results are based on cross-section data and thus cannot be given direct dynamic interpretations. However, the results appear sufficiently robust to suggest that the process of nutrient depletion may be associated with: high use of organic manures; high shares of food-crops in production patterns; high farm-to-market transport costs; low levels of family labor; and low levels of hired-in labor. Manure use is common among African smallholders (e.g., de Jager et al. (1998). But sustained soil fertility replenishment requires increases in both inorganic and organic nutrient stocks and flows (Palm et al., 1997). The finding that once a range of farmer-specific characteristics are taken into account, farmers substitute one nutrient source for the other does not bode well for efforts to resurrect productivity growth in African agriculture via soil fertility replenishment. Returns to research aimed at explaining such behavior are likely to be high, as are those to efforts seeking to identify options to promote complementary use of inorganic and organic fertilizers on small farms. The results point to several additional pressures against increased use of inorganic fertilizer and thus toward soil nutrient depletion. The results indicate that inorganic fertilizer use is higher on cash-crops than it is on food-crops. But African smallholders grow mainly food-crops for home consumption (e.g., Badiane et al., 1997). The results suggest that inorganic fertilizer use is higher the lower are farm-to-market transport costs. But most of Africa’s smallholders reside in remote areas and thus face high farm-to-market transport costs (e.g., Omamo, 1998a). Finally, the results underscore that inorganic fertilizer use is labor-intensive. But increasing numbers of farming households in Africa are losing their working-age members to urban areas but cannot afford to hire-in labor (e.g., UNFPA, 1996). To the extent, therefore, that soil fertility replenishment features increased use of inorganic fertilizers, the results indicate that it springs from (accompanies) changes in farming systems that make them less dependent on manure, less subsistence-oriented, less physically and administratively isolated, and less reliant on direct labor inputs. Such changes hinge on adjustments both deep within and well beyond small-

13

This formulation of the ACCESS variable can also be viewed to capture fixed costs of trade that render transport and other farm-to-market transaction costs non-linear functions of distances separating homesteads and market centers.

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holder farming systems. Those adjustments themselves depend on factors that lie firmly outside the purely biophysical domain. Direct attacks on soil nutrient depletion—such as in on-going efforts to “recapitalize” phosphorus in Africa (Sanchez et al., 1997)—are likely to yield disappointing outcomes. For implicit in these efforts is a view of soil nutrient depletion as a largely biophysical process. Clearly, successful design and implementation of soil fertility replenishment initiatives in Africa requires an understanding of the basic rationale of small-scale farming on the continent. Many years ago, Hunter (1973) fingered the lack of such a rationale as one cause of policies that failed to address the needs of Africa’s millions of smallholders. That knowledge gap remains largely unfilled. And policies continue to fail smallholders (Badiane et al., 1997; Jayne et al., forthcoming; Kherallah et al., 2000). When that rationale does emerge, smallholders’ soil fertility management decisions are certain to be central to it (Lynam, 1994); the analytical challenge will be how precisely to represent those decisions in a consistent manner. To fully integrate smallholders’ soil fertility management decisions with their other choices in production, consumption, and trade would require a dynamic, stochastic, multi-scale formulation (e.g., Babu et al., 1995; Lynam, 1994). The cost of collecting and managing the necessary detailed panel data comprising several interconnected layers of spatially explicit socioeconomic and biophysical variables is likely to be prohibitive. The current analysis suggests that considerable insight can be gained from approaches that use key results from soil science to add structure to consistent but tractable socioeconomic models of small-scale agriculture.

Summary and conclusions Small-scale farmers are prominent in world agriculture. They dominate African agriculture. They are thus the principal custodians of the continent’s soil resource base. Recent efforts to monitor soil nutrients in Africa’s small-scale farming systems have greatly increased understanding of nutrient stocks and flows in many of these systems (e.g., de Jager et al., 1998). The information base on the structure and functioning of fertilizer markets is also growing (Argwings-Kodhek, 1997; IFDC, 2001; Freeman, 2001; Omamo and Mose, 2001). However, relatively little is known about how smallholders’ soil fertility management decisions are linked to other features of their production systems and external environments. Prospects for translating the increased knowledge about the biophysical determinants of soil nutrient conditions on small farms and conditions in fertilizer markets into sustainable programs for soil nutrient replenishment are lowered as a consequence. Using farm-level data collected in Nakuru District, Kenya, this paper addresses this knowledge gap by developing and estimating a model that explores relationships between inorganic and organic fertilizer use and a range of household socioeconomic characteristics. At base, its findings are that, when various household socioeconomic characteristics are taken into account, inorganic and organic sources of soil nutrients appear to substitute for each other, lowering prospects for sustained soil fertility replenishment. The key recognition is that the household characteristics identified as

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significant molders of farmers’ soil fertility management choices are also key descriptors of small-scale agriculture in Africa. Soil fertility replenishment thus is no different from any other productivity-enhancing intervention in African agriculture in that achieving it requires answers to familiar but largely unanswered questions about small-scale agriculture in Africa: What are the key constraints facing Africa’s smallholders? How do smallholders’ production, consumption, and trading decisions reflect these constraints? What do changes in external conditions imply for smallholders’ abilities to override binding constraints? Successful programs aimed at soil fertility replenishment in Africa will be those that help smallholders address combinations of biophysical and socioeconomic constraints that make nutrient depletion advantageous.

Acknowledgements The authors thank the Editor and two anonymous reviewers for helpful comments on an earlier draft of the paper. Funding support from Stanford University, UCDavis, and the Rockefeller Foundation is gratefully acknowledged.

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