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The following commonly accepted wisdoms are challenged :
Use of modern inputs remains dismally low
COMMON WISDOM : African farmers’ use of modern inputs is dismally low.
Chemical input use is not as low as is often assumed
Irrigation and tractor use is negligible
Input use varies strikingly within countries
Modern inputs are often not combined to reap agronomic gains
Input intensification is happening for maize in particular
Larger farms and plots receive inputs less intensively
Input application does not adjust to farmer-perceived soil quality
Few households use credit to purchase modern inputs
There are gender differences in input use
National-level factors explain bulk of modern input use variation
In sum, modern input use is not as low as is commonly believed, but there is room for considerable improvement, in both the level and method of input use. Although the conventional wisdom remains largely true, some movement is occurring on Africa’s agricultural input front.
The central message is that governments need to build on and learn from these achievements. Our findings open up a range of important new policy research questions amenable to further exploration.
Conventional wisdom holds that Sub-Saharan African farmers use few modern inputs such as improved seeds, fertilizers and other agro-chemicals, machinery, and irrigation. Is this true following several years of high food prices, concerted policy efforts to subsidize fertilizer and hybrid seed use, and increased public and private investment in agriculture? This study revisits perceptions about Africa’s agricultural input landscape, using data from more than 22,000 households and 62,000 plots in Ethiopia, Malawi, Niger, Nigeria, Tanzania, and Uganda. Data were collected under the Living Standard Measurement Study–Integrated Surveys on Agriculture Initiative.
Input use across Sub-Saharan Africa is more complex than prevailing beliefs and macroscale statistics suggest. Here are ten of the most striking newly verified facts: Most smallholders in the countries studied use rudimentary technologies and eschew the use of modern inputs:
Two-thirds report no use of inorganic fertilizer.
Eighty-four percent do not use agro-chemicals.
Only 1 to 3% of land cultivated by smallholders is irrigated, and no more than 10% of households have any form of water control on agricultural
Accurate data on the use of improved seeds remain hard to find.
Inorganic fertilizer use is significant in Nigeria (41% of households), Ethiopia (56%), and Malawi (77%), and one-third of households in Ethiopia and Nigeria use agro-chemicals.
Tractor ownership is low, but less so in Ethiopia, Niger, and Nigeria, suggesting that community rental or sharing schemes facilitate mechanization.
Within-country input use varies strikingly across subnational regions and agro-ecological zones, with the richer and—surprisingly—the less educated typically using more inputs.
Although many modern inputs (particularly inorganic fertilizer, improved seeds, irrigation) perform best when used together on the same plot, most households do not so (see figure). Improved agronomic practices remain an important focus for extension services.
Input intensification is happening for maize in particular. Given that maize is not a cash crop, this finding is promising.
The literature suggests that yields fall with farm size and that input use falls with farm and even plot size. Household-level factors (such as distance to market and household-specific price of inputs and outputs) cannot explain this puzzle, which requires further research.
Farmers do not significantly vary fertilizer application rates according to perceived soil quality, raising another opportunity for gains.
Less than 1% of households (except in Ethiopia) use formal or informal credit to purchase modern inputs, corroborating evidence about the weakness of agriculture input credit markets in Africa. Despite recent advances, much scope remains for deepening financial rural markets.
Male-headed households apply, use, and own more modern agricultural inputs than female-headed ones. Closing this gap would help empower women and raise their income.
Household socioeconomic status explains little of the inter-household variation observed in input use rates. Hence, policy tools can help increase the use of modern inputs.
2. Land, labor and capital markets remain largely incomplete
Factor markets, which include labor, capital and credit, regularly fail African farmers
The pattern of market failures is general and structural, not related to the head-of-household’s gender, or to geographic characteristics such as distance to roads or to large population centers
In some countries, the degree of market failure varies between agro-ecological zones, suggesting that market performance may depend in part on agro-climatic factors outside households’ control POLICY MESSAGES:
The research supports the current beliefs and discussion on African agricultural and rural development, showing that there is a pressing need to address widespread, systemic market failures that impede productivity growth and poverty reduction.
Agricultural factor markets in Sub-Saharan Africa are widely believed to be failing or incomplete because of bad roads, unreliable electricity and telecommunications services, insufficient credit and insurance, tenure systems that do not ensure secure property rights, corrupt officials, crowded ports, slow technological development, labor supervision problems, and so forth. This belief pervades the development community’s policy briefs and strategic plans for Africa.
However, priority setting in this area is based largely on longstanding assumptions about markets rather than on rigorous research using current data. Using data from the recent and ongoing Living Standard Measurement Study-Integrated Survey’s on Agriculture (LSMS-ISA), a careful empirical study of the common belief that factor market failures are widespread in rural Africa was completed in Ethiopia, Malawi, Niger, Tanzania, and Uganda.
First, the study provides a comprehensive overview of farmers’ factor market participation. In contrast to prevailing wisdom, it shows that a large share of farmers transact in agricultural labor or land markets. Second, it tests for failures in markets serving agrarian households. It predicts that when markets are complete and competitive, households decide about production and consumption separately. If this hypothesis holds, households allocate resources to maximize farm profits first and condition their consumption choices on the resulting budget. The paper hypothesizes that household size does not affect the amount of farm labor used. If a farmer can transact freely and at market determined prices, it should not matter whether his or her household consists of one person or 10.
For the five countries studied, the research notes a general and structural pattern of market failures that does not vary meaningfully with the gender of the household head or geographic characteristics such as distance to roads or to large population centers. In some countries, the degree of market failure varies between agro-ecological zones, suggesting that market performance may depend in part on agro-climatic factors outside households’ control. However, the overall message concerns a pressing need to address widespread, systemic market failures that impede productivity growth and poverty reduction.
Policy and Research Implications
Researchers must locate the sources and causes of factor market failures more precisely. Programming and policymaking should take into account that factor markets in major Sub-Saharan African countries are not fully integrated, and interventions that treat the rural farm economy as a unified, well-functioning whole are unlikely to achieve the desired objective.
3. Land is abundant and land markets are poorly developed
Land rental markets are emerging in most of the countries studied.
Differences in land endowments and productivity create potential for land markets to equalize endowments and contribute to higher levels of productivity.
Land rental markets improve equity by promoting land access to those with limited land.
Labor-rich and young households are more likely to participate in land markets in most countries. (Niger is an exception.)
Female heads of households are much less likely to lease in land. This finding suggests barriers to land market participation by women.
Rental market performance seems lower where there are greater risks of expropriation.
Legal framework: To support sustainable land management, investment in land improvements, and efficiency-enhancing transfers, property rights that effectively protect against the threat of land loss are essential. Low-cost models to secure these rights are available in many countries.
Institutional development: easy access to unambiguous and comprehensive information on land rights is key for transparency, land market functioning, and planning.
Women’s rights: Land and associated resources make up the lion’s share of most households’ wealth. Women’s use rights, control rights, and transfer rights to land will thus affect not only land use but also women’s ability to start independent nonfarm enterprises.
Institutions facilitating efficiency-enhancing land transfers at low cost can increase the productivity of land use, help diversify the economy, and foster economic development. Yet, the general perception is that land markets are still largely absent in Africa, and that access to land is even more difficult for women.
This study uses data from the World Bank Living Standards Measurement Study– Integrated Surveys on Agriculture (LSMS–ISA) initiative implemented in six countries (Ethiopia, Malawi, Niger, Nigeria, Tanzania, and Uganda) to explore these issues. First, the data are used to describe whether and how rural households are participating in land markets and the characteristics of those households. Second, the study analyzes the determinants of land market participation by taking a multivariate econometric approach, the findings of which are interpreted within each country’s broader institutional context.
The descriptive findings reveal five key features of land markets in Africa today (figure):
The amount of land used for crop cultivation by most households remains small (between 0.3 and 0.51 ha/adult), consistent with limited mechanization. The main exception is Niger, where land is less fertile and cultivation more extensive.
Land rental market activity is not negligible (between 6 and 20 percent of households report to rent in land).
Gender patterns in land access differ across countries. Female headed households are less likely to rent in land in Ethiopia, Malawi and Niger, but not in the other countries.
Rental markets prove not dis-equalizing; in fact they are especially instrumental in helping smaller smallholder access land.
Returns to family farm labor (per adult day) vary substantially across countries (number of adult family labor days in agriculture are not available in Ethiopia and Nigeria), but land markets are not found to affect productivity much so far.
The multivariate findings suggest that land market performance is lower where:
implicit or explicit restrictions on land rental exist;
perceived threats of uncompensated expropriation reduce subjective tenure security;
where policies to document existing land rights exist but are not implemented or implemented in an ad hoc manner, or in a way that leaves out specific groups of land holders, in particular women.
Three policy issues emerge from this study:
Legal framework. To support sustainable land management, investment in land improvements, and efficiency-enhancing transfers, property rights that effectively protect against the threat of land loss, and are transparent and known to the population, are essential. Low-cost models to secure these property rights in ways that can evolve over time are available and implemented in many countries.
Women’s rights. Land and associated resources make up the lion’s share of most households’ wealth. Women’s use rights, control rights, and transfer rights to land will thus affect not only land use but also women’s ability to start independent nonfarm enterprises. More could be done to improve data for further policy research—such as adopting uniform recall periods and categories of shocks and coping mechanisms.
Research agenda. Consistently implementing improvements in questionnaire design to obtain data on nonagricultural land, land acquisition history, individuals’ rights, land-attached investment, tenure and ownership status, prices, and output and input at the plot level can help in better harnessing this potential for analysis.
4. Access to credit is limited
The use of credit (formal, informal, tied, and untied) for financing modern inputs is extremely low.
This applies in all countries, across farm sizes and for food as well as cash crops.
Farmers primarily finance modern input purchases with cash from nonfarm activities and crop sales.
Tied output-factor market arrangements with input traders and output traders only play a minor role in financing external inputs, but appear to be relatively widely used for labor credit.
“Traditional cash crop” farmers selling to processors rarely receive credit from processors, except in a few enclaves, such as larger tobacco farmers in Tanzania.
Nonetheless, access to loans (mostly informal) has a favorable effect on fertilizer use .
Nonfarm self-employment is associated with greater use of fertilizers.
Rural development policies and programs that spur broad development of the rural nonfarm sector would benefit farm input purchases and thus productivity and food security. These policies and programs would be important complements to credit policies and programs.
Recent evidence indicates that many farmers in Sub-Saharan Africa purchase external inputs such as fertilizer, seeds, and pesticides and herbicides. However, there is limited information on how the increasing use of modern inputs is being financed. This study investigates empirically how African smallholders finance the purchase of modern external inputs and revisits conventional wisdom about how African farmers finance agricultural activities
There is no current and systematic inventory of how farmers pay for inputs. To fill this gap, the study undertakes a cross-country empirical examination of input finance among smallholders, using recently available, nationally representative Living Standards Measurement Study farm household survey data sets. These data comprise more than 10,000 households in four countries: Malawi, Nigeria, Tanzania, and Uganda. The study focuses on purchases of “external inputs,” that is, nonlabor variable inputs (fertilizer, pesticides, and seeds) and of labor. Relying mostly on descriptive statistics on formal and informal tied and untied credit sources, the study explores the influence of crop types (cash crops versus food crops) and farm size. It also uses econometric regression methods to examine the correlates of input purchases.
There is much variation across countries in modern external input purchases.
The use of credit for input purchases is rare.
When used, credit is more commonly used for fertilizers than for other external inputs.“Tied credit” is also rare for external inputs. Even traditional cash crop farmers rarely receive credit from processors. Tied credit is common for labor inputs. Earnings from nonfarm employment and cash from output sales are the main sources of input financing.
Agricultural commercialization and RNFE development are the key sources of input financing, but they are often also relatively concentrated among a subset of households. Making agricultural commercialization and RNFE development inclusive is thus key.
Further analysis of the factors that explain the limited use of noncash income sources to finance external input purchase is also called for. In addition to credit availability, issues such as the associated interest rates and expected returns to investing in modern external inputs should be explored.
5. Labor Productivity and Employment Gaps in Sub-Saharan Africa
In poor economies, agriculture is typically the sector that employs the most people and uses labor least productively.
Microeconomic analogs of sector and cross-sector productivity gaps are smaller than those generated from national accounts data.
Cross-sector productivity gaps disappear almost entirely when they are measured on the basis of hours worked.
Cross-sector productivity gaps in national accounts data may reflect gaps in employment levels rather than gaps in returns to hours worked.
Most non-farm activities that rural households are engaged in (whether in industry or services) have close links to agriculture
Because of this, agriculture continues to play a key role in these Sub-Saharan African economies
Agriculture plays a key role in Sub-Saharan African economies, and cross-sector employment gaps are a phenomenon warranting future research. Inter-sectoral differences in annual earnings per worker arise from differences in employment volume (hours per worker of labor supplied) rather than wages or productivity per hour of labor supplied. Understanding why these employment gaps exist is crucial for understanding what policy responses can address them. In addition to decreasing agricultural employment gaps, policies to enhance access of rural workers to the industry and services sectors are clearly critical.
Structural change refers to the reallocation of labor from one low-productivity sector to another, higher-productivity sector and the economic growth resulting from that shift. Therefore, the premise of higher returns to labor outside of agriculture is central to the structural change process. National accounts data suggest that nonagricultural labor in Africa is six times more productive than is agricultural labor. Are productivity differentials really that high? If so, why does so much labor remain in rural areas, and why does rural income diversification remain low? Understanding microlevel cross-sector productivity differences and how they relate to sector allocation is crucial to understanding the forces that power agricultural labor exits.
Data from the Living Standards Measurement Study–Integrated Surveys on Agriculture (LSMS-ISA) are used to measure sector participation, time use, and labor productivity. Those key parameters are examined on a microlevel to reflect the perspective of individuals and firm owners making labor allocation decisions in developing countries. Furthermore, microdatasets contain the variables required to examine the assumptions of macrostatistics. The LSMS-ISA data were analyzed as follows:
At the individual and household levels, annualized labor supply aggregates were constructed by sector (agriculture, industry, and services) and by activity (household-operated farm enterprises [farms], household operated nonfarm enterprises [NFEs], and wage labor market participation).
Firm-level labor demand aggregates, which included labor inputs of hired workers and family members, were constructed for farms and NFEs.
Returns for operating a farm were based on an annual net farm revenue derived from rural income–generating activities calculations. An annualized net firm revenue variable was constructed using either reported profits or household estimates of gross NFE revenue and costs. Returns for labor market participation comprised wage workers’ gross wages, including in-kind payments and gratuities.
Two types of average labor productivity measures were constructed using the labor supply variables and returns variables: the per worker measure (output per worker per year) and the per hour measure (output per hour of labor supplied to each activity per year).
All activities were assigned to their respective sectors of the economy using Industry Standard Industrial Classification codes. Sector-level aggregates of labor supply and returns were generated for each household.
The results of the study are as follows:
Microlevel cross-sector labor productivity gaps are smaller than those generated using national accounts data. Annual per capita household consumption levels are quite similar between primarily agricultural households and households of other sectors, thus confirming small cross-sector gaps.
Cross-sector labor productivity gaps almost vanish when computed on a per hour basis (see figure). The disparity between per worker and per hour measures arises because nonagricultural laborers work, on average, far more hours per year than do agricultural laborers.
Intersectoral differences in annual earnings per worker arise from differences in employment volume (hours per worker of labor supplied) rather than wages or productivity.
Rural activities in the industry and services sectors are very closely linked with agriculture. A large portion of NFEs and jobs relate to buying and selling agricultural products, processing raw agricultural materials, or providing services that support farm production.
The analysis emphasizes agriculture’s key role in Sub-Saharan African economies, while also raising questions about agricultural employment gaps, their determinants, and the ways they affect economy-wide labor productivity growth. A between-sector gradient in annual output per worker remains to be exploited. Improving annual output per worker within agriculture, the highest participation sector by far, requires a better understanding of smallholder farmers’ labor demand.
6. Women perform the bulk of Africa’s agricultural tasks
Women provide the bulk of labor input in African agriculture, regularly quoted to be 60 to 80%.
Analysis of individual labor input data from Ethiopia, Malawi, Niger, Nigeria, Tanzania, and Uganda puts the female share of labor in crop production across these countries at 40%.
The share varies across countries, from 24% in Niger to 56% in Uganda, but remains consistently well below the regularly quoted 60-80%.
Accounting for the gender and knowledge profile of the respondents does not overturn this lower than expected female labor share in Africa’s crop production.
There are no systematic differences in female labor shares across staple and cash crops or across agricultural tasks.
Two messages emerge. First, the findings question prevailing assertions that increasing female agricultural productivity would yield substantial gains in aggregate crop output. Nonetheless, this should not be taken to mean, that investment in female labor productivity in agriculture cannot be a high return activity to reach other objectives, such as female empowerment or improved nutritional outcomes of children. Establishing this requires further research. Second, nationally representative household surveys remain key to inform policy making, including to get the stylized facts right.
Women’s large overrepresentation in agricultural tasks, combined with the existence of a gender gap in agricultural productivity and the need to boost Africa’s agricultural output is motivating increased attention to raising female agricultural productivity. Yet can it really be true that women’s labor share in African agriculture amounts to 60-80%, as is regularly mentioned in policy circles? Women make up only about 50% of the population and are already tasked with a host of other domestic duties. If women do 60-80% of the work in agriculture, what would the men be doing, especially given that in rural Africa agriculture is still the primary economic activity for most men?
Second, good data on labor input in agriculture are hard to come by, let alone good labor input data disaggregated by gender. It does not surprise then that the 60-80% labor share figure can in fact only be traced back to an undocumented quote in a 1972 United Nations report holding that: ‘Few persons would argue against the estimate that women are responsible for 60-80 [percent] of the agricultural labor supplied on the continent of Africa.’ (United Nations Economic Commission for Africa, 1972, p. 359). So, what is the labor contribution of women to agriculture in Africa?
The Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) surveys provide a unique opportunity to put the female labor share estimates on more sound empirical footing. They cover six countries (Ethiopia, Malawi, Niger, Nigeria, Tanzania and Uganda) which comprise 40%of the Sub-Saharan African population and span a wide array of farming systems.
They collect for each household member its labor input per plot per agricultural activity. This enables the calculation of the national female labor share in crop production as the total labor input in crop production provided by women across all households (both rural and urban) divided by the total labor input provided by men and women.
Potential sensitivity of the findings to the gender and knowledge profile of the respondent is further analyzed. Male or less knowledgeable respondents may for example over- or underestimate contributions by female household members and vice-versa.
The results of the study are as follows:
On average across the six countries, the female share of labor input into crop production is just 40%, substantially less than the widely quoted 60-80%.
There is substantial variation across countries, with Uganda, Tanzania and Malawi recording shares slightly above 50% and the female labor shares in Ethiopia and Niger well below half (29% and 24% respectively).
The average share for Nigeria is 37%. It declines, to 32% in northern Nigeria and rises to 51% in the south. The ability of the data to distinguish these differences, which are consistent with expectations, provides confidence in the approach.
Finally, robustness analysis confirms that the reported labor shares can be sensitive to the gender and knowledge of the respondent. Yet the effects are sometimes positive and sometimes negative depending on the country. Considering extreme cases (all respondents knowledgeable and female or male) only increases the female shares by 5 to 8% at most and does not change the core finding of much lower female contribution to crop production than currently perceived.
Figure: Female labor input in crop production is well below 60% across countries
What does this all mean for policy?
First, the policy priority for females in agriculture is not so clear-cut. The lower than expected female labor shares (well below 50% in some countries) do not, as such, support universally disproportionate attention to female farmers to boost crop production. That said, investing in female agricultural productivity may well be a high return activity to reach other objectives such as increasing female empowerment. Yet this requires further study.
Second, despite the power and popularity of randomized control trial, the findings underscore the continuing importance of nationally representative household surveys including to query common wisdom and put the policy debate on solid empirical footing.
7. Agroforestry is gaining traction
Do Trees on Farms Matter in African Agriculture?
Trees on farms are negligible
Trees are widespread in the five Sub-Saharan African countries studied, with more than 30% of all rural households reporting cultivating trees on their farms.
The economic contribution of on-farm trees is non-negligible: they provide 17% of total gross income among tree growing households and 6% on average across all rural households.
Gender, land and labor endowments, and especially forest proximity and national context are key determinants of on-farm tree adoption and management.
Trees on farms in Sub-Saharan Africa are typically more widespread and important than previously thought. They provide a significant source of income for many households across the continent and, in some contexts, a measure of food security. Because data limitations prevent a proper account of the indirect (environmental effects of trees on farms (soil conservation, biodiversity, etc.), they are likely even more important than the numbers presented here suggest. The implication is that governments and others should raise the profile of trees as an important crop and recognize that trees on farms can be an integral part of landscape planning, particularly in the face of a changing climate.
Trees on farms are often overlooked in research and policy on agriculture, forests, and rural livelihoods in Sub-Saharan Africa. Forestry as a field largely focuses on trees in forests rather than outside them. The focus in agriculture is usually on annual crops. One consequence of this separation is limited empirical knowledge of the prevalence of trees on farms and their economic contribution across the continent. Sub-national case studies suggest that on-farm trees can be important to household welfare, but there is little national-scale evidence.
This study uses nationally representative data from the Living Standards Measurement Study – Integrated Surveys on Agriculture (LSMS-ISA) in five African countries: Ethiopia, Malawi, Nigeria, Uganda and, Tanzania. Together, these countries represent 41% of the population in Sub-Saharan Africa and cover a wide array of agro-ecological zones. The dataset includes information from more than geo-referenced 20,000 households surveyed during 2010-2012. This also permits examining the effect of variables such as distance to forest, population, soil quality, and other relevant variables. This information is used to picture and explain the prevalence of trees on farms across the study countries.
1. Trees are widespread on farmland across LSMS-ISA countries.
On average, almost a third of landholders report trees for productive use on their farms.
Trees on farms were especially prevalent in Tanzania, Ethiopia, and Uganda (55, 38, and 30%respectively), but were less common in Nigeria and Malawi, where 16 and 22% (respectively) of landowners report having trees.
The prevalence of trees on farms varies by tree type. Fruit trees are especially widespread in Tanzania (reported on the farms of 45% of landholders). Tanzania also has the highest proportion of trees for timber and firewood (18%) while such trees were minimal or poorly recorded elsewhere.
About 25% of landowners reported tree cash crops across four of the five study countries, with Malawi an outlier where less than 1% reported cultivating such trees.
2. The contribution of trees on farms to rural livelihoods is non-negligible
Trees can have multiple functions (e.g. gardens, production, inter-cropping, among others), which make them a valuable asset within the farm productive structure.
Most of the products (fruits and tree cash crops) are sold, though in Ethiopia and Uganda, a sizeable share of the fruits is also directly consumed on the farm.
Trees on farms accounted for 17% of total gross income among tree crop growers, and 6% on average across all rural households.
3. Drivers of on farm tree growing
Multivariate analysis shows that the adoption of and land allocation to trees on farms is highly influenced by national context, accounting for about 40% of the explained variation.
Proximity to forests is also an important predictor of on farm tree presence.
Female headed households were less engaged in tree growing while households with larger landholdings tended to allocate more land to trees.
Tree growing on farms is more common than anticipated and it contributes a non-negligible share of income for many rural households.
Policy can make a difference: national institutional and other contextual factors are major predictor of on-farm management.
Further investment in the inclusion of forestry modules in household surveys can help strengthen the information base on on-farm tree growing. Otherwise, the non-negligible contribution of trees on farms risks being ignored and left out in agricultural and landscape policy design.
8. African agriculture is intensifying
Is African Agriculture Intensifying? The Status in Six African Countries
Population pressure and improved market access are intensifying African agriculture.
Fallow practices for regenerating fertile soil have virtually disappeared in the six countries studied; Ethiopia, Malawi, Niger, Nigeria, Uganda and Tanzania,Except in Ethiopia, Malawi, and Nigeria, the proportion of households using chemical fertilizers is too low to maintain or restore soil nutrients removed by plants Population pressure and market access have triggered an inadequate response with respect to irrigation and technology use
In response to rising populations, market opportunities from urbanization, better market access and cropping intensities have increased, but use of inputs, technology, and investment hasbeen lower than expected. In the six countries studied, cropping intensification, which provides opportunities for optimizing crop production, appears to have induced less use of inputs and less investment than projected.
The Boserup-Ruthenberg (BR) theory (Boserup 1965; Ruthenberg 1980), which has long been used to explain determinants of agricultural growth, holds that both population growth and market access can lead to a virtuous cycle of the higher use of organic fertilizers, and investments in mechanization, land development, and irrigation. This cycle can offset the negative impact of population growth on farm sizes, maintain or increase per capita food production, and increase farmers’ incomes. An extensive body of evidence tests the BR hypothesis in Africa and often confirms it.
In the past two decades, rapid population growth has stressed African farming systems while a sharp increase in urbanization and economic growth has provided new market opportunities for farmers. This paper investigates whether these changes have resulted in rapid intensification of farming systems, permitting rapid agricultural growth and increased incomes for farmers.
Using the first round of data from the Living Standards Measurement Study–Integrated Surveys on Agriculture, researchers took a comprehensive, internationally comparable measure of the agro-ecological potential using the modeling of estimated attainable crop yields across all agricultural areas worldwide. In addition, as a proxy for urban demand, researchers also took a measure of urban gravity that a location experiences with respect to all urban centers in the country with current population exceeding 500,000.
The analysis shows that patterns of intensification observed across countries are not entirely consistent with the BR theory. Given the rise in population, improvements in infrastructure, and growing urban demand, land use intensity has reached the stage where land is cropped every year (permanent cropping) in all six countries. As the BR framework predicts, fallow areas have virtually disappeared. But cropping intensities, involving the use of organic and chemical fertilizer and irrigation, are much lower than expected. Cropping intensity (see figure) is highest in Uganda because of its bimodal rainy season and lowest in Malawi and Tanzania. Although it is greater than one in all six countries, indicating they have reached the stage of permanent cropping, it could have been higher given the population density and increased market access—especially in Malawi, where agro-ecological population pressure is already high and, contrary to the BR model, land use intensity is low.
As other studies note, organic and chemical fertilizer use, though improved in Ethiopia, Malawi, and Nigeria, is too low to maintain soil fertility. Investments in irrigation also fall far short of what the high population pressures imply (partly explaining the lower cropping intensities). Across the six countries, average area irrigated per farm is only 0.03 ha, and just 4.4% of the total area is irrigated. Inconsistent with the BR model, irrigated area is highest in Tanzania (0.45 ha), where land pressure is lowest, and lowest in Malawi (0.03 ha), where land pressure is highest. Warm arid areas have the largest mean irrigated area per farm. Warm semiarid areas come next, followed by cool semiarid and warm sub-humid areas.
Intensification in many African countries appears to be far less beneficial to farmers than expected, perhaps in part because of the poor policies and public agricultural development expenditures prevailing during the 1970s and 1980s. International prices remained extremely low up to 2006. Institutions, public investment, and private investment take time to respond, leaving hope for accelerating response in the future. Long-running panel data and several intensification variables are needed to test the BR framework further and to discover whether agricultural involution is occurring in parts of Africa.
9. Seasonality continues to permeate rural livelihoods
Does Seasonality Continue to Permeate African Rural Livelihoods?
Seasonality permeates rural livelihoods, but is increasingly less considered by analysts and policy makers
Pre-harvest maize price levels (wholesale) exceed post-harvest price levels on average by 31 percent across seven African countries (ranging from about 55 percent in Malawi and 20 percent in Ethiopia—see figure)
Seasonality in prices tends to be higher for maize than other cereals, but also varies a lot across regions within countries, and is highest for (perishable) tomatoes. Rice and cassava prices show the lowest seasonal variability.
For three of the countries studied (Malawi, Tanzania and to a lesser extent Uganda), food consumption reflects these price patterns, and is lower in the pre-harvest hungry months, particularly for poor households.
Maize price fluctuations are twice those observed on the South Africa Future Exchange suggesting substantial scope for improvement and welfare gains; for example through better access to credit and better coping strategies for households, more secure storage at village level, reduced transport costs, and improved intra-African trade.
Seasonality in prices, and possibly welfare, remains much of an issue in many African food markets.
Seasonality (in food prices and consumption) was much studied in the 1990s and shown to be associated with significant fluctuations in hunger and nutrition within a given year. Since then the topic has largely disappeared from the policy debate as well as in project design and the academic literature.
Against this background, this study (two papers) systematically revisits the question of seasonality in Sub-Saharan African livelihoods. It examines the extent of seasonal patterns in food prices as observed during 2000-2012 across 196 market locations in seven countries (Malawi, Tanzania, and Uganda, Ethiopia, Niger, Burkina Faso and Ghana) for a range of food products. While essentially descriptive in nature, the papers fill an important empirical void in the current understanding of the evolution of food prices and consumption across seasons. They apply time series econometric methods to administrative market price information to control for price trends; they systematically look across different food crops, markets (wholesale and retail) and agro-ecological settings; and for three countries (Malawi, Tanzania and Uganda); they further exploit the Living Standard Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) to explore how the seasonal price fluctuations affect (food) consumption patterns.
Overall, the findings suggest that the current neglect of price seasonality and the inability of households to smooth their consumption fully across months within each year may be premature. Through econometric analysis of monthly food price series across 196 locations in seven countries during 2000–12, it is shown that:
Averaging across food crops, regular seasonality appears to contribute around 14 percent of overall food price volatility in the seven countries examined, with wholesale prices during the peak months estimated to be 28 percent higher than those during the troughs.
While some seasonality in food prices is natural given storage costs, the levels observed in the domestic markets of the study countries in this paper are two (maize) to three (rice) times higher than those in the international markets.
There is a strong negative association between price seasonality and fluctuations in both food and non-food consumption within a given year. This indicates that a large proportion of African households have a limited ability to smooth consumption, and that there are good indications
that such fluctuations may partly follow from an excessive seasonal behavior of staple prices.
Policy and Research Implications:
The burgeoning literature on food price volatility, much of which focuses on financial and energy market influences on food prices, has largely neglected the more prosaic deterministic seasonal factors most immediately obvious throughout Africa. These could be reduced through better access to financial markets for households, more secure storage at village level, a reduction in transport costs, and increased intra-African food trade.
Turning to methodology, poverty measurement is likely to be sensitive to seasonal issues. This should be taken into account in survey design since a sample which is nationally random may fail to be seasonally random.
While indicating appreciable welfare gains, the results on consumption are based on limited data and are suggestive rather than conclusive. Future work will bring in further survey waves but may also extend to a wider range of welfare indicators, including indicators of longer term impacts such as child growth and nutrition.
10. Post harvest losses are large
Is Post-Harvest Loss Significant in Sub-Saharan Africa?
Self-reported on-farm postharvest loss (PHL) is estimated to be between 1.4 and 5.9% of national maize harvest, much lower than Food and Agriculture Organization of the United Nations (FAO) estimates
PHL is concentrated among less than one-fifth of households
PHL increases with humidity and temperature and declines with better market access, post-primary education, higher seasonal price differences, and possibly improved storage practices
Farmers estimate PHL losses to be substantially less than those of the FAO. Self-reported estimates are arguably the relevant metric when assessing likely adoption of better PHL handling techniques.
The issue: traditional estimates
FAO estimates from 2011 suggest that as much as 37 percent of food produced in Sub-Saharan Africa is lost between production and consumption. Estimates for cereals are 20.5 percent. For post-harvest handling and storage loss only, the FAO estimate is 8 percent, and the African Post-harvest Losses Information System (APHLIS) estimate is 10-12 percent.
These high estimates have motivated international attention to PHL. Yet interventions typically focus on improving on-farm grain storage techniques for small-scale farmers. The estimates use extrapolation from purposively sampled (and often older) case studies that may focus on areas where PHL is largest. More and better quantification of (on-farm) grain loss is needed (which can then be compared with the costs of improved postharvest practices). Also needed is a better understanding of farmers’ behavior in adopting improved postharvest technologies.
The Living Standards Measurement Study–Integrated Surveys on Agriculture includes nationally representative household surveys from six countries in Sub-Saharan Africa. Farmers provided socioeconomic information about their households, agricultural production systems, and share of PHL for their crops. This paper uses these self-reported estimates to gauge the extent of PHL among maize producers and its agroecological and socioeconomic drivers in Malawi, Tanzania (two surveys), and Uganda. Although prone to measurement error, farmers’ self-reported estimates are drawn from nationally representative samples, thereby avoiding overestimation from sample selection bias. Harmonization across surveys facilitates cross-country comparison. And estimates likely reveal the losses that farmers deem important, which is critical when assessing likely adoption of improved postharvest storage techniques.
Estimates national maize harvest loss are between 1.4 percent in Malawi and 5.9 percent in Uganda, which are substantially lower than the estimates of FAO and APHLIS.
Only a minority of farmers reports a loss, with average losses ranging between 20 percent and 27 percent of the total maize harvest.
Use of improved storage technologies is extremely low, between 0.6 percent in Uganda and 12 percent in Tanzania. Use of postharvest crop treatment, such as spraying or smoking, is higher, from 63 percent maize harvest in Uganda, where PHL is highest, to 10.8 percent in Malawi, where it is lowest.
More in-depth multivariate analysis of the Tanzania 2008 food crisis experience shows the following:
Economic incentives substantially reduce PHL, widening the gap between pre- and post-harvest food prices in more remote markets.
Climatic factors (particularly heat and humidity) substantially increase PHL.
PHL is less for farmers with post-primary education.
Household poverty does not appear to affect PHL.
Female-headed households tend to experience lower PHL.
PHLs were less likely to occur in households with the head having post primary education (not just completed primary).
The following are important when planning, designing, and targeting PHL interventions:
Current estimates of on-farm PHL are high when compared with self-reported estimates for maize. Yet self-reported estimates are more relevant indicators of demand for better storage and handling techniques.
Targeting is key. As only a small proportion of households report a loss, ‘one-size-fits-all’ approaches are bound to fail (as highlighted 30 years ago by Lipton). Understanding why some farmers suffer high levels of PHL and not others is an essential step to designing the right policy interventions.
Policies outside the sector are important. That low levels of education and lack of market access lead to higher PHL (other things constant), suggests that policy interventions outside the agricultural sector are needed. Improving access to markets and encouraging farmers (or rather their children) to continue to secondary schooling will reduce food waste in the long run.
Data and insights from nationally representative household surveys can be used, directly, to inform PHL information systems (such as APHLIS); to update their annual estimates; and, indirectly, to help fine-tune underlying algorithms.
11. Droughts dominate Africa’s risk environment
More than 60 percent of households report sudden losses in income and assets.
Weather shocks are very common, but price risk is just as prevalent. Death and illness were also frequently reported.
Health and weather shocks are often repeatedly experienced by the same household. Price risk is by far the most commonly reported covariate shock, much more so than weather shocks.
Risk is higher in rural areas, particularly risks to income. Rural households are more susceptible to income shocks because agriculture is a risky business.
Female-headed households are less susceptible to agricultural price risk, but more susceptible to food price risk.
The coping mechanisms:
Many households have no means to cope with shocks.
Savings are the most widely used coping mechanism, but have a more limited role for poor and rural households, which, as a result, rely more on their assets.
Working more (sometimes involving migration) is a common coping strategy in rural areas.
Government assistance is limited. Social assistance is most often informal and is the most prevalent coping mechanism among households headed by women.
Reducing the risk associated with agricultural livelihoods is an important part of reducing volatility for households in Africa. This can be done by increasing access to irrigation and drought-tolerant crops and by improving the integration of domestic crop markets.
Strengthening financial markets to provide financial products as buffers in periods of distress should be part of the development strategy, especially for rural areas.
Improving and strengthening national social protection systems as well as formalizing social transfers would also help the most vulnerable in smoothing the impact of risk.
Everyday life in Sub-Saharan Africa carries considerable risk, which often is linked to extreme weather, such as drought. But households also face price shocks—increases in food prices or input prices, or falls in output prices. Illness or death in the household is also frequently reported by rich and poor households alike. And Africa is changing. Climatic conditions are changing, and so too are markets, asset holdings, and livelihoods. In dealing with shocks, households commonly rely on informal transfers, reductions in household expenditures, and even asset sales. These mechanisms can be ineffective and costly. This study explores whether drought is indeed still the dominant risk faced by households, and how households cope with shocks today.
The study draws on the World Bank’s Living Standards Measurement Study–Integrated Surveys on Agriculture, which have been fielded in six Sub-Saharan African countries: Uganda, Ethiopia, Nigeria, Niger, Malawi, and Tanzania. These are standard household surveys that include modules on the shocks experienced, negative consequences of the shocks (loss of assets, income, food production, and food stocks), as well as the coping mechanisms that households adopt in the wake of an income shock. Most of the surveys are available for one year (one wave or round) only. For some countries (Uganda and Nigeria), the study was able to utilize pooled data across years.
The Risks Households Face:
Sudden losses in income and assets were reported by the majority of the households surveyed.
Weather shocks are very common, but price risk is just as damaging.
Death and illness were also frequently reported.
Other shocks occur, but less often.
Multiple shocks are reported more often than single shocks.
Health and weather shocks are often repeatedly experienced by the same household.
Price shocks are more likely to hit all households in a community at once. Weather shocks tend to do so as well, but not to the same extent.
Shocks are more frequently reported in rural areas, linked to their greater dependence on agriculture.
Rural households are more susceptible to income shocks, because agriculture is a risky business.
Business- and employment-related shocks are more prevalent among urban households.
Theft is as often a feature of the rural landscape as the urban landscape.
Wealth reduces and changes the nature of income risk.
How Households Cope with the Shocks They Experience:
Many households do not manage to cope with shocks.
Savings are the most widely used coping mechanism, but have a more limited role for poor and rural households.
Working more or longer (sometimes with migration) is a common coping strategy for poor households in rural areas.
Social assistance is most often informal, with very limited government assistance reported across the continent.
Informal assistance is the most prevalent coping mechanism among households headed by women.
Government assistance is poorly targeted to poor households.
Reducing the risks associated with agricultural income and helping households transition into less risky livelihoods are essential for establishing more stable income for households in Africa.
Reducing risk in agriculture requires addressing market risk in addition to climate risk and crop disease
Strengthening the financial markets in many Sub-Saharan African settings could go a long way, by improving the use of financial products as buffers in periods of economic distress. This is especially important for poor households and in rural areas, where relying on the sales of assets represents the main coping mechanism, Improving and strengthening the national social protection systems as well as formalizing social transfers could also help the most vulnerable in smoothing the impact of risks.
More could be done to improve data collection on risks and coping strategies for further policy research—such as adopting uniform recall periods and categories of shocks and coping mechanisms.
12. African farmers are increasingly diversifying their incomes
Agriculture remains the mainstay of Africa’s rural livelihoods, particularly where agro-ecological conditions are favorable
Up to 98% of rural households in the nine African countries studied engage in on-farm agriculture (including livestock) compared with an average of 85% in the 13 non-African countries studied
Only between 1% and 26% of rural households engage in agricultural wage labor, which typically contributes less than 5% of rural income
For their level of development, rural households in Africa appear no less diversified, with diversification more in household enterprises than in wage employment
Rural African households derive about two-thirds of their income from on-farm agriculture—a level consistent with the level of gross domestic product (GDP) in the African countries
Income from nonfarm wage employment averages 8% in African countries, compared with 21% elsewhere
In favorable agro-ecological conditions, farming remains the occupation of choice but when urban integration is low, households engage more fully in nonfarm activities in Malawi and Niger, but less so in some other countries
Rural household income diversification seems in line with Africa’s level of development. In all countries, African and non-African, wealthier households show greater reliance on nonfarm sources of income.
The literature from the past 20 years suggests that rural household income diversification is the norm, with some off-farm diversification common at all levels of welfare. Does this hold true in Africa, a latecomer to the process of structural transformation? This paper compares income diversification among rural households in Africa with that in other regions and seeks to understand how geography drives income diversification, particularly agro-ecological potential and distance to urban areas.
The analysis uses comparable disaggregated income data from 41 national household surveys from 22 countries from all developing regions spanning 1991–2012. It complements the surveys compiled in the Rural Income Generating Activities (RIGA) database of the Food and Agriculture Organization of the United Nations with six Living Standard Measurement Study–Integrated Surveys on Agriculture (LSMS-ISA). The nine African countries included in the study, Ethiopia, Ghana, Kenya, Madagascar, Malawi, Niger, Nigeria, Tanzania, Uganda, represent 51% of the Sub-Saharan African population in 2012. Comparable protocols were followed to construct the occupational classifications and income aggregates as described on the RIGA website. The geo-referencing of the households in the LSMS-ISA surveys is exploited to analyze the role of geography in income diversification.
Most countries outside Africa—generally with higher gross domestic product (GDP) levels—have a larger share of households with diversified portfolios (45% versus 29%), and they derive a larger share of income from off-farm activities (67% versus 37%).
Although African households are more likely to specialize in farming than are households in other regions (52% versus 21%), once level of GDP is controlled for, shares of income and participation in nonagricultural activities are similar, suggesting Africa’s structural transformation is on track.
Africa shows less diversification into wage employment, both in agriculture (5% of income versus 13%) and outside agriculture (8% versus 21%).
As elsewhere, wealthier rural households tend to participate more in, and derive more income from, nonfarm activities. They also participate more in nonagricultural wage employment. Agricultural wage employment is disproportionately at the low end of the income distribution.
In agro-ecologically well-endowed areas, the share of agricultural income is higher, irrespective of the distance to urban centers. Elsewhere, and in more remote areas, patterns in both diversification in Malawi and Niger, and agricultural specialization in Tanzania and Uganda, are observed.
Policy and Research Implications
The latest evidence does not support that Africa’s income diversification is lagging, though rural wage employment remains limited and agriculture is the mainstay of rural livelihoods.
The sheer number of people engaged in agriculture and the large share of income derived from it suggests that a balanced rural development strategy is needed with continued attention to productivity growth in both the farm and nonfarm sectors. Even a rapidly growing nonfarm employment sector cannot absorb the population currently engaged in farming.
As countries develop, their policy emphasis on farm, nonfarm self-employment, and nonfarm wage employment evolve such that the latter sectors become more important. Geographic considerations (agro-ecological potential and integration with urban centers) will inevitably influence the policy perspective.
13. Household enterprises operate mainly in survival mode
Non-farm Household Enterprises in Rural Africa: New Empirical Evidence
Non-farm enterprises in rural Africa are often operated due to economic necessity and survival. Consequently they tend to have low-productivity levels, do not create many jobs, and do not drive structural transformation in Africa.
SCORE: 3 – Factish
Forty-two percent of rural households operate a nonfarm enterprise, contributing between 8% (Malawi) and 36 percent (Niger) of average household income
Most of these enterprises are informal, operate only seasonally, and create few jobs
Many perform poorly, but a few perform very well. Enterprises operated by younger people, women, and those further away from urban centers are less productive, as are those operated in response to a shock (drought, flood, illness).
Households are pushed into operating a nonfarm enterprise when they have difficulty coping with shocks or dealing with agricultural seasonality, or when household members need employment. Much heterogeneity exists across countries in the extent and frequency of these determinants, and many enterprises are not operational throughout the year (see figure).
Positive business opportunities—particularly for households living closer to denser markets—often lead rural households to operate enterprises.
The better educated and those who can obtain credit are more likely to start businesses, suggesting that access to human and physical capital matters.
Lack of profitability or financing, as well as idiosyncratic shocks such as illness or death, can cause rural enterprises to cease operations.
The study generally confirms the common perception that Africa’s rural household enterprises operate largely in survival mode, although a small portion of them are highly productive.
The paper suggests policies that improve the business environment, that assist households in rural areas to manage and cope with risk, and that strengthen the capabilities of individuals to be entrepreneurial. It also recommends improvements in data collection on rural enterprises.
African rural nonfarm household enterprises are commonly perceived as operating largely in survival mode, but little is known about the truth of this perception and the economic roles of such enterprises. Such knowledge is needed because informal household enterprises in rural Africa may have to absorb large parts of the estimated 65 million new labor market entrants by 2020. This paper describes these enterprises, investigates household motives to operate them, analyzes their productivity, and assesses their continuity over time.
Drawing on the Living Standards Measurement Study–Integrated Surveys on Agriculture, this study examines the birth, life, and death of rural household nonfarm enterprises in Ethiopia, Niger, Nigeria, Malawi, Tanzania, and Uganda.
Several factors are associated with households starting a nonfarm enterprise as well as with the type of business operated:
When households are large and agricultural activities season bound; when they must cope with shocks such as drought, floods, and illness; and when they lack social protection and insurance schemes, households can be pushed into operating a nonfarm enterprise. However, positive business opportunities—especially for rural households that live close to a market—also encourage households to form such enterprises.
Wealthier households and households headed by older, more educated men tend to be more engaged in nonfarm enterprises.
Credit and education are closely associated with agribusiness and trade, as well as operating a bar or restaurant, which tend to be capital intensive.
Households that have experienced a shock tend to operate businesses that are easier to enter, such as agribusiness and trade.
Distance to a population center is less important for professional services or bars and restaurants that serve local clients.
A link exists between motivation to operate a nonfarm enterprise and the subsequent productivity of that enterprise:
Enterprises operated by necessity seem less productive than those operated because of an opportunity arising from market proximity. The latter tend to attain better capacity use by operating throughout the year, seek credit more often, and have better-educated owners.
Rural nonfarm enterprises seem on average less productive than urban enterprises, perhaps because they face greater risk and susceptibility to market failure.
Nonfarm enterprises in regions with a history of violent conflict are less productive.
Female-owned enterprises are less productive, although their productivity may be underestimated because of constraints on women’s time and imprecise productivity measures.
Enterprises owned by young adults seem significantly less productive. Given Africa’s youthful population and the millions of young people entering rural labor markets annually, additional support for such enterprises may be needed.
Survival and Exit
Although starting an informal enterprise in rural Africa is relatively uncomplicated, raising its productivity so that it can grow and survive is challenging:
Rural enterprises are more likely than urban enterprises to cease operations because of idiosyncratic shocks such as illness or death.
Many rural household enterprises operate for only part of the year, thus reflecting coping or seasonality (see figure).
Rural enterprises are more likely to operate intermittently than are their urban counterparts.
Market involvement is commonplace among farm households; it accounts for 90 percent of production in Malawi.
Contrary to common perceptions, the bulk of market participation is driven by the sale of food crops.
In most cases, market participation only involves the sale of small quantities of own food production.
Although female farmers appear to participate less in market activities, when they do participate, they tend to sell larger shares of the production under their control relative to their male counterparts.
No clear trends emerge when the degree of agricultural commercialization is correlated with children’s anthropometrics as measured through Z-scores, or other measures of nutritional status.
While agricultural commercialization may not pose a threat to nutrition, it is, on average, also unlikely to suffice on its own to improve nutritional outcomes. Complementary measures will be needed.
The progressive move toward a market-oriented system of production in agriculture is also expected to initiate a virtuous cycle that, by raising income levels, improves consumption, food security, and nutritional outcomes in rural households. Nonetheless, this process requires that households choose to commercialize their production and use the returns from crop sales in ways that foster improved nutrition. Thus, although the commercialization of crops may potentially increase incomes, and thereby improve nutrition, farming households often avoid commercializing their crops. This finding is often attributed to the fact that households are not indifferent between production for the market and production for the homestead
This study covers two survey panel waves in three countries included in the Living Standards Measurement Study–Integrated Surveys on Agriculture (LSMS–ISA) program, namely Malawi, Tanzania, and Uganda. The surveys were fielded throughout the year. Given the focus, the study sample only includes agricultural households, defined as households that reported involvement in agricultural activities through ownership and/or cultivation of land in the most recently completed agricultural season. After excluding nonfarming households and households that were only observed once, the final sample that was used for the panel analysis consisted of 2,222 households in Malawi, 1,744 in Tanzania, and 1,587 in Uganda. The degree of commercialization was proxied by the share of total crop production value marketed, the Household Crop Commercialization Index (CCI). To proxy nutritional outcomes, three measures were used: a) child anthropometric measures; b) food expenditure per capita; c) total expenditure per capita.
Contrary to conventional wisdom, the data reveal that (at least some) market involvement is widespread, even among the poorest and smallest landholders, with rates of market participation as high as 90 percent in Malawi.
Similarly, contrary to common perception, a considerable portion of this market presence is driven by the sale of staple and other food crops, and not necessarily by traditional cash crops.
Although female farmers appear to participate less in market activities, when they do, they tend to sell larger shares of the production under their control compared with their male counterparts.
There is little evidence of a relationship between increased commercialization and improved nutritional status.
While agricultural commercialization may not pose a threat to nutrition, it is, on average, also unlikely to suffice on its own to improve nutritional outcomes.