the economics of soil fertility management in malawi

Review of Agricultural Economics—Volume 31, Number 3—Pages 535–560

The Economics of Soil FertilityManagement in Malawi

Johannes Sauer and Hardwick Tchale

We estimated a normalized translog yield-response model using African farm-householdsurvey data to compare the yield of smallholder maize production under integrated soilfertility management (ISFM) and chemical-based soil fertility management. Controllingfor other factors, maize yield responses were higher under ISFM. Results suggest ISFMpractices would significantly improve the profitability of smallholder maize production,especially under escalating fertilizer prices.

Maize is the dominant crop in smallholder farming systems in Southern andEastern Africa. In Malawi, it is the main staple crop, grown on over 70%

of the arable land, and nearly 90% of the cereal area. Malawi has the highest percapita maize consumption in the world at 148 kg per person (Smale and Jayne).Thus, maize will remain a central crop in the food security equation of Malawieven if the agricultural economy is diversified. The dominance of maize as astaple crop mainly emanates from the self-sufficiency policy that the governmentadopted after independence in the mid 1960s resulting from the need to produceenough food to feed the growing rural population as well as keep staple foodprices low.

Many studies conducted in Malawi indicate declining levels of maize yieldthat poses serious food security concerns (Kydd; Smale and Jayne; Chirwa, 2003).Smale and Jayne have attributed the decline in maize yield over the time to fourmain reasons: (a) removal of subsidies, (b) devaluation of the Malawi Kwacha,(c) increase in world fertilizer prices, and (d) low private market developmentbecause fertilizer dealers require substantial risk premiums to hold and transportfertilizer in an inflationary economy with uncertain demand (Conroy; Diagne andZeller; Benson 1997, 1999). The situation is exacerbated as maize price changesfollow export parity while fertilizer price changes reflect full import costs. Sincemost fertilizer in Malawi is used on maize and tobacco, the removal of implicit

� Johannes Sauer is Senior Lecturer at the University of Manchester, UK. HardwickTchale is an Agricultural Economist with World Bank and the University of Malawi,Lilongwe, Malawi.

DOI:10.1111/j.1467-9353.2009.01452.x

536 Review of Agricultural Economics

subsidies in the form of overvalued exchange rates had a strong negative effecton fertilizer use. Furthermore, since almost all of Malawi’s fertilizer supply isimported, the depreciation of the real exchange rate has also invariably raisedthe nitrogen-to-grain price ratios (Minot, Kherallah, and Berry 2000a; Heisey andSmale).

In this article, we analyze the factors that influence maize yields among small-holders taking into account differences in soil management practices. The mostcomprehensive studies of smallholder yields in Malawi have been conductedby Chirwa (1996, 2003) and Edriss, Tchale, and Wobst. The first two studies usefarm-level data collected from a sample of 156 farmers from Machinga Agri-cultural Development Division (ADD) for 1995 and 2002. The 156 householdsinterviewed had a total of 444 plots used for the production of various crops, 206plots used for maize production, and only 50 plots purely for maize. The authorfound that those farms partly using hired labor and those applying fertilizer aremore technically efficient. Furthermore, it was revealed that the adoption of hy-brid seeds is positively associated with market-based land tenure systems andfertile soils but negatively associated with the farmer’s age and the distance toinput markets. Edriss, Tchale, and Wobst used national-level data for 1985 to 2002to analyze the levels of maize yields given the labour market liberalization. Theauthors concluded that as a result of labor market reforms (i.e., the Labor Rela-tions Act 1996 and the Employment Act 2000) the decrease in farm labor inputshare has been the largest contributing factor to the maize yield decline duringthe period investigated. All these studies use parametric approaches to estimatethe efficiency of Malawian smallholder farmers in maize production. Our studycomplements these studies in a number of ways. First, the first two studies havebeen restricted to only one ADD and their results may not be applicable to otherdivisions, whereas our sample is drawn from three ADDs and thus accounts foragroecological variations. Second, both studies did not account for the theoreticalregularity conditions in their analysis. Therefore, it is highly likely that policy con-clusions drawn from these studies may have been flawed due to lacking regularityof the estimated functions. Third, our study considers the yield effect of alterna-tive soil fertility management options available to smallholder farmers. Such afocus seems to be important as while many alternative soil fertility managementoptions have been developed for smallholder farmers, very little is known abouttheir impact on improving smallholder farmers’ yield. The obvious weakness ofthe study by Edriss, Tchale, and Wobst is the use of national-level data that ignoresthe farm-level variations. We improve on that by using farm-level data.

In general, integrated soil fertility management (ISFM) refers to an integrativeuse of inherent soil nutrient stocks, locally available soil amendments, and min-eral fertilizers to increase the yield of the land while maintaining or enhancingsoil fertility (see e.g., Pieri or Breman and Sissoko).1 An increase in yield throughagricultural intensification generally requires the use of mineral fertilizers leadingto serious damages to soil fertility (i.e., acidification) and yield decline in the longterm. On the other hand, the nutrient content by organic sources is relatively lowand not abundantly available to the farmer. Hence, a combination of inorganicand organic fertilizers has been suggested as a superior remedy for soil fertilityin Sub-Saharan Africa (see e.g., Breman and Sissoko). Here the inorganic fertil-izer provides the nutrients and the organic fertilizer increases soil organic matter

The Economics of Soil Fertility Management in Malawi 537

Table 1. Maize varieties and fertilizer use in Malawi

North Centre South Total

Maize growers (%) 93 97 99 97Local hybrid varieties (%) 38 55 62 56Composite varieties/OPV (%) 5 6 7 7Fertilizer use (%) 59 39 27 35Fertilizer use (kg/ha) 69 35 36 39ISFM (%) 35 23 16 21

Notes: NSO Annual Welfare Monitoring Survey 2006. “Local hybrid varieties” are hybrid varietiesthat are adapted to local Malawi conditions and farmers’ tastes (e.g., Malawi hybrid 17 and 18).“Composite varieties” refer to cross breeds between hybrids and local varieties (e.g., UkiriguruComposite A, UCA). “Open pollinating varieties” (OPVs) are improved varieties.

status, the structure of the soil, and its buffering capacity in general. Moderateapplications of minerals ensure balanced plant nutrition and the maintenance ofthe soil fertility. Table 1 shows the shares of Malawian farmers growing hybridand OPV maize as well as the use of fertilizer.

To increase the fertilizer use by smallholder farmers the Malawian governmentimplemented a Starter Pack Program (SP) in 1998–99. Under this program, ini-tially small quantities of fertilizer (covering a 2.5 kg bag of hybrid seed and bags ofrecommended fertilizer as well as complementary nitrogen-fixing legume seed)were distributed to households identified as poor (Minot, Kherallah, and Berry,2000b). The cost of a representative pack was $18 per household (Blackie). Fromthe beginning, the program has been subject to concerns about targeting errorsand creating dependence on rather than access to fertilizer. Supporters of the pro-gram stressed on the other side, that unlike a conventional subsidy, all resourcescommitted to the program generated incremental production. The SP Programevaluation report (see Levy) concluded that about 40% of the farmers receiv-ing such starter packs had purchased additional inputs with their own money,that 32% of maize sales by the poorest category of households were attributedto starter packs compared with 13% for the richest households. The “TargetedInputs Programme” (TIP) followed on from the Starter Pack campaigns and wasintended to provide rural smallholder households with one Starter Pack contain-ing 0.1 ha worth of fertilizer, maize seed, and legume seed. The initial Starter Packcampaigns were designed to cover all rural smallholder households, providing2.86 million packs. The TIP covered roughly a million beneficiaries. For the TIP,the evaluation report (Levy) concluded that the net contribution to total maizeproduced was even lower than by the SP program (on average 0.750 kg bags ofmaize) because of poor weather and premature harvesting. The report concludesthat potential benefits are mediocre because of widespread incorrect fertilizer ap-plication. In 2005–06, the TIP was transformed into a targeted fertilizer and seedsubsidy program targeting 1.7 million poor smallholders in Malawi.

Reviewing trends and determinants of fertilizer use in Sub-Saharan Africa,Naseem and Kelly found that fertilizer use has been highly variable and con-centrated among a few countries being positively correlated with the amount ofrainfall per year, the density of road infrastructure, improved human capital at


the farm level, as well as the percentage of cultivated area devoted to cotton.One critical consequence of the increase in fertilizer prices relative to maize grainprices is that most farmers over the past decade have continued to overexploitthe natural soil fertility. The main reason for this is the fact that improved maizevarieties released by the National Agricultural Research (i.e., MH17 and MH18)proved to yield more than local maize without fertilizer at the seed prices that pre-vailed through the early 1990s, implying that it made economic sense for farmersto grow hybrids even if they could not apply fertilizer (Heisey and Smale; Benson1999). The result has been soil fertility mining as the inherent soil fertility is nolonger capable of supporting crop growth at a rate that is required to feed thegrowing population.

However, farmers’ choice of the available soil fertility management options de-pends largely on the relative returns of the options. Most studies indicate that ingeneral, ISFM options are more remunerative where purchased fertilizer aloneremains unattractive or highly risky, as is the case with the maize-based small-holder farming systems in Malawi (see e.g., Chirwa 2003). For example, marginalrate of return analysis conducted on trials in Malawi identified maize-pigeonpea intercropping, groundnut-maize intercropping, and rotation as being eco-nomically attractive to smallholder farmers (Twomlow, Rusike, and Snapp). InZimbabwe, Whitebread, Jiri, and Maasdorp reported a 64% higher yield whenmaize is planted following green manure rotation compared to continuous fer-tilized maize. By considering different cropping patterns (i.e., Mucuna/Maize,Groundnut/Maize, Pigeaonpea/Maize, Tephrosia/Maize, and Soyabean/Maize)Mekuria and Waddington (2002) also reported that ISFM options gave a returnto labor of $1.35 per day compared to $0.25 per day when either mineral fertiliz-ers or organic soil fertility management options are used alone in Zimbabwe. InKenya, Place et al. reported that the returns to labor from ISFM options rangedfrom $2.14 to $2.68 per day compared to $1.68 per day when only inorganic fertil-izer is used. Economic analysis in central Zambia also indicates that velvet beanand sunhemp green manure followed by maize gives higher rate of returns com-pared to fertilized maize crop alone (Mwale et al. report a net present value ofabout $19 per ha). Such superior economic performance indicators are also re-ported by Mekuria and Siziba in the case of Zimbabwe (a net present value of$152 per ha). By evaluating the viability of improved fallows in agroclimatic zonesin Eastern Zambia, the International Center for Research in Agroforestry foundthat improved fallow practices have important economic and ecological benefitsto participating farmers and society as a whole (see Franzel et al.). The studyconcludes that by using improved fallows, farmers can substitute relatively smallamounts of land and labor for cash needed to purchase mineral fertilizer. Hagg-blade and Tembo found evidence that the effectiveness of conservation farmingvaries across regions, across crops as well as across time resulting from differingweather and rainfall conditions. Hence, they conclude that the establishment oflong-term monitoring efforts for conservation farming and the controlling of plotsacross a broad range of geographic settings, crops, and seasons are crucial.

Our study aims at assessing the yield and profitability of maize productionusing ISFM or inorganic fertilizers only. Hence, we first determine the relativeeffect of ISFM on maize yield. Using the yield effect, we estimate the profitmaximizing fertilizer level with and without ISFM. We then test whether use


of ISFM in maize production is more profitable than using inorganic fertilizeralone. Such an analysis could assist extension personnel’s and NGOs’ decisionmaking with respect to the initiation and the support of integrated soil fertilityprograms on farm level. Furthermore, the results of this study aim to influencepolicy measures on fertilizer subsidization and try to show the need for an in-tensification of education programs on soil fertility management among farmers.Hence, we aim to reveal empirical evidence on the following underlying researchhypotheses: (a) the relative yield of small-scale maize production based on ISFMpractices is expected to be higher than the yield of small-scale maize productionbased on inorganic fertilizer use practices only and (b) ISFM is expected to bemore profitable for small-scale maize production in Malawi than the use of inor-ganic fertilizer alone. The rest of the article is arranged as follows: the next sectionpresents a theoretical review with respect to the specification of a yield-responsefunction (section 2) followed by the discussion of the empirical model used (sec-tion 3). Section 4 describes the data and the analysis whereas section 5 concludeswith main findings and their policy implications.

Theoretical ReviewA number of functional forms have been used to specify yield-response

functions, most commonly the Cobb-Douglas, quadratic, square root, translog,Mitscherlich-Baule (or MB), as well as the linear and nonlinear Von-Liebig func-tions. The rationale for choosing a particular functional form depends on theresearch questions and the underlying production processes to be modeled(Nkonya). Furthermore, the choice of a functional form should be based on theneed to ensure rigorous theoretical consistency and factual conformity within agiven domain of application as well as flexibility and computational ease (Lau;Sauer). Most commonly, beside quadratic functional forms, the nonlinear Von-Liebig, and MB functions are used, especially in the field of agronomy (Ackello-Ogutu et al., Belanger et al.). However, the latter are highly nonlinear. Especiallywhen a number of inputs are involved, their estimation is cumbersome and liableto several parametric restrictions. The other weakness of the MB function is thatit may not be appropriate for modeling farm production in developing countriesbecause it is only appropriate for stage II production (where marginal productincreases at a decreasing rate).

But research shows that most constrained farmers in developing countries stilllargely operate within stage I where marginal product increases at an increasingrate (Frank, Beattie, and Embelton; Keyser). Belanger et al. compared the perfor-mance of three functional forms (quadratic, exponential, and square root) andconcluded that although the quadratic form is the most favored in agronomicyield-response analysis, it tends to overstate the optimal input level, and thusunderestimating the optimal profitability (see also Bock and Sikora; Angus, Bow-den, and Keating; Bullock and Bullock). Our choice of the translog is further basedon the following: first, it is the best-investigated second-order flexible functionalform and certainly one with the most applications (Sauer); second, this functionalform is convenient to estimate and has proved to be a statistically significant spec-ification for economic analyses as well as a flexible approximation of the effect ofinput interactions on yield.


We assume farmers make soil management choices to maximize profits, giventhe technology and soil fertility management options available. Farms are as-sumed to be output price takers and given prices, select inputs (and hence, yield) tomaximize profits. Furthermore, because of a lack of stochastically sufficient pricevariation the following analysis uses a primal production function framework.Given finally the uncertainties in expected agricultural prices and production, it isunlikely that the correspondence between expected prices and production wouldgive an appropriate model fit.

Thus, we describe the yield function

h (q , x, z) = 0(1)

where q is the vector of yield, x is the vector of variable inputs, and z is a vectorof fixed factors. The farmer is thus assumed to choose a combination of variableinputs that will maximize yield subject to the production technology constraint

Maxx,z

q s.t. h (q , x, z) = 0.(2)

Hence, it is possible to approximate the optimal level of inputs, which whensubstituted into the corresponding yield function results in the optimum levelof yield and consequently the optimum level of household utility. In this analy-sis, we use a normalized translog functional form because we assume that yieldresponse depends on nitrogen use efficiency and a second-order polynomial func-tion can approximate such a relationship. The normalized translog models havebeen widely used to describe crop response to fertilization and tend to performbetter statistically than other functional forms.

The ModelThe normalized translog maize production model can be expressed as

ln qi = �0 +n∑

i=1

�i ln xi + 12

n−1∑

i=1

n∑

j=i+1

�ij ln xi ln x j+m∑

k=1

�k zk

+�sfmzsfm + �sfmsfm(zsfm

)2 +n∑

i=1

�isfmxi zsfm +m∑

k=1

�ksfmzk zsfm

+r∑

l=1

�lvl +r∑

l=1

�lsfmvl zsfm + ε ε ∼ N(0, �2)

(3)

where q = qq ′ is the yield (kg/ha), xi = xi

x′i

and x j = xj

x′j, respectively, are the vari-

able inputs (fertilizer, labor, and seed), zk is a vector of yield shifters (i.e., rainfall,weeding frequency, planting date, the agricultural zone, soil depth, organic mat-ter in the soil, total nitrogen in the soil, soil bulk density, soil particle density,cropping patterns, the quantity of manure applied, the different kind of manureapplied) and vl as a vector of general control variables (market access, exten-sion frequency, credit access). All variables are normalized to the sample mean


by dividing by the mean value (q′, xi′, xj

′). We further include a variable for soilfertility management zsfm (i.e., integrated management or use of inorganic fertil-izer only) in order to assess the impact of the soil fertility management choiceon yield response as well as account for possible interactions with the conven-tional inputs and the yield shifters as well as control variables chosen. Hence�i denote the linear input parameters, �i j are the quadratic and input interac-tion parameters, �k and cl are the parameters for the yield shifters and controlvariables, respectively, �sfm, �sfmsfm, �isfm, �ksfm, �lsfm are the parameters for the soilfertility management-related variables and interaction terms and εi finally de-notes the error term assumed to be randomly distributed with zero mean andconstant variance �2.

As was mentioned before we maintain the assumption that farmers’ choice ofa soil fertility management option is beside self-sufficiency based on the desireto increase the profit derived from increased crop yield given the technology andsoil fertility management options available. However, this raises the problem ofsimultaneity with respect to certain production decisions, that is, input levels,cropping pattern, and manure quantity and their possible correlations with un-observed inputs or yield shocks (e.g., managerial ability, inputs’ quality, capacityutilization, see also Olley and Pakes; Levinsohn and Petrin).2 Such correlationswould lead to a severe bias in the estimated coefficients and standard errors.Hence, we apply a Hausman test formula where the null hypothesis implies thatthere is no simultaneity. Furthermore, the modeling issue of potential selectivitybias has to be discussed (see e.g., Greene): selectivity bias would be the case forour sample if those farms participating in the trials (i.e., the use of ISFM) are sig-nificantly different from those farms not participating in the trials (i.e., the use ofinorganic fertilizer only). We test for such selectivity bias by applying an inverseMill’s ratio procedure where in a first step, a regression for observing a positiveoutcome of the dependent variable (ISFM or not) is modeled with a Probit modelspecification. The estimated parameters are used to calculate the inverse Mills’ratio, which is then included as an additional explanatory variable in the produc-tion function estimation. If the estimated parameter were statistically significant,selectivity bias would be likely the case for our sample.

In the case of a (single output) production function, monotonicity requires posi-tive marginal products with respect to all inputs and thus nonnegative elasticities.By further adhering to the law of diminishing marginal productivities, marginalproducts, apart from being positive should be decreasing in inputs. The necessaryand sufficient condition for a specific curvature consists in the semidefinitenessof the bordered Hessian matrix. With respect to the translog production functioncurvature depends on the specific input bundle. For some input bundles quasi-concavity may be satisfied, but for others not. Hence, one may expect the conditionof negative semidefiniteness of the bordered Hessian is met only locally or with re-spect to a range of input bundles (see Sauer). Our normalized translog productionmodel has to be checked a posteriori for every input bundle (i.e., here for every ob-servation) that ensure monotonicity and quasi-concavity hold. If these theoreticalcriteria are jointly fulfilled the obtained estimates are consistent with microeco-nomic theory and consequently can serve as empirical evidence for possible policymeasures. With respect to the proposed normalized translog production modelquasi-concavity can be imposed at a reference point (usually at the sample mean)


following Jorgenson and Fraumeni. By this procedure the bordered Hessian is re-placed by the negative product of a lower triangular matrix ∆ times its transpose∆′. Imposing curvature at the sample mean is then attained by setting

�ij = −(��′)ij + �i �ij + �i � j(4)

where i, j = 1, . . . , n, �ij = 1 if i = j and 0 otherwise and (∆∆′)ij as the ijthelement of ∆∆′ with ∆ a lower triangular matrix. As our point of approximationis the sample mean all data points are divided by their mean transferring theapproximation point to an (n + 1)-dimensional vector of ones. At this point theelements of H do not depend on the specific input price bundle. The estimationmodel of the normalized translog production function is then reformulated asfollows:

ln qi = �0 + �1 ln x1 + �2 ln x2 + �3 ln x3 + 12

(−�11�11 + �1 − �1�1) ln(x1)2

+12

(−�12�12 − �22�22 + �2 − �2�2) ln(x2)2

+12

(−�13�13 − �23�23 − �33�33 + �3 − �3�3) ln(x3)2

+ 12

(−�12�11 − �1�2) ln(x1)2 ln(x2)2 + 12

(−�13�11 − �1�3) ln(x1)2 ln(x3)2

+ 12

(−�13�12 − �23�22 − �2�3) ln(x2)2 ln(x3)2

+m∑

k=1

�k zk + �sfmzsfm + �sfmsfm(zsfm

)2

+n∑

i=1

�isfmxi zsfm +m∑

k=1

�ksfmzk zsfm +r∑

l=1

�lvl +r∑

l=1

�lsfmvl zsfm + εi .

(5)

However, the elements of ∆ are nonlinear functions of the decomposed matrix,and consequently the resulting normalized translog model becomes nonlinear inparameters. Hence, linear estimation algorithms are ruled out even if the orig-inal function is linear in parameters. By this “local” procedure a satisfaction ofconsistency at most or even all data points in the sample can be reached. Thetransformation in equation (5) moves the observations toward the approximationpoint and thus increases the likelihood of getting theoretically consistent resultsat least for a range of observations (see Ryan and Wales). However, by imposingglobal consistency on the translog functional form Diewert and Wales note thatthe parameter matrix is restricted, leading to seriously biased elasticity estimates.Hence, the translog function would lose its flexibility. By a second analyticalstep we finally (a posteriori) check the theoretical consistency of our estimatedmodel by verifying that the first derivatives are positive (monotonicity) the ownsecond derivatives are negative and finally the Hessian is negative semidefinite(concavity). To our knowledge, this econometric combination of a yield-responsemodel with curvature restrictions has not been applied in the relevant literaturebefore.


Using equations (5) and (2), we determine the marginal profit for the conven-tional inputs labor, fertilizer, and seed and compare the resulting total profit tothe observed total profit at the sample means. Assuming that all farmers facethe same output and input prices, profit solely depends on the yield-responsefunction given by the marginal yield of the input, hence

∂

∂xi= p ∗ ∂q

∂xi− c,(6)

where p denotes the observed market price and c as the observed costs calculatedby using the observed input prices w. The outlined procedure is performed forboth alternative soil fertility management options and the levels of optimal profitare compared.

DataThe country is divided into eight ADDs and 27 regional districts (RDP). Maize

production in Malawi is mainly concentrated on the ADD of Mzuzu, Kasungu,Lilongwe, Machinga, and Blantyre. Agricultural technologies are transferred tofarmers in Malawi through two main channels: (a) the Department of AgriculturalExtension and Training (DAET) and (b) nongovernmental organizations (see alsoMilner). With respect to the establishment of a network of extension staff in ruralareas by DAET (with the responsibility of organizing and training farmers as wellas disseminating technologies) the whole country was divided into eight ADDsbased mainly on agroecological aspects. As the results and conclusions of ourstudy predominantly focus on extension services and policy actors we use ADDsas one level of analysis beside the farm level.

In 2003–04, the average maize yield in Malawi was about 1.1 t/ha rangingfrom 0.9 t/ha to 1.4 t/ha (see FAO 2004). The sampling frame for the datasetused in this study is based on information collected by on-farm trials, which havebeen conducted on representative farmers’ fields in all extension planning areas inMalawi. These farmers represent the sampling frame as they continuously appliedthe soil fertility management practises that have been demonstrated in these trialsbefore. Hence, they can be regarded as being representative of smallholder farmersin the Malawian study region, which adopted the soil fertility technologies.

Different on-farm trials have been conducted by the Maize Yield Task Force(MPTF), Department of Agricultural Research and Technical Services, Ministry ofAgriculture. The MPTF started in 1995 with funding from the Rockefeller Foun-dation. The MPTF sponsored two on-farm trials: area-specific fertilizer recom-mendation and best-bet trials. These area-specific fertilizer recommendation tri-als involved 1,750 trials for the final analysis (initially 2,000 plots were included)across the country for the 1997–8 season. Four treatments were applied to a plotsize of about 40 square meters, laid out on farmers’ fields. The four fertilizer treat-ments were 0, 35, 69, and 92 kg ha−1 of urea and 23:21:0 + 4S. Flintier hybridmaize varieties MH17 and MH18 were planted depending on the altitude. Theplots were managed by the farmers supervised by the local agricultural extensionstaff who also recorded the maize yields. An effective response rate of over 80%was attained by using in total 1,408 trials for the analysis. The best-bet trial was


implemented on 1,400 on-farm sites by the Malawian Extension Service in 1998–99, using the same setup as for the area-specific fertilizer recommendation trial.In total, six treatments were included in the experiment: (a) green legume rotationinvolving soybean or groundnuts, (b) Mucuna pruriens rotation, (c) maize pigeonpea intercrop, (d) fertilized maize, (e) unfertilized maize, and (f ) local maize (fer-tilized and unfertilized) as the control treatment. The fertilized option involvedeither 35 or 69 kg ha−1 of N fertilizers (urea or 23:21:0 + 4S) depending on the area-specific fertilizer recommendations. With exception of the control treatment, thesame maize varieties (i.e., MH17 and MH18) were planted depending only on thealtitude of the area. Here an effective response rate of nearly 99% was attained byusing 1,385 trials for the analysis (see also Twomlow, Rusike, and Snapp; Mekuriaand Waddington; Whitbread, Jiri, and Maasdorp).

The analysis focuses on hybrid maize only, which has been grown on 253 of the500 plots managed by 376 farmers for which data were collected. These farmerswere drawn from those continuously participating in the soil fertility managementinvolving public research institutions, donor organizations, and NGOs from theseason 1997–98 to the season 2002–03.

The climate in Malawi is semiarid in the Lower Shire Valley, semiarid to subhu-mid on the plateau, and subhumid in the highlands (see FAO 2000). Most of thecountry receives 760–1,150 mm rainfall per year, whereas 90% of rainfall occursbetween December and March, with no rain between May and October over mostof the country. The average temperatures vary with altitude ranging from 13◦C to25◦C. In general, there are five main landform areas: the Highlands, Escarpments,Plateaux, Lakeshore, Upper Shire Valley, and the Lower Shire Valley. Over thelast fifteen years, the mean annual rainfall level per year has been the highest forthe ADD of Mzuzu in the North and the lowest for the ADD of Lilongwe (FAO2004). The farms are located in the ADD of Blantyre, Lilongwe, and Mzuzu usingthe stratified random sampling approach in order to distill an accurate sample offarms located in the different areas of the country. All agricultural activities areundertaken from October to May, during the unimodal rainfall season. Table 2summarizes the rainfall levels and different soil characteristics for the farms inthe sample according to the agricultural development zones.

From these farmers, maize technology information was collected with respectto the variety grown, the rate of input applied, other soil fertility options followed,as well as the general husbandry practices applied to the crop. The maize yieldswere measured using “crop-cuts” involving collecting and measuring the harvestsfrom specific quadrants within the farmer’s field. The yield obtained from thequadrants is then used to calculate the per hectare yield levels. The sample usedfor the analysis is composed of the 253 plots on which hybrid maize was grown asthe main crop. As policy exclusively promotes farmers’ adoption of hybrid and notlow-yielding local maize varieties we are interested in the yield of hybrid maizehere. Many Malawian farmers use no fertilizer at all. Others apply open pollinatedmaize varieties, due to the lack of cash or credit. However, data on such farmsplanting continuous maize plots with open pollinated varieties without applyingadditional fertilizer were not available at the time conducting this study. Thepoint of reference for this study is hybrid maize used with inorganic fertilizer.Further research will be required to determine if ISFM is more profitable thanopen pollinated maize or hybrid maize without fertilizer (see table 3).


Table 2. ADDs’ rain and soil conditions—sample 2003

Agricultural Development Division (ADD)

Characteristic Mean SD Min Max

Blantyre (N = 129)Soil depth (in cm) 16.89 1.95 13.6 25.3Organic matter in the soil (%) 1.01 0.42 0.48 2.39Total nitrogen in the soil (%) 0.11 0.05 0.06 0.37Bulk density (g/cm3) 1.69 0.28 0.81 1.99Particle density (g/cm3) 2.53 0.19 1.96 2.87Rainfall (in mm) 892.83 47.56 812.4 992.3

Lilongwe (N = 60)Soil depth (in cm) 13.63 1.88 11.5 20Organic matter in the soil (%) 1.14 0.45 0.66 2.17Total nitrogen in the soil (%) 0.10 0.03 0.04 0.28Bulk density (g/cm3) 1.55 0.33 0.66 1.98Particle density (g/cm3) 2.42 0.26 1.95 2.8Rainfall (in mm) 903.46 71.72 812.4 992.3

Mzuzu (N = 64)Soil depth (in cm) 16.03 2.97 11.2 24.6Organic matter in the soil (%) 1.04 0.46 0.4 2.6Total nitrogen in the soil (%) 0.11 0.04 0.06 0.24Bulk density (g/cm3) 1.59 0.32 0.81 1.99Particle density (g/cm3) 2.45 0.27 1.98 2.86Rainfall (in mm) 907.81 65.61 812.4 992.3

Note: For a comprehensive descriptive statistics of the data sample see table 4.

Apart from the conventional inputs fertilizer, seed, and labor (including bothfamily and hired labor as specific data were not available and most smallholderfarmers in the sample do not hire additional labor), the specification of the yieldmodel includes also a number of important variables that substantially affectyields, especially in the smallholder farming systems. These include rainfall, crophusbandry practices such as weeding frequency, planting date, soil depth, organicmatter in the soil, total nitrogen in the soil, soil bulk density, soil particle density,cropping patterns, the quantity of manure applied, the different kind of manureapplied. Further critical policy variables include frequency of extension visits,access to seasonal agricultural credit, access to product and factor markets, andagroecological dummies. We also incorporate a soil fertility management dummy(either fertilizer only or ISFM involving fertilizer and grain legume intercrops forbiological nitrogen fixation). Because of lacking degrees of freedom we are notable to incorporate terms for all other variables’ interactions in the estimationmodel and hence incorporate those most valuable with respect to the underlyingresearch question. The descriptive statistics for all the variables that were includedin the yield model are presented in table 4.

The reformulated and curvature-constrained model described in equation (5) isestimated by using a nonlinear iterative estimation routine contained in STATA.Curvature correctness is checked for all observations.


Table 3. Farm household survey results (plot-level data)

Maize Yield (kg/ha) and Input Intensity

Low Low-to- Moderate HighCropping Pattern Input Moderate Input Input Input Average

Hybrid maize, grainlegume intercrop,fertilizer

623.0 (3.8) 1,727.8 (0.6) – 2,344.8 (0.9) 914.9 (2.2)

Mixed/local maize,grain legumeintercrop,fertilizer

400.2 (2.6) 1,136.8 (1.5) 1,670.5 (0.4) 1,921.5 (0.9) 858.4 (2.6)

Hybrid maize only+ fertilizer

264.1 (2.4) 1,589.6 (1.2) 1,943.1 (0.8) 2,552.3 (0.8) 952.5 (1.7)

Mixed/local maizeonly + fertilizer

213.8 (4.0) 1,016.9 (1.6) 1,581.9 (0.8) 1,855.0 (0.9) 511.0 (4.6)

Hybrid maize, grainlegume intercrop,no fertilizer

– – – – 431.7 (2.1)

Mixed local maize,grain legumeintercrop, nofertilizer

– – – – 352.8 (2.2)

Source: Own calculations from farm household survey data (2002–03 season).Notes: Figures in parentheses are coefficients of variation (CV) expressed as a percentage. Low inputusage = 0–34 kg/ha; moderate input usage = 35–90 kg/ha; high input usage = >90 kg/ha.

Results and DiscussionThe estimation results are shown in table 5. Given the cross-sectional dataset

and the imposed regularity constraints, the overall model fit is significant at the1% level (p < 0.000). The Hausman test formula showed no significant simultane-ity in the production decisions with respect to fertilizer, labor, and seed as well ascropping pattern and the quantity of manure applied (the null hypothesis of nosimultaneity bias could not be rejected at the 10% level of significance). Potentialselectivity bias could be rejected (the estimate of the inverse Mill’s ratio has beennot significant at the 10% level of significance). More than 90% of all observa-tions are consistent with the regularity conditions of monotonicity, diminishingmarginal returns, and quasi-concavity, respectively. All conventional input coef-ficients show a positive sign and are statistically significant. The parameters withrespect to soil fertility management are nearly all statistically significant imply-ing that the use of integrated soil fertility practices significantly influences maizeyields on the farm level.

The estimated parameters for the effects of rainfall, weeding frequency, exten-sion frequency, access to credit, organic matter in the soil, total nitrogen in thesoil, the soil particle density, the cropping pattern followed, and the differentmanure practices showed all a positive sign. Furthermore, farms located in the


Table 4. Descriptive sample statistics (plot-level data)

Variable Description Mean Std.

yield Hybrid maize yield (kg/ha) 914.9 886.6fertilizer Fertilizer intensity (kg/ha) 30.9 38.3labor Labor intensity (person days/ha/month) 67.3 34.8seed Seed intensity (kg/ha) 25.7 15.6isfm Soil fertility management (1 = ISFM; 0 = fert) 0.6 0.5weeding Frequency of weeding (number of weedings per season) 1.4 0.8planting Timing of planting (1 = early; 0 = later than first rains) 0.4 0.6rain Rainfall in mm (total seasonal rainfall) 899.1 59.0maccess Market access (1 = accessible; 0 = remote) 0.4 0.5extfreq Frequency of extension visits per month 0.8 1.0credit Access to credit (1 = yes; 0 = no) 0.4 0.5addm Agricultural development Mzuzu (1 = yes; 0 = no) 0.3 0.4addl Agricultural development Lilongwe (1 = yes; 0 = no) 0.2 0.4addb Agricultural development Blantyre (1 = yes; 0 = no) 0.5 0.4soildepth Soil depth (in cm) 15.9 2.5organm Organic matter in the soil (% of total) 1.1 0.4totaln Total nitrogen in the soil (% of total) 0.1 0.1bulkdny Bulk density (g/cm3) 1.6 0.3partdny Particle density (g/cm3) 2.5 0.2cropatrn Cropping pattern (1 = monocropping, 0 = intercropping) 0.2 0.4mnrq Amount of manure applied (in tons) 0.6 1.3mnr1 Animal manure applied (1 = yes; 0 = no) 0.1 0.1mnr2 Green manure applied (1 = yes; 0 = no) 0.1 0.1mnr3 Compost and animal manure applied (1 = yes; 0 = no) 0.1 0.3mnr4 Compost and green manure applied (1 = yes; 0 = no) 0.1 0.2

Source: Own survey (2003).Note: Total sample = 253; 143 households for ISFM and 110 households for inorganic fertilizer only.

agroecological zone of Mzuzu and Lilongwe showed to have a higher yield (ceterisparibus) than those located in the agroecological zone of Blantyre. On the otherhand, the estimated parameters for the date of planting, the access to input andoutput markets, the depth of the soil found, the bulk density in the soil, and thequantity of manure applied showed a negative sign. However, these variableswere statistically insignificant for the chosen model specification. To adequatelyreflect the interaction effects for ISFM practices and the conventional maize pro-duction inputs as well as the other soil, climate, and policy-related factors crosseffects variables have been included. Except for organic matter, particle density,and manure quantity all estimated parameters showed to be statistically signifi-cant. Nevertheless, positive interaction effects with ISFM were found for the con-ventional inputs fertilizer and seed, as well as the variables weeding frequency,planting date, market and credit access, the different agroecological zones, theparticle density in the soil, and the quantity of manure applied. A negative inter-action with ISFM practices was found for all other remaining control variables.The most interesting interactions are with respect to the quantity of rainfall and

548 Review of Agricultural EconomicsT

able

5.E

stim

atio

nre

sult

s

Par

amet

erC

oeff

Se

Par

amet

erC

oeff

Se

Con

stan

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.899

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466∗

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047

Lab

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ting

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Lilo

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dep

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orga

nic

mat

ters

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Tota

lnit

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128

ISFM

×m

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33∗∗

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man

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−0.0

65∗∗

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Part

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den

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0.21

4∗∗∗

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soil depth, organic matter and total nitrogen in the soil, and particle and bulk den-sity in the soil. The estimates for the conventional inputs labor, fertilizer, and seedare in line with the findings by earlier studies for small-scale farming in Malawi(see Chirwa, 2003; Edriss, Tchale, and Wobst). However, regarding the signifi-cance of the rainfall-related variables, following past evidence these estimatesshould be interpreted with caution. In most cases, seasonal rainfall has showedto be insignificant in crop response models as the rainfall distribution within theseason is much more important (see e.g., Frank, Beattie, and Embelton).

The output elasticities presented in table 6 indicate that, keeping all factorsconstant, a unit increase in labor, fertilizer, and seed will result in a 0.07%, 0.32%,and 0.68% increase in total maize yield, respectively. The elasticities for farmsusing ISFM are relatively higher for all inputs.

These elasticities are consistent with the values found by Chirwa (1996, 2003)for labor and fertilizer with respect to small-scale farming in Malawi. Table 7

Table 6. Output elasticities

95%-ConfidenceVariable Elasticity (Se) Interval

Labor 0.071∗∗∗ (0.018) [0.039; 0.109]- Inorganic fertilizer only farms 0.054∗∗∗ (0.004) [0.039; 0.060]- ISFM farms 0.088∗∗∗ (0.011) [0.001; 0.109]

Fertilizer 0.322∗∗∗ (0.149) [0.162; 0.489]- Inorganic fertilizer only farms 0.172∗∗∗ (0.006) [0.162; 0.194]- ISFM farms 0.470∗∗∗ (0.011) [0.433; 0.489]

Seed 0.681∗∗∗ (0.151) [0.419; 0.857]- Inorganic fertilizer only farms 0.532∗∗∗ (0.029) [0.419; 0.059]- ISFM farms 0.829∗∗∗ (0.019) [0.755; 0.857]

Note: ∗∗∗ p < 0.001; ∗∗p < 0.01; ∗p < 0.05; estimated for the sample mean using the original data;standard t-test formulas were used to test against the null hypothesis of no significance with respectto the estimated elasticities.

Table 7. Marginal physical products (MPP)—conventional inputs

Inorganic Fertilizera ISFMb Total SampleMPP (Maize kg/ha) (N = 110, SE) (N = 143, SE) Average (SE)

Labor (per pd)c 2.78∗∗∗ (0.51) 5.93∗∗∗ (0.32) 4.36∗∗∗ (0.33)Fertilizer (per kg)c 1.43∗∗∗ (0.08) 3.53∗∗∗ (0.19) 2.49∗∗∗ (0.18)Seed (per kg)c 10.99∗∗∗ (0.56) 69.10∗∗∗ (3.56) 40.18∗∗∗ (3.87)Total MPPc 15.21 78.57 47.03

Notes: aFertilizers include a combination of 23:21:0 + 4s and CAN.bISFM involves the application of inorganic fertilizers and incorporation of grain legumes, that is,groundnuts (Arachis hypogea) or pigeon peas (Cajanas cajan) in one system.cCalculated at sample means based on estimated parameters.∗∗∗ p < 0.001; ∗∗p < 0.01; ∗p < 0.05; estimated for the sample mean using the original data.


Table 8. Returns to scale by soil fertility management option

Soil Fertility Returns to 95%-ConfidenceManagement Option Scale (Se) Interval

Inorganic fertilizers 0.49∗∗∗ (0.01) [0.38; 0.59]ISFM 2.53∗∗∗ (0.07) [2.26; 3.06]Total Sample 1.27∗ (0.46) [0.38; 3.06]

Note: Returns to scale (RTS) difference between soil fertility management options is significant at(p < 0.001).∗∗∗ p < 0.001; ∗∗p < 0.01; ∗p < 0.05; estimated for the sample mean using the original data; standardt-test formulas were used to test against the null hypothesis of no significance with respect to theestimated returns to scale.

shows the marginal physical products (MPP) for the conventional inputs labor,fertilizer, and seed for farms using inorganic fertilizer only and farms using ISFMpractices.

As we use a flexible translog specification the estimated coefficients can not bedirectly interpreted. The relative increase in maize yield due to ISFM practices canbe obtained by estimating the elasticity for ISFM. The value shows a significantpositive change in output as a marginal increase in ISFM practices keeping allother factors constant and corresponds to an increase in maize yield of about78.6 kg/ha (the total marginal physical product for farms using ISFM practices intable 7). The estimated coefficient for the squared ISFM term in table 5 (ISFM ×ISFM) indicates a positive influence of an increase in ISFM use on the slope ofthe yield curve (i.e., the second derivative of maize output with respect to ISFMpractices). The use of ISFM improves the yield of maize for the average farmlocated in the ADD of Mzuzu by up to 7.8%, for an average farm located in theADD of Lilongwe by up to 8.8%, and for an average farm located in the ADD ofBlantyre by up to 2.8%. By this result, hypothesis (1) that ISFM increases maizeyield, statistically is confirmed.

In table 8, we compare the returns to scale associated with smallholder maizeproduction using alternative soil fertility management options. The results indi-cate that at sample average smallholder farmers exhibit considerable returns toscale, consistent with other previous studies (see e.g., Kamanga, Kanyama-Phiri,and Minae). Hence, the average farmer operates in a region of the production func-tion where the marginal yield of inputs is increasing (stage I in figure 1). However,whereas small-scale farmers applying inorganic fertilizers exhibit decreasing re-turns to scale, for those applying ISFM practices considerable increasing returnsto scale have been found (see table 6). The significant difference in returns to scalefor the two soil fertility options imply that there is still scope for smallholder farm-ers to improve maize yield by an increase of their production under integratedpractices: ISFM options improve the soil fertility and hence enhance the efficiencyof conventional input use.

These results further imply that assuming constant maize/fertilizer price ratios,the optimal yield response for inorganic fertilizer (as well as other inputs) is higherin the case of ISFM. Thus, with farmers facing more or less the same maize priceand input cost, the profitability of smallholder maize production is likely to be


Figure 1. Estimated total average and total marginal product curves

0

5000

10000

15000

20000

25000

AVP, MVP (in MK/input bundle unit)

units of input bundle xi

AVPinorganic

AVPISFM

MVPISFM

MVPinorganic

stage llstage l

Farmer 1 Integrated Soil Fertility Management

Farmer 2 Inorganic Fertilizer Only

10 25 40 55 70 85 100 115 130 145 160 175 190 205 220

Note: This figure is a stylized representation and is only meant as an illustration of the arguments.

higher when farmers integrate inorganic fertilizers with grain legumes (illustratedby figure 1).

Farmer 1, as the average farmer using ISFM, enjoys a higher marginal valueproduct (MVPISFM) as well as average value product (AVPISFM) than farmer 2, asthe average farmer applying inorganic fertilizers only (MVPINORG, AVPINORG). Asdepicted by figure 1 farmer 1 experiences increasing returns to scale and hencecould enhance the production of maize, however, farmer 2 experiences decreasingreturns to scale and hence should not increase his maize production further. Theaverage returns to scale for farmer 1 are significantly higher than those for farmer2. Although the yield effect implied by the marginal effect of ISFM is considerablylow (at 7.3% on average), given the low yields experienced by smallholder farmers,if we account for other crops such as grain legumes (groundnuts, soy, and pigeonpeas), the overall additional yield effect of ISFM is quite substantial. In fact, it islikely to be higher among farmers that are unable to afford optimal quantities ofinorganic fertilizer, but still have access to hybrid maize seed.

No information has been available with respect to the type of previous legumecultivated on the specific plot. Different types of legumes nevertheless fix differ-ent amounts of nitrogen in the soil (e.g., mucuna vs. beans). Chilongo presentedpartial budgets of different soil fertility cropping patterns for Malawi and con-cluded on a superior performance of the groundnut-maize rotation (net benefitof about 3,537 USD/ha) followed by the pigeonpea-maize rotation technology(net benefit of about 885 USD/ha) and the soybean-maize rotation (net benefit ofabout 544 USD/ha). However, empirical evidence on the relative yield effect ofsuch legumes can be, by comparing the estimates for total nitrogen in the soil aswell as for green manure, applied as potential proxies for the previous legume on


the plot. The estimation reveals that the marginal yield effect by nitrogen fixinglegumes is positive as can be generally expected. After controlling for possibleinteraction effects with soil-fertility management practices, the marginal yield ef-fect by nitrogen fixing legumes is still positive. The same findings apply withrespect to green manure applied on the specific plot: here the results show alsoa positive marginal yield effect as well as a positive yield effect after controllingfor possible interaction effects with the soil-fertility practice applied. However, ithas to be kept in mind that the nitrogen availability in the soil depends on theprevailing temperature and rainfall conditions. Nevertheless, this reasoning is inline with those by Whitbread, Jiri, and Maasdorp who reported a 64% higher yieldwhen maize is planted following green manure rotation compared to continuousfertilized maize. According to the ADD where the individual farm is located,small-scale maize production in Mzuzu and Lilongwe shows a higher yield thansmall-scale maize production in Blantyre. This result is somehow in line with theprecipitation and soil conditions summarized for the farms in the sample (seetable 2). The same was found for the interaction effect with ISFM practices. Maizefarms located in the ADD of Blantyre experience a relatively lower yield effectthan the rest of the farms in the sample.

These results corroborate those of past studies in many ways. Most studiesindicate that in general, ISFM options are more remunerative where purchasedfertilizer alone remains unattractive or highly risky, as is the case with the maize-based smallholder farming systems in Malawi (see e.g., Chirwa 2003). Applyingthe assumption that all farmers face the same input and maize price ratios, theseresults imply that on average (here at the sample mean), the use of ISFM in maizeproduction improves profitability compared to the use of inorganic fertilizer only.The marginal and total value products given in table 9 by using the market pricesfor inputs, maize grain, and wages impressively support these results for theconventional inputs labor, fertilizer, and seed.3

The marginal value product per unit of fertilizer and seed is significantly higherwhen farmers use ISFM. Hence, hypothesis (2) assuming a higher profitability ofconventional inputs by the application of ISFM practices in small-scale maize pro-duction is confirmed for these inputs. The marginal value product of labor is sig-nificantly higher for the ‘maize only’ system when farmers use ISFM but shows tobe slightly lower for the ‘intercropping/rotation’ system. These results agree withthose obtained using on-farm trials data, which indicate higher yields in greenlegume rotation systems compared to maize applied with inorganic fertilizer only.The optimal yield for groundnut/soybean rotation and maize pigeon pea inter-crop is higher than that of maize with inorganic fertilizer only (Kumwenda andGilbert; Gilbert 1998a and 1998b; Sakala, Ligowe, and Kayira; Chilongo 2003).Finally, table 10 shows the value cost ratios (MVCR) for the different inputs basedon the marginal value products and the observed input prices. It becomes clearthat the MVCR are higher for all inputs in the case of ISFM practices comparedto the case of organic fertilizer only. The same applies for the value cost ratioswith respect to the use of intercropping. However, the various crop rotations andmanagement practices needed if intercropping and ISFM is used together are la-bor intensive. This could be a reason for the lower value cost ratio for the inputlabor in this case compared to the use of intercropping practices combined withinorganic fertilizer only. The numbers in brackets indicate the value cost ratios


Table 9. Marginal value products (MVP)—conventional inputs

Maize Only

Inorganic Fertilizerb ISFMc Total SampleMVP (in MK)a (N = 110) (N = 143) Average

Labor (per pd)d 19.84 22.25 21.05Fertilizer (per kg)d 8.27 11.55 9.92Seed (per kg)d 73.19 378.09 226.34Expected total MVPd 5,669.32 22,927.19 14,337.66Observed total MVPe 2,705.83 19,268.79 12,104.66Difference Expected/observed (in%) 109.52 18.99 18.45

Intercropping / Maize and Legume Rotation

Inorganic Fertilizerb ISFMf Total Sample(N = 110) (N = 143) Average

Labor (per pd)d 22.31 21.15 21.73Fertilizer (per kg)d 8.30 12.09 10.20Seed (per kg)d 73.20 381.89 228.25Expected total MVPd 5,758.39 24,505.69 15,461.79Observed total MVPe 3,596.87 23,158.75 14,498.31Difference expected/observed (in %) 60.09 5.82 6.65

Notes: aMalawian Kwacha is the local currency (exchange rate 1 US$ = MK 109 in 2002–03).bFertilizers include a combination of 23:21:0 + 4s and CAN.cInvolves the application of inorganic fertilizers and incorporation of grain legumes, that is,groundnuts (Arachis hypogea) or pigeon peas (Cajanas cajan) in one system.)dCalculated at sample means based on estimated parameters and market prices for the sample year2002–3.eBased on observed values and market prices for the sample year 2002–3.fInvolves the application of inorganic fertilizers and incorporation of grain legumes, that is,groundnuts (Arachis hypogea) or pigeon peas (Cajanas cajan) in a rotation system.

for the case where only the amount of fertilizer is used provided by the StarterPacks distributed (0.1 ha worth of fertilizer, maize seed, and legume seed). Thisindicates that the use of fertilizer is profitable only at either low or high levels ofapplication. At very low levels, the payoff is ensured when it is combined withbest-bet technologies. At high levels, the payoff comes from the resulting higherproductivity, which offsets the low maize prices. Logically, for all different sce-narios the MVCR is higher as well as is the MVCR for the case where ISFM isused compared to the use of organic fertilizer only. In general, ISFM practices area financially viable alternative for smallholder farmers in Malawi.

Population and economic growth, agricultural production, prices, and govern-ment policies have historically influenced fertilizer demand. However, the currentstate of agricultural markets experiences a hike in world prices concerning nearlyall major food and feed commodities. The year 2007 had been characterized bypersistent market uncertainty, record prices, and an unprecedented volatility ingrain markets (FAO 2008). The declaration of the African Heads of State andGovernment at the Africa Fertilizer Summit in June 2006 to support an increasein fertilizer use4 has to be regarded as an answer to these recent developments


Table 10. Marginal value cost ratios (MVCR)—conventional inputs

Maize Only

Inorganic Fertilizer ISFMa Total SampleMVCR (N = 110) (N = 143) Average

Laborb

- min 1.51 1.69 1.62- max 2.68 3.01 2.86Fertilizerb

- min 0.68 (2.22)c 0.95 (3.10) 0.83 (2.72)- max 0.71 (2.09) 1.01 (2.93) 0.88 (2.56)Seedb

- min 3.23 16.70 10.84- max 4.38 22.72 14.75

Intercropping / Maize and Legume Rotation

Laborb

- min 1.70 1.61 1.65- max 3.01 2.86 2.93Fertilizerb

- min 0.68 (2.23)c 0.99 (3.25) 0.86 (2.81)- max 0.72 (2.11) 1.05 (3.06) 0.91 (2.64)Seedb

- min 3.23 16.83 10.92- max 4.39 22.89 14.85

Notes: aISFM involves the application of inorganic fertilizers and incorporation of grain legumes, thatis, groundnuts (Arachis hypogea) or pigeon peas (Cajanas cajan) in one system for ‘maize only’ and ina rotation system for ‘intercropping/rotation.’bCalculated at sample means based on estimated parameters and observed input prices for thesample year 2002–3.cMVCR in brackets: assuming the use of only fertilizer obtained by the Starter Pack (0.1 ha worth offertilizer, maize seed, and legume seed).

on the agricultural input and output markets (Chianu et al.). Maize productivitygrowth is still the first major target of the Agricultural Development Program ofthe Malawi government (World Bank). During the past two years (2006–07), over50% of the Ministry of Agriculture budget was allocated to distributing fertilizerand seed under the Agricultural Input Subsidy Program (AISP). The 2007–08 AISPprogram provides 170,000 tons of subsidized fertilizer to 1.7 million farmers (i.e.,100 kg per farmer) with more than half of all smallholder farmers are expected toreceive subsidized fertilizer. The World Bank expects that these investments willcontinue for the near future (World Bank). Table 11 shows the marginal cost offertilizer at current and future estimated prices (see Chianu et al., World Bank).The stark difference between subsidized and market prices for fertilizer are evi-dent (the subsidized price corresponds to about 15% of the actual market price).The estimated fertilizer prices follow scenarios suggested by the World Bank (seeWorld Bank). Table 12 shows the marginal value products for fertilizer at currentmaize prices and at estimated future maize prices based on a IFPRI price scenario


Table 11. Marginal cost of fertilizer—current prices and futureprice scenarios

MC (MKa Per kg MC (MK Per kg MC (MK per kgFertilizerb) Fertilizer) Fertilizer)(Subsidized) Price 2007–8c Market Price 2007–8d Future Price Scenarios 2015e

(A) (B)Minf Max Min Max Min Max Min Max17.46 18.54 116.41 123.60 883.91 938.59 3,724.81 3,955.24

Notes: aMalawian Kwacha is the local currency (exchange rate 1 US$ = MK 140 in 2007–8).bFertilizers include a combination of 23:21:0 + 4s and CAN.cFarm gate price 2007–8 (Chianu et al.).dBased on World Bank information (World Bank).eBased on price scenarios suggested in World Bank: (A) = 50% increase p.a., (B) = 100% increase p.a.(up to 2015).f Min and max values estimated based on price variation found in the sample year 2002–3.

Table 12. Marginal value products—current prices and future pricescenarios

MVP (MKa per kg Maize/ha) MVP (MK per kg Maize/ha)Farming Practice Market Price 2007–8d Future Price Scenarios 2015

Inorganic fertilizerb (A)e (B)f

(N = 110, SE) per kg 34.23 175.52 184.45fertilizer

ISFMc (A) (B)(N = 143, SE) per kg 84.51 433.23 455.31

fertilizer

Total sample average (A) (B)Per kg fertilizer 59.61 305.60 321.17

Notes: aMalawian Kwacha is the local currency (exchange rate 1 US$ = MK 140 in 2007–8).bFertilizers include a combination of 23:21:0 + 4s and CAN.cISFM involves the application of inorganic fertilizers and incorporation of grain legumes, that is,groundnuts (Arachis hypogea) or pigeon peas (Cajanas cajan) in one system.dMarket price 2007–8 (World Bank: Commodity price data, 2008 estimate based on import and exportparity prices).eBased on maize price scenario suggested in Braun: 26.3% increase p.a.fBased on maize price scenario suggested in FAO, 2008: 27.2% increase p.a.

outlined in Braun and an FAO, 2007 price scenario outlined in FAO (2008). It isevident, that at current maize prices the marginal value product per kg fertilizerused on farms applying ISFM practices is more than twice as high as the marginalvalue product per kg fertilizer used on farms applying only inorganic fertilizertypes. Table 13 finally summarizes the marginal value cost ratios for differentcombinations of marginal costs and marginal value products for fertilizer basedon the different estimated future prices.

Given current and expected future prices for maize and fertilizer (see table 13,MVCR I to IV) our analysis reveals that at current subsidized fertilizer prices the


Table 13. Marginal value cost ratios (MVCR)—current prices andfuture price scenarios

MVCR Ia MVCR IIb

Fertilizer: Subsidized Price 2007–8 Fertilizer: Market Price 2007–8Maize: Market Price 2007–8 Maize: Market Price 2007–8

Inorganic FertilizerPer kg Fertilizer- Min 1.85 0.28- Max 1.96 0.29ISFMPer kg Fertilizer- Min 4.56 0.68- Max 4.84 0.73Total Sample AveragePer kg Fertilizer- Min 3.22 0.48- Max 3.41 0.51

MVCR IIIc MVCR IVd

Fertilizer: Estimated Price Fertilizer: Estimated Pricescenario (A) [Subsidized] Scenario (B) [Subsidized]

Maize: Estimated Maize: EstimatedMarket Price (A) Market Price (B)

Inorganic FertilizerPer kg Pertilizer- Min 0.20 [1.31] 0.05 [0.31]- Max 0.21 [1.39] 0.05 [0.31]ISFMPer kg Fertilizer- Min 0.49 [3.23] 0.12 [0.77]- Max 0.52 [3.43] 0.12 [0.81]Total Sample AveragePer kg Fertilizer- Min 0.34 [2.28] 0.08 [0.54]- Max 0.36 [2.42] 0.09 [0.57]

Notes: All previous table notes apply here.aMVCR I: subsidized fertilizer price 2007–8 (Chianu et al.; World Bank) and maize market price2007–8 (World Bank).bMVCR II: fertilizer market price 2007–8 (World Bank) and maize market price 2007–8 (World Bank).cMVCR III: estimated fertilizer market price 2015 (World Bank scenario A: 50% increase p.a.) andestimated maize market price 2015 (IFPRI scenario: 26.3% increase p.a.), [estimated subsidizedfertilizer price 2015 (based on current level of subsidization, World Bank scenario A) and estimatedmaize market price 2015 (IFPRI)].dMVCR IV: estimated fertilizer market price 2015 (World Bank scenario B: 100% increase p.a.) andestimated maize market price 2015 (OECD/FAO scenario: 27.2% increase p.a.), [estimated subsidizedfertilizer price 2015 (based on current level of subsidization, World Bank scenario B) and estimatedmaize market price 2015 (FAO, 2007)].

marginal value cost ratios are well above 1 for fertilizer (MVCR I) showing againa two times higher ratio for farms using ISFM practices (4.56 to 4.84). How-ever, the marginal value cost ratios significantly drop below 1 for both farmingpractices when calculated at the current market price for fertilizer (MVCR II).


Nevertheless, the ratio for ISFM farms shows the highest value (about 0.68 to0.73). The marginal value cost ratios for both future price scenarios are dramati-cally dropping to values between 0.05 and 0.52 whereas the highest ratio is againobserved for farms using ISFM practices (MVCR III). Assuming the same level offertilizer subsidization in the future brings the marginal value cost ratios above 1reaching from about 1.31 (inorganic fertilizer only) to about 3.43 (ISFM practices).Keeping all other factors constant, this marginal ratio analysis reveals ISFM prac-tices significantly improve the profitability of smallholder maize production giventhe experts’ expectation of escalating fertilizer prices in the short-term future.5

Conclusions and ImplicationsThe study clearly shows that the yield of maize production applying ISFM

practices is higher than when farmers use inorganic fertilizer only. Furthermore,marginal values are higher, assuming farmers face the same maize prices andinput costs. These results are meaningful for smallholder farmers who can hardlyafford optimal levels of inorganic fertilizer and assist to dispel skepticism associ-ated with the benefits of ISFM. This is crucial for farmers who have been crowdedout of the agricultural inputs market for reasons of affordability despite differentinput subsidy programs. Depending on a consistent integration of grain legumeswith inorganic fertilizers as well as an effective access to improved maize varieties,ISFM provides scope for improving maize yield especially where the use of inor-ganic fertilizer is highly unaffordable. This points to the need for complementarypolicy interventions to promote the smallholders’ uptake of ISFM options: ISFMestablishment in smallholder farming systems can, for example, be facilitatedthrough seasonal credit provision to enable farmers to afford inorganic fertilizersand improved maize and legume seeds (see also Green and Ngongola; Mekuriaand Waddington; Chilongo). Finally, our analysis reveals that ISFM practices sig-nificantly improve the profitability of smallholder maize production given theexperts’ expectation of escalating fertilizer prices. The government’s current ma-jor food security strategy of massive fertilizer subsidies represents an inefficientuse of public resources. By linking fertilizer subsidization to use of ISFM practices,the cost of inorganic fertilizer could be reduced at the farm level and national levelin Malawi.

Endnotes1Following the definition used by the Department of Agricultural Research and Technical Services,

Ministry of Agriculture, Malawi (see e.g., Gilbert, 1998a or Chilongo, 2003), soil fertility managementcombines organic and inorganic technologies with the aim of improving the soil’s natural capital,thereby enabling it to support plant productivity in an economically viable and socially acceptablemanner. In this way, agricultural productivity is achieved while using available nutrient resourcesmore efficiently, effectively, and sustainably.

2We are grateful to an anonymous referee for pointing out the relevance of this important modelingissue.

3The average wage rate in Malawi’s rural areas is much lower than the minimum wage rate setby the Ministry of Labor and Vocational Training (MOLVT) and which amounted to approximately$0.52 (MK 56) per day for urban areas, and approximately $0.37 (MK 40) per day for all other areas.However, the MOLVT lacked the resources to enforce the minimum wage effectively and consequentlythe minimum wage was largely irrelevant for the great majority of citizens, who earned their livelihoodoutside the formal wage sector (see US Department of State).


4From the present average of about 8 kg/ha to an average of about 50 kg/ha by 2015 (Chianuet al.).

5Only limited conclusions can be drawn from this marginal ratio analysis. The full economic effectsin short and long term can only be assessed by a general equilibrium analysis based on all agriculturalinput and output markets.

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