Farm size is a key characteristic of agricultural systems, closely linked to farming and land management practices. However, data on farm size are often difficult to obtain, especially in smallholder-dominated regions, and where available, they are rarely spatially explicit. This study examines the use of field size as a proxy for farm size, aiming to identify field size thresholds that can distinguish farms of different sizes. Using household-level data from Zambia's Crop Forecast Survey, we applied both continuous and categorical methods to quantify the field-to-farm size relationship. Non-parametric Theil-Sen regression revealed a generally linear relationship between log-transformed field and farm sizes, particularly at the survey enumeration area scale, where farm size is proportional to field size (slope approximate to 1) and field size alone predicts farm size with a median bias of 0.017 ha. A Gaussian Naive Bayes classifier was also developed to assign fields to three categories: very small (A: <2 ha), small (B: 2-5 ha), and medium (C: 5-20 ha), and a coarser two-class scheme (S: <5 ha; M: 5-20 ha) that has been applied in previous studies. The Gaussian model identified field size thresholds at 0.58-0.6 ha (A-B) and 1.18-1.69 ha (B-C), depending on the prior farm size distribution. For the S-M scheme, thresholds ranged from 0.96-1.4 ha. Using these thresholds, national classification achieved F1 scores of 0.78, 0.59, and 0.77 for A, B, and C, respectively, with B more prone to misclassification. The two-class model achieved 0.87 and 0.88 for S and M, indicating more robust classification performance under a coarser scheme. Kernel density analysis shows that field-size distributions within farm categories and the separability of these distributions, vary across regions, with the underlying factors responsible for these differences remaining an open question for future research. These results demonstrate the potential of field size as a spatial proxy for farm size to support remote sensing based farming system classification in data-scarce regions.

Agricultural and forested landscapes in Africa are changing rapidly in response to socio-economic and environmental pressures. Integrated landscape approaches provide an opportunity for a more holistic and coordinated resource management strategy through the engagement of multiple stakeholders. Despite their influence as landscape actors, participation of private businesses in such initiatives has thus far been limited. This study focuses on the Kalomo District in southern Zambia, which provides an example of a rural landscape characterized by high levels of poverty, low agricultural productivity, and widespread deforestation and forest degradation. The study applied a value-chain analysis approach to better understand how the production of four locally important commodities (maize, tobacco, cattle, and charcoal) impacts land use, local livelihoods, and environmental objectives in this landscape, focusing on the role and influence of private sector actors. Data were collected through focus group discussions and key informant semi-structured interviews. Qualitative content analysis was employed to analyze the data and contextualize the findings. Results indicate three key potential entry points for increased private sector engagement: (1) improving water security for smallholders; (2) empowering small and medium-sized enterprises (SMEs) as private sector actors; and (3) collective planning for sustainable landscape activities with deliberate measures to involve private sector actors. We discuss options for optimizing benefits from the identified entry points.

Existing studies on calf management practice adoption tend to treat practices individually and, by implication, ignore the possibility that some practices are more likely than others to be jointly adopted. This study applies market basket analysis to examine bundling of calf management practices based on the likelihood of joint adoption using producer survey data. Results indicate that the base practices of horn management, deworming, and castration are the three most widely adopted practices and are more likely to be jointly adopted in varying combinations with other practices. We discuss implications for extension programming and future studies concerned with understanding practice adoption decisions.

With only 10 years before the 2030 Sustainable Development Goal of Zero Hunger, the global community is in a race to address mounting food and nutrition insecurity among the over 820 million people in the World. This is more profound for developing countries like Zambia where food and nutrition insecurity remain huge development challenges. Output market participation by smallholder rural households has been touted as a possible pathway to improving dietary diversity in addition to production diversity. This study applied structural equation modeling (SEM) techniques to a nationally representative, two-wave household-level panel data of over 6000 rural smallholder farmers in Zambia to assess the extent to which participation in output markets affects dietary diversity and how this effect changed between 2015 and 2019. We measure dietary diversity using a household dietary diversity score (HDDS) and market participation using household commercialization index. We find that market participation enhances household dietary diversity, with the effect being stronger in 2019 than in 2015. Besides reinforcing the importance of market access and participation in improving household nutritional outcomes, our finding demonstrate the increasing role of markets in the nutrition of rural households. Another key result is that household production diversity improves dietary diversity. Taken together, these findings imply that nutrition sensitive agricultural interventions should concomitantly help improve market access and participation for smallholder farmers, and promote a broader food group diversification at the farm level.

There is an urgent need to increase agricultural production in order to meet increasing food demands driven in part by population growth and changing dietary preferences. Doing so by expanding area cultivated into forests has important environmental consequences, including engendering climate change. Climate-smart agriculture (CSA) is considered an important option to increase agricultural productivity and resilience, intensify agricultural production, and possibly reduce cropland expansion. This paper uses nationally representative survey data to assess the extent, intensity and drivers of cropland expansion, and applies an instrumental variable approach to determine the extent to which CSA reduced cropland expansion in Zambia. We find that one-fifth of the 7241 farm households surveyed in 2019 expanded cropland between the 2016/2017 and 2017/2018 farming seasons, clearing on average 0.18 ha, but only 13% expanded their cropland into intact forests, clearing an average of 0.09 ha of forestland per household per year. In aggregate, cropland expansion by smallholder into forests represents about 60% of the estimated 250,000 ha of forests lost per year in Zambia. Most households expanded cropland because they needed to meet subsistence food requirements and a few others in response to market opportunities. We did not find statistically significant associations between adopting CSA and cropland expansion in our national sample. Thus, given the low extent and intensity of CSA adoption as defined in this paper, relying only on CSA as a means to spare forests may be risky. These findings have important implications on CSA practice definition, promotion, framing and adoption.

While it is generally accepted that climate change will exacerbate poverty for small and medium sized farmers in Sub-Saharan Africa (SSA) over the coming years, at least due to rising variability and rainfall shocks (Mulenga, Wineman, and Sitko 2017; Hallegatte et al. 2016), a number of questions remain unanswered. Which types of the poor are more exposed to climate risk and how do the impacts of climate and weather shocks vary across stochastically and structurally poor households? Addressing these questions is crucial for improved targeting of interventions intended to build the resilience of smallholder farmers. Smallholder farmers’ reliance almost entirely on rain-fed agriculture and their limited capacity to cope with weather shocks exposes them to climate risks. Weather shocks negatively impact smallholders through their effects on agricultural productivity, which is the mainstay of rural smallholder households. If left unchecked, weather shocks can lead to increased poverty incidence and intensity. In this paper, we utilize data from a nationally representative two-wave panel of recent agricultural household surveys to conduct a high resolution analysis of the spatial distribution of poverty, and how the different types of poverty are impacted by exposure to climate change variability. The data allows us to (a) control for observed and unobserved sources of household heterogeneity, and (b) distinguish between the structurally poor, i.e., those households that have very little assets or savings, and the stochastically poor, i.e., those households that have low savings but enough assets that they could liquidate if necessary to smooth consumption during a climate shock Out of the 14,508 rural households interviewed in Zambia in 2012 and 2015, about 51% were structurally poor (low income and assets) and 5% were stochastically poor (low income and high assets). About 23% of households that were structurally poor in 2012 remained structurally poor in 2015, hence, chronically poor. A third of the structurally not poor in 2012 fell into poverty in 2015, while about 19% of poor households in 2012 managed to escape poverty in 2015. Structurally poor households in Zambia are more exposed to drought risk. Lower than normal rainfall, as measured by a negative precipitation index, significantly increases the probability of being structurally poor by 2.3 percentage points. Three implications follow from our findings. First, there is a need for well-structured and targeted social promotion programs to lift the viable but chronically and structurally poor and stochastically poor households from poverty. This can be achieved within the agricultural sector by using the electronic voucher delivery systems to better target large-scale, anti-poverty programs such as the farmer input support program. Along with improved targeting, the use of the electronic based voucher systems crowds-in private sector investments, which make available diverse inputs for farmers and also help develop the rural nonfarm sector where farmers can earn extra incomes. Smart-subsidies should be flanked by output market linkages and/or market development in order to enhance market participation and help improve incomes from agricultural production. Second, for those not commercially viable, there is a need for a better targeted and sustained social welfare program specifically meant for this group. Thus there is need for sustained social protection (e.g., social cash transfers) in order to prevent the non-poor from falling into poverty. And lastly, the intricate linkages among climate variability, climate risk, and poverty call for more support to enable farmers not only adapt to, but also mitigate climate change and variability. Such support may be v directed towards climate-smart agriculture adoption, autonomous and planned adaptation, improved extension, and climate information services.

This study uses a nationally representative dataset of urban households in Zambia to examine household cooking fuel choice patterns and to quantify the effect of access to electricity on household charcoal consumption. We find charcoal to be the most prevalent cooking fuel, for both households with and without electricity access. Proportionately more charcoal users reside in low income residential areas. Using a two-stage econometric estimation procedure that accounts for endogeneity of access to electricity, we find that on average, households with access to electricity consume 54% less charcoal than their counterparts without access. Further, our results indicate that charcoal consumption tends to increase with income, but this increase attenuates as income increases further. Other socio-demographic variables such as age, education and household size are also important in influencing charcoal consumption. We discuss implications for interventions aimed at promoting cleaner energy sources and efficient charcoal use for cooking among urban residents.

Wheat streak mosaic (WSM), caused by Wheat streak mosaic virus , is the most widespread and economically important virus disease affecting winter wheat ( Triticum aestivum L.) in the Great Plains of the United States. Using reflectance data from a hand-held hyperspectral radiometer and yields from a field experiment, this study estimated economic thresholds of WSM beyond which it is uneconomical to continue with mid-season input applications. Disease severity assessments based on reflectance measurements were taken from 99 plots across the field at three time points, namely 27 April, 4 May, and 10 May in the 2015–2016 wheat season. A log-linear regression model of yield on reflectance indicates potential yield losses of up to 35% for every unit increase in infection severity (as measured by reflectance readings). Using regression and partial budget analysis, results show varying thresholds of WSM depending on the date of disease severity assessments. The 27 April assessment had the lowest threshold when compared with the other assessment dates. Threshold analysis indicates potential to save resources by discontinuing mid-season input applications and introducing cattle for grazing, in about 14% of the sampled plots, but only 1% if grazing was not possible.

Wheat streak mosaic virus is among the most economically important viruses affecting winter wheat in the Great Plains region. Depending on infection severity, the virus can lead to significant yield loss, rendering continuation of mid-season input application uneconomical. Determining an economic threshold infection severity soon enough in the season so that farmers could discontinue input application, could increase farmer net returns and save resources. Using data from a field experiment involving 114 sample plots, which were sensed for the presence of the virus using reflectance readings, we conducted econometric and partial budget analysis to estimate the effect of the virus on yields, and determine the economic threshold level of infection. Results indicate varying threshold infection severity depending on the date of sensing, with earlier sensing having a higher threshold than sensing at a later date. Further, estimates show that the virus can reduce yields by as much as 35 percent for every unit increase in reflectance readings, between growth stages Feekes 5 and 6. Without a better predictor of yield losses, however, it is rarely going to be the case that it would pay to discontinue input application.

The study uses a nationally representative dataset of smallholder farmers in Zambia to determine the effect of agricultural productivity on households’ participation in charcoal production. An instrumental variable probit approach is applied to account for the endogeneity of agricultural productivity in household's charcoal participation decision. We find a negative and significant effect of agricultural productivity on household's likelihood of participation in charcoal production. Results also show that higher education, income, asset value, and participation in off-farm employment opportunities reduce the likelihood of participation in charcoal production. Therefore, interventions seeking to reduce charcoal production in rural Zambia could benefit from improving smallholder agricultural productivity, incomes, asset base, and off-farm employment creation. However, interventions need not lose sight of other important macro-level factors.

A number of studies use meteorological records to analyze climate trends and assess the impact of climate change on agricultural yields. While these provide quantitative evidence on climate trends and the likely effects thereof, they incorporate limited qualitative analysis of farmers’ perceptions of climate change and/or variability. The present study builds on the quantitative methods used elsewhere to analyze climate trends, and in addition compares local narratives of climate change with evidence found in meteorological records in Zambia. Farmers offer remarkably consistent reports of a rainy season that is growing shorter and less predictable. For some climate parameters—notably, rising average temperature—there is a clear overlap between farmers’ observations and patterns found in the meteorological records. However, the data do not support the perception that the rainy season used to begin earlier, and we generally do not detect a reported increase in the frequency of dry spells. Several explanations for these discrepancies are offered. Further, we provide policy recommendations to help farmers adapt to climate change/variability, as well as suggestions to shape future climate change policies, programs, and research in developing countries.

Minimum tillage has been promoted for about two decades as a way to conserve soils and to increase agricultural productivity in Zambia and sub-Saharan Africa. However, the extent of its uptake by smallholder farmers remains debatable. This paper assesses factors influencing the uptake and uptake intensity of minimum tillage, using large household survey data for the period 2010 to 2014 in Zambia. We apply double-hurdle models to account for corner solution outcomes resulting from the limited uptake of minimum tillage. Less than 5% and 10% of smallholders used minimum tillage per year as the main tillage method at the national level and in the top 10 districts with the highest use rates respectively. Low seasonal rainfall and being in districts where minimum tillage has been promoted for over 10 years increase the likelihood of minimum tillage uptake and uptake intensity, but not for all its components. These results have implications for targeting future programmes aimed at promoting minimum tillage.

The past decade has ushered in an era of increasingly contentious land politics in Zambia, with investors, the government, and chiefs simultaneously blamed for injustices in land allocation. These conflicts over land have been exacerbated, and at times caused by the lack of transparency and available data on the status of land. While a variety of actors has real grievances with the security and efficiency of the current system of land allocation, smallholder farmers bear the brunt of the risk of continuing the status quo in land policy.

Zambia is one of the most forested countries in Africa, with about 50 million out of the 75 million hectares total land area under some form of forest cover. However, the country also has one of the highest rates of deforestation and degradation in the world, estimated at 250,000-300,000 hectares of forest loss per annum. Reversing/slowing this high deforestation and degradation trend will require the country to design and implement programs and strategies that will effectively deal with both the proximate and underlying drivers of deforestation and degradation.

Zambia is one of the most forested countries in Africa, with about 50 million out of the 75 million hectares total land area under some form of forest cover. However, the country also has one of the highest rates of deforestation and degradation in the world, estimated at 250,000-300,000 hectares of forest loss per annum. Reversing/slowing this high deforestation and degradation trend will require the country to design and implement programs and strategies that will effectively deal with both the proximate and underlying drivers of deforestation and degradation.

ACKNOWLEDGMENTS The Indaba Agricultural Policy Research Institute is a non-profit company limited by guarantee and collaboratively works with public and private stakeholders. IAPRI exists to carry out agricultural policy research and outreach, serving the agricultural sector in Zambia so as to contribute to sustainable pro-poor agricultural development. We wish to acknowledge the financial and substantive support of the Swedish International Development Agency and the United States Agency for International Development (USAID) in Lusaka. We further would like to acknowledge the technical and capacity building support from Michigan State University and its researchers and the editorial assistance of Patricia Johannes. Any views expressed or remaining errors are solely the responsibility of the authors. Comments and questions should be directed to: Zambia, like many countries in Sub-Saharan Africa, has witnessed a recent expansion in the number of indigenously-owned farms greater than 20 hectares in size. The growth of the medium-scale farms is reshaping the agricultural landscape in Zambia and the prospects for achieving an inclusive, smallholder growth strategy. This paper examines the role played by medium-scale land acquisitions in shaping the Zambia's potential to achieve inclusive, agricultural-led growth. In particular, this article analyzes the spatial and socioeconomic implications of medium-scale land acquisitions on land access, land ownership inequality, and land use in Zambia. By exploring these underappreciated dimensions of Zambia's ongoing land rush, we hope to provide insights into the role of domestic land policies and land investors in shaping the future of Zambia's food system. Data and Methods We draw from nationally representative household survey data of small-and medium-scale farmers in Zambia. In particular, we draw on various Crop Forecast Surveys (CFS), the 2001 Supplemental Survey, and the 2012 Rural Agricultural Livelihoods Survey (RALS). To fill these gaps we rely on the Supplemental Survey 2001 and the RALS 2012. However, these surveys are only provincially representative, and therefore, cannot be used to assess changes at the district level. In addition, we draw on a survey of medium-scale land holders conducted in 2011 by Sitko and Jayne (2014). The data presented here are, therefore, not nationally representative, but they do provide indicative findings on the emergent farming sector in districts where emergent farmers are concentrated.  Between 2001 and 2014 the number of farms between 10 and 20 hectares grew at a rate of 79%.  Farms of 10 to 100 hectares now control 34% of the land in the small-and medium-scale …

This study examined the use of various sources of cooking energy among urban households in Zambia, and analyzed urban households’ energy choice and charcoal consumption decisions using econometric models. Overall, charcoal is the most common source of main cooking energy in urban areas, followed by electricity, and lastly the other non-specific sources, such as gas, and kerosene. Of the three main sources considered in this study (i.e., electricity only, charcoal only, and a mix of charcoal and electricity) charcoal accounted for almost half (44%) of urban households, followed by a mix of charcoal and electricity representing 38%, and lastly electricity only accounting for 17% of urban households.

Conservation farming (CF) practices are widely considered to be important components of sustainable agricultural development in Sub-Saharan Africa because of their potential to raise farm productivity and incomes while maintaining or improving soil quality and reducing vulnerability to variable climatic conditions. CF in Zambia can be traced to the 1980s when government, private sector, and donor communities started promoting CF as an alternative set of agronomic practices for Zambian smallholders (Haggblade and Tembo 2003).