Smallholder Agricultural Systems Research Articles

Assessment of programme level costs and benefits of implementing nature-based solutions for climate change mitigation and adaptation in smallholder agricultural systems.

Abstract Background: Climate change poses a serious threat to the world's 475 million smallholder farms and to the ability to feed the world's growing population. Climate-driven natural hazards, such as droughts, floods, pests, invasive plants and diseases, are increasingly impacting yields and damaging the natural resource base upon which farmers' livelihoods depend, while also threatening the return and impact capacity of private and public agricultural investments (IPCC, 2021). Apart from being extremely vulnerable to climate change, agriculture is one of the largest contributors to global greenhouse gas emissions, accounting for around a quarter of all emissions annually (IPCC, 2019). Nature-based solutions (NBS) are "actions to protect, conserve, restore, sustainably use and manage natural or modified terrestrial, freshwater, coastal and marine ecosystems which address social, economic and environmental challenges effectively and adaptively, while simultaneously providing human well-being, ecosystem services, resilience and biodiversity benefits" (UNEA/EA.5/Res.5). NBS can therefore be considered as a systemic solution that offers multiple benefits, including sustainable agriculture, mitigating and adapting to climate change and reducing biodiversity loss. To meet the challenges of decarbonizing the agricultural sector, enabling it to adapt to climate change, and to function in a nature-positive way, it is critical to look at ways for making NBS accessible, affordable and scalable, particularly for smallholder farmers. As a first step, this research aims to understand the extent to which the costs and benefits of supporting NBS in the agri-food sector have been evaluated and how effective nature-based solutions have been in delivering environmental, social and economic benefits for climate change adaptation and mitigation.

Assimilation of Earth Observation Data for Crop Yield Estimation in Smallholder Agricultural Systems

Assimilation of Earth Observation Data for Crop Yield Estimation in Smallholder Agricultural Systems

MASSAI: Multi-agent system for simulating sustainable agricultural intensification of smallholder farms in Africa

The research and development needed to achieve sustainability of African smallholder agricultural and natural systems has led to a wide array of theoretical frameworks for conceptualising socioecological processes and functions. However, there are few analytical tools for spatio-temporal empirical approaches to implement use cases, which is a prerequisite to understand the performance of smallholder farms in the real world. This study builds a multi-agent system (MAS) to operationalise the Sustainable Agricultural Intensification (SAI) theoretical framework (MASSAI). This is an essential tool for spatio-temporal simulation of farm productivity to evaluate sustainability trends into the future at fine scale of a managed plot. MASSAI evaluates dynamic nutrient transfer using smallholder nutrient monitoring functions which have been calibrated with parameters from Malawi and the region. It integrates two modules: the Environmental (EM) and Behavioural (BM) ones.•The EM assess dynamic natural nutrient inputs (sedimentation and atmospheric deposition) and outputs (leaching, erosion and gaseous loses) as a product of bioclimatic factors and land use activities.•An integrated BM assess the impact of farmer decisions which influence farm-level inputs (fertilizer, manure, biological N fixation) and outputs (crop yields and associated grain).•A use case of input subsidies, common in Africa, markedly influence fertilizer access and the impact of different policy scenarios on decision-making, crop productivity, and nutrient balance are simulated. This is of use for empirical analysis smallholder's sustainability trajectories given the pro-poor development policy support.

What future for organic farming? Foresight for a smallholder Mediterranean agricultural system

This study aims to foresee the future of organic farming in the smallholder agricultural systems of the Valencian Region (Spain), as well as to identify how different drivers of change may affect such a future. To do so, two qualitative methodologies were combined: The Delphi method and the participatory scenario development. The results estimate an upward trend for organic farming area and sales, which would contribute to a greener and more sustainable economy in the region, a slight drop in organic versus conventional food prices, the entrance of large operators and a “dualisation” of the models of production and consumption of organic food. The key role that the public sector plays for the future of organic farming is underlined by the stakeholders, who suggest that the civil society may counterbalance the lack of public support by way of collective action and an increasing awareness about health and sustainability. This study concludes by highlighting the main findings obtained, both regarding the expected evolution of organic agriculture in the region and the key factors that would influence such evolution, emphasising the applicability of the results to other similar smallholder Mediterranean farming systems.

Soil Health and Sustainability in the Age of Organic Amendments: A Review

Organic amendments have emerged as a pivotal component in the trajectory of sustainable agriculture, given their multifaceted contributions to soil health, crop yield, and environmental conservation. This comprehensive review delves into the intricacies of organic amendments, spanning their historical context, types, sources, nutrient profiles, and their interplay with soil and plant health. Special emphasis is laid on the integration of modern technological advancements with traditional amendment practices, exploring the synergistic potential of digital agriculture and precision farming in enhancing the efficacy of organic inputs. The review also sheds light on the economic, social, and environmental ramifications, emphasizing the role of organic amendments in smallholder versus large-scale agricultural systems and their influence on farmer resilience and consumer perceptions. Crucially, this review addresses the challenges and limitations inherent in organic amendment practices, including concerns related to quality variation, scalability, over-application, and regulatory nuances. Concomitantly, the work culminates with a forward-looking perspective, highlighting emergent trends and innovations that portend the future of organic amendments in global agriculture. The findings underscore the significance of organic amendments not merely as soil additives but as integral elements in the blueprint for a sustainable, resilient, and food-secure future.

CH4 and N2 O emissions from smallholder agricultural systems on tropical peatlands in Southeast Asia.

There are limited data for greenhouse gas (GHG) emissions from smallholder agricultural systems in tropical peatlands, with data for non-CO2 emissions from human-influenced tropical peatlands particularly scarce. The aim of this study was to quantify soil CH4 and N2 O fluxes from smallholder agricultural systems on tropical peatlands in Southeast Asia and assess their environmental controls. The study was carried out in four regions in Malaysia and Indonesia. CH4 and N2 O fluxes and environmental parameters were measured in cropland, oil palm plantation, tree plantation and forest. Annual CH4 emissions (in kg CH4 ha-1 year-1 ) were: 70.7 ± 29.5, 2.1 ± 1.2, 2.1 ± 0.6 and 6.2 ± 1.9 at the forest, tree plantation, oil palm and cropland land-use classes, respectively. Annual N2 O emissions (in kg N2 O ha-1 year-1 ) were: 6.5 ± 2.8, 3.2 ± 1.2, 21.9 ± 11.4 and 33.6 ± 7.3 in the same order as above, respectively. Annual CH4 emissions were strongly determined by water table depth (WTD) and increased exponentially when annual WTD was above -25 cm. In contrast, annual N2 O emissions were strongly correlated with mean total dissolved nitrogen (TDN) in soil water, following a sigmoidal relationship, up to an apparent threshold of 10 mg N L-1 beyond which TDN seemingly ceased to be limiting for N2 O production. The new emissions data for CH4 and N2 O presented here should help to develop more robust country level 'emission factors' for the quantification of national GHG inventory reporting. The impact of TDN on N2 O emissions suggests that soil nutrient status strongly impacts emissions, and therefore, policies which reduce N-fertilisation inputs might contribute to emissions mitigation from agricultural peat landscapes. However, the most important policy intervention for reducing emissions is one that reduces the conversion of peat swamp forest to agriculture on peatlands in the first place.

Agroecology Can Promote Climate Change Adaptation Outcomes Without Compromising Yield In Smallholder Systems

A critical question is whether agroecology can promote climate change mitigation and adaptation outcomes without compromising food security. We assessed the outcomes of smallholder agricultural systems and practices in low- and middle-income countries (LMICs) against 35 mitigation, adaptation, and yield indicators by reviewing 50 articles with 77 cases of agroecological treatments relative to a baseline of conventional practices. Crop yields were higher for 63% of cases reporting yields. Crop diversity, income diversity, net income, reduced income variability, nutrient regulation, and reduced pest infestation, indicators of adaptative capacity, were associated with 70% or more of cases. Limited information on climate change mitigation, such as greenhouse gas emissions and carbon sequestration impacts, was available. Overall, the evidence indicates that use of organic nutrient sources, diversifying systems with legumes and integrated pest management lead to climate change adaptation in multiple contexts. Landscape mosaics, biological control (e.g., enhancement of beneficial organisms) and field sanitation measures do not yet have sufficient evidence based on this review. Widespread adoption of agroecological practices and system transformations shows promise to contribute to climate change services and food security in LMICs. Gaps in adaptation and mitigation strategies and areas for policy and research interventions are finally discussed.

Sustainable Farming in Practice: Building Resilient and Profitable Smallholder Agricultural Systems in Sub-Saharan Africa

Sub-Saharan Africa is at crossroads. With a rapidly growing population of 1.2 billion and changing climates, the continent faces major development challenges, including food insecurity, climate change, resource degradation, poverty, gender inequality, and social exclusion. While there are multiple competing narratives promoted in the high-input, industrialised world to address climate change and the resilience of agricultural systems (e.g., regenerative agriculture, agroecology), there is an ongoing debate and genuine questions about the appropriateness of these approaches to small-scale farmers in SSA. African agricultural systems are unique, characterised by low productivity, nutrient mining, land degradation, hoe culture, and fragmented and diversified small farms. Though environmental pollution and over-dependence on fossil fuel-powered mechanisation are rarely topping the priority list, climate change is becoming a major concern. The top-down narratives from environment-concerned communities lack the tools to address the most pressing and immediate challenge of local communities in Sub-Saharan Africa, namely (i) intensification by increased crop productivity per unit of inputs, (ii) increased access to rural energy forcing farmers to use available biomass for cooking instead of soil regeneration; (iii) the intent of no use or reduced mineral fertilisers, in a system marred by nutrient mining over centuries; and (iv) failure to address recurrent drought through integrated soil water management interventions. To address these specific challenges, we present context-specific, outcome-oriented farming solutions as a viable and appropriate strategy called ‘sustainable farming’. We argue that the nature-based narratives will remain to be important but will be better adopted if they respond to local demands and context-specific challenges of small-scale farmers. By means of three successful land restoration programmes in East Africa, we present eight outcomes that should be addressed to ensure sustainable farming of small holdings and reduce the risk of climate change. For these innovations to be adopted at scale, we proposed to put in place incentive mechanisms and functional last-mile delivery systems.

Strengthening agroecological resilience through commons-based seed governance in the Philippines.

The Filipino agricultural sector is exposed to multiple climatic, economic, and social risks that will likely intensify in the near future. Building agroecological resilience has been proposed to protect small-scale farmers' livelihoods and improve food security in the context of (unexpected) shocks and disruptions, and slow system changes such as climate change. This paper argues that commons-based seed production, based on collective management and ownership of seeds and varieties, can play a central role in building resilience capacities in smallholder communities. I explore this by applying an indicator-based framework to assess the contribution of the Filipino farmer network Magsasaka at Siyentipiko para sa Pag-unlad ng Agrikultura (MASIPAG) to agroecological resilience. I find that the networks' commons-based seed governance builds agroecological resilience in various ways. By equipping small-scale farmers with the tools to regain control over seed production and breeding, they become stewards of an actively evolving collection of varieties. The in situ maintenance and development of traditional, open-pollinated varieties and a network of diversified trial and backup farms build up buffering capacities and foster agrobiodiversity and local adaptation. A focus on regionally available natural resources reduces vulnerabilities to external factors. Adaptive capacities are strengthened through a high degree of flexibility and responsiveness achieved by self-organization and polycentric organizational structures. Broad participation, shared learning and spaces for experimentation support the development of farmers' capacities to respond to diverse challenges. Commons-based approaches to seed governance can thus strengthen agroecological resilience and long-term food security in smallholder agricultural systems.

A Forward Future-Based Approach to Optimizing Agriculture and Climate Change Adaptation in Lower Eastern Kenya

Kenya’s vulnerability to climate variability and change has been compounded by dependence on rain-fed agriculture with constrained capacity to adapt, a rapidly growing population, low-mechanized and low-input smallholder agricultural systems, and compromised soil fertility. The Ukraine war, COVID-19 and the desert locust invasion have only amplified the prevailing sensitivity to shocks in the agriculture sector, creating an emphasis on the need to strengthen local agricultural production to reduce reliance on imports. This paper seeks to assess the opportunities for improving agriculture adaptation and resilience based on future expected changes in climate, length of the growing period and agro-ecologies. The study uses 2020 as the baseline year and explores changes in agro-ecological zones (AEZs) in “near future” 2040 through two representative concentration pathways, 4.5 and 8.5, representing a medium carbon emissions and a dire emissions future, respectively. Google Earth Engine and R Statistics are used in data-processing. Down-scaled climate projections from CIMP5 are used for future analyses combined with static soil suitability and drainage data. Fuzzy logic is used to normalize inputs and compute the agro-ecological zones (AEZ). Interesting results emerge from the study that validate the hypothesis that the seasons and production potential are shifting. Lowland drylands will experience an increasingly long growing period, creating the potential for diversifying production systems from rangelands to agro-pastoral systems, with the capacity to grow more drought-resistant crops and the potential to take advantage of increased runoff for water harvesting. Midland highland areas, which form part of the food basket areas, have already started experiencing a reduction in the length of the growing period and agricultural potential. In these areas, resilience mechanisms will need to consider the expected future reduction in rain-fed agricultural potential, gendered preferences, convergence of technology and indigenous coping mechanisms, and drought-resilience-focused diversification.

Micro-Investment by Tanzanian Smallholders’ in Drip Irrigation Kits for Vegetable Production to Improve Livelihoods: Lessons Learned and a Way Forward

Food security in sub-Saharan Africa is one of the major issues confronting the continent. Innovative use of fresh water, the world’s scarcest food production resource, is vital for family-run small-holder agricultural systems, which supply up to 80% of the world’s food. Agriculture employs 70% of Tanzania’s rural population and supplies 95% of the country’s food. The goal was to measure the impact of smart drip irrigation kits on smallholder farmers’ resilience and rural poverty in Tanzania. A household survey was conducted using an exploratory sequential design in four districts (Babati, Hai, Kasulu, and Kilosa) in Tanzania. A total of 383 respondents (Micro-investing (MI) farmers, n = 195; control, n = 187) were randomly selected from a pool of 3444 farmers. Partial budgeting and enterprise economic analysis were used for the calculation of gross margins, and multivariate analysis was used for poverty analysis. Gross margin analysis showed that communities using drip-irrigated vegetable farming are more profitable. Partial budgeting analysis showed that micro-irrigation increased the revenue generation for most vegetable varieties. However, multivariate analysis was unable to confirm that household poverty was markedly reduced through the adoption of this technology. Half of the MI farmers could afford an education for their children due to the extra income generated from MI. This investment strategy has the potential to improve smallholder livelihoods and resilience to climate change.

Estimating Groundnut Yield in Smallholder Agriculture Systems Using PlanetScope Data

Crop yield is related to household food security and community resilience, especially in smallholder agricultural systems. As such, it is crucial to accurately estimate within-season yield in order to provide critical information for farm management and decision making. Therefore, the primary objective of this paper is to assess the most appropriate method, indices, and growth stage for predicting the groundnut yield in smallholder agricultural systems in northern Malawi. We have estimated the yield of groundnut in two smallholder farms using the observed yield and vegetation indices (VIs), which were derived from multitemporal PlanetScope satellite data. Simple linear, multiple linear (MLR), and random forest (RF) regressions were applied for the prediction. The leave-one-out cross-validation method was used to validate the models. The results showed that (i) of the modelling approaches, the RF model using the five most important variables (RF5) was the best approach for predicting the groundnut yield, with a coefficient of determination (R2) of 0.96 and a root mean square error (RMSE) of 0.29 kg/ha, followed by the MLR model (R2 = 0.84, RMSE = 0.84 kg/ha); in addition, (ii) the best within-season stage to accurately predict groundnut yield is during the R5/beginning seed stage. The RF5 model was used to estimate the yield for four different farms. The estimated yields were compared with the total reported yields from the farms. The results revealed that the RF5 model generally accurately estimated the groundnut yields, with the margins of error ranging between 0.85% and 11%. The errors are within the post-harvest loss margins in Malawi. The results indicate that the observed yield and VIs, which were derived from open-source remote sensing data, can be applied to estimate yield in order to facilitate farming and food security planning.

Estimating crop type and yield of small holder fields in Burkina Faso using multi-day Sentinel-2

Remote Sensing affords the opportunity to monitor and evaluate data scarce regions where field collection efforts are costly. A particular challenge is monitoring and evaluation in regions with smallholder agricultural systems (∼1 ha) that are often subsistence focused, vulnerable to food insecurity and data scarce. Using multi-day moderate resolution Sentinel-2 and Random Forest models, this study shows that crop type and rice yields in Burkina Faso can be predicted with greater than ∼80% accuracy in the rainy season. Model optimization using varying spectral and vegetation index inputs can increase crop type and yield prediction accuracy in the dry season where denser cultivation is a challenge for the 10–20 m resolution of Sentinel-2. However, there is a trade-off between opting for very high-resolution imagery (<2 m) or the number of bands offered by Sentinel-2 as the bands that occupy and vegetation indices that utilize the red through NIR ranges were most important across all models. In addition, model type, linear Regression or nonlinear Random Forest, matters little when estimating yield in these landscapes, unless Harmonic regression is utilized for the linear model. This study also showed that a model trained with high quality 2019 dry season crop cut data can predict the subsequent dry season's interannual crop type with overall accuracy as high as 60%, comparable to crop type models trained with 2020 survey data and used to estimate crop type in the concurrent season, as the survey collection. This indicates some utility in leveraging the calibrated Random Forest models to make skillful predictions of interannual crop type and ultimately food availability of nearby communities for years with no training data. Given increasing global food prices and restricted commodity trade, understanding local agricultural productivity using affordable and timely remote sensing-based methods is essential for ensuring appropriate humanitarian interventions.

Phenotypic diversity and biochemical characteristics of selected rhizobia nodulating the common bean (Phaseolus vulgaris L.)

Increasing interest in using rhizobia as biofertilizers in smallholder agricultural farming systems has prompted scientists to investigate rhizobia diversity, resulting in the identification of many strains. Fifty-five Rhizobium strains nodulating in the common bean (Phaseolus vulgaris L.) were isolated from soil samples from different areas of Tunisia and phenotypically characterized to determine their symbiotic nitrogen fixation capabilities. Their tolerance to pH, salinity, temperature and alkalinity, as well as their cultural and biochemical characteristics indicated wide physiological diversity. These phenotypic characteristics significantly affected rhizobia growth, and strains of interest were identified and used in inoculation trials. They were efficient and able to tolerate pH from 4 to 9, NaCl concentrations of 25 to 100 mM, temperature variation from 10 to 40 °C, and lime (CaCO3) from 0.05 to 0.20 mM. Selected Rhizobium strains were identified as candidates for biofertilizer production for a variety of Tunisian soil types.

Using Sentinel-2 to Track Field-Level Tillage Practices at Regional Scales in Smallholder Systems

Zero tillage is an important pathway to sustainable intensification and low-emission agriculture. However, quantifying the extent of zero tillage adoption at the field scale has been challenging, especially in smallholder systems where field sizes are small and there is limited ground data on zero tillage adoption. Remote sensing offers the ability to map tillage practices at large spatio-temporal scales, yet to date no studies have used satellite data to map zero tillage adoption in smallholder agricultural systems. In this study, we use Sentinel-2 satellite data, random forest classifiers, and Google Earth Engine to map tillage practices across India’s main grain producing region, the Indo-Gangetic Plains. We find that tillage practices can be classified with moderate accuracy (an overall accuracy of 75%), particularly in regions with relatively large field sizes and homogenous crop management practices. We find that models that use satellite data from only the first half of the growing season perform as well as models that use data throughout the growing season, allowing for the creation of within-season tillage maps. Finally, we find that our model can generalize well through time in the western IGP, with reductions in accuracy of only 5–10%. Our results highlight the ability of Sentinel-2 satellite data to map tillage practices at scale, even in smallholder systems where field sizes are small and cropping practices are heterogeneous.

Crop-livestock integration provides opportunities to mitigate environmental trade-offs in transitioning smallholder agricultural systems of the Greater Mekong Subregion

CONTEXTThe Greater Mekong Subregion has been undergoing rapid agricultural transformation over the last decades, as traditional diverse subsistence-oriented agriculture is evolving towards intensified commercial production systems. Negative environmental impacts often include deforestation, nutrient pollution, and greenhouse gas (GHG) emissions. OBJECTIVEThis study aims to explore the potential of crop-livestock integration to mitigate trade-offs between economic and environmental impacts of smallholder farming systems at different stages of agricultural transition and degrees of agricultural diversity across the Greater Mekong Subregion. METHODSWe chose a ‘middle ground’ between detailed modeling of few, representative farming systems and modeling of large household populations. 24 low and high diversity farms were selected in Laos (Xieng Khouang province), Cambodia (Ratanakiri province) and Vietnam (Central Highlands) from a survey dataset of 1300 households. These farming systems were simulated with the whole-farm bio-economic and multi-objective optimization model FarmDESIGN, calculating operating profit, GHG emissions and nitrogen (N) balance. Two optimizations (‘business as usual’ vs. ‘crop-livestock integration’) were performed, generating ‘solution spaces’ or alternative configurations aiming to maximize profitability, keep farm N balanced and minimize GHG emissions. RESULTS AND CONCLUSIONSAgricultural systems across the sites differed in their production orientation and management practices, representing various stages of agricultural transition. Nitrogen balances varied between sites, being negative in Ratanakiri (average − 20.5 kg N ha−1 y−1) and Xieng Khouang (−36.5 kg N ha−1 y−1) and positive in the Central Highlands (73 kg N ha−1 y−1). Negative balances point to unsustainable mining of nutrients due to sale of cash crops without sufficient inputs, and positive balances to the risk of environmental contamination. Total GHG emissions ranged from 0.52–8.12 t CO2e ha−1 and were not significantly impacted by stage of agricultural transformation or agricultural diversity. GHG sources in Ratanakiri and Xieng Khouang were determined by crop residue burning while in Central Highlands fertilizer and livestock were main emitters. High diversity farms obtained higher operating profits (10,379 USD y−1) than low diversity farms (4584 USD y−1). Crop-livestock integration, a combination of measures including introduction of improved forages grasses, manure recycling and residue feeding, and reduction of residue burning, resulted in larger ‘solution spaces’, thus providing farmers with more options to mitigate agro-environmental trade-offs. SIGNIFICANCEThese findings underline the potential of crop-livestock integration to support sustainable intensification pathways in the Greater Mekong region. Public and private investment in further research and extension is needed to develop and scale context-specific crop-livestock integration practices.

Data-driven decentralized breeding increases prediction accuracy in a challenging crop production environment

Crop breeding must embrace the broad diversity of smallholder agricultural systems to ensure food security to the hundreds of millions of people living in challenging production environments. This need can be addressed by combining genomics, farmers’ knowledge, and environmental analysis into a data-driven decentralized approach (3D-breeding). We tested this idea as a proof-of-concept by comparing a durum wheat (Triticum durum Desf.) decentralized trial distributed as incomplete blocks in 1,165 farmer-managed fields across the Ethiopian highlands with a benchmark representing genomic prediction applied to conventional breeding. We found that 3D-breeding could double the prediction accuracy of the benchmark. 3D-breeding could identify genotypes with enhanced local adaptation providing superior productive performance across seasons. We propose this decentralized approach to leverage the diversity in farmer fields and complement conventional plant breeding to enhance local adaptation in challenging crop production environments.

Investigating the impact of classification features and classifiers on crop mapping performance in heterogeneous agricultural landscapes

Timely and accurate mapping crops is essential for agricultural management, policy making and food security. The smallholder agricultural systems lead to large number of fragmented and heterogeneous landscape, making fine crop mapping a huge challenge. Feature and classifier selection are two important influencing factors in crop classification. However, there are few systematic tests to determine the specific features and classifiers needed for heterogeneous agricultural landscapes. In this study, 24 candidate spectral features, 8 spatial texture features from the red-edge (RE) and near-infrared (NIR) bands, and 4 supervised classifiers (i.e. random forest (RF), support vector machine (SVM), artificial neural network (ANN), and maximum likelihood classifier (MLC)) were used for crop mapping. 60 spatially heterogeneous landscapes in Heilongjiang Province, northeastern China were selected as the study areas, evaluated by compositional heterogeneity (homogeneity index, HOI) and configurational heterogeneity (splitting index, SPLIT). The results summarized a look-up table for searching the optimum classification features and classifiers in different landscape, providing a reference for future crop classifications and preventing the consumption of computing time. The results revealed that (1) an optimal feature-subset (with reduced data volume by 65%) can achieve high-accuracy crop mapping in heterogeneous regions. (2) The optimum type and number of features and classifiers are landscape sensitive. When a specific accuracy was required, homogenous regions need a smaller number of features and a simple MLC could meet the requirement. (3) The impact of configurational heterogeneity on textural features is more significant, while compositional heterogeneity performs better on spectral Vegetation Indices (VIs). Findings from this study provide a general guideline for crop mapping in plains or fragmented landscape and areas with single or complex planting structure.

Dynamic vulnerability of smallholder agricultural systems in the face of climate change for Ethiopia

Assessing vulnerability to climate change and extremes is the first step towards guiding climate change adaptation. It provides the basis to decide ‘what’ adaptation measures are needed ‘where’. Vulnerability which is defined as a function of exposure, sensitivity, and adaptive capacity, differs spatially and evolves temporally. Therefore, it is imperative to understand the dynamics of vulnerability at sub-national scales to be prepared for and respond to current and future climatic risks. This paper focuses on Ethiopia where a sub-national understanding of vulnerability dynamics in smallholder agriculture systems is missing to date. The paper assesses the vulnerability of crop-based smallholder systems in Ethiopia for the past (1996–2005), current (2006–2015), and two future (2036–2045 and 2066–2075) climate scenarios using an indicator-based approach. The future scenarios are based on two Representative Concentration Pathways (RCPs) RCP 2.6 and RCP 6.0 from four general circulation models. Results show the emergence of highly vulnerable zones that were missing in the past scenario. With Paris agreement pathway, keeping global warming under 2 °C (RCP 2.6), reduction in vulnerability of 10% of the zones is noted in far future (2066–75) as compared to RCP 6.0 where the exposure increases, making 30% of the zones highly vulnerable. The projected increase in exposure to climatic hazards will worsen the vulnerability of smallholder agricultural systems in future unless the current adaptation deficit is sufficiently addressed. This study maps the temporal dynamics of vulnerability unlike the prevailing snapshot assessments at subnational-level for Ethiopia. The study seeks to assist the decision-making process to build resilience to climate change in Ethiopia and other low-income countries with similar geophysical and socio-economic conditions.

Risk factors associated with Ctenocephalides felis flea infestation of peri-urban goats: a neglected parasite in an under-appreciated host

Goats are critical in mixed smallholder agricultural systems in lower and middle-income countries, while fleas are important human and animal health concerns around the world. Convenience sampling was used to describe and consider risk factors for flea infestations of peri-urban goats, with the aim of informing the iterative development of animal husbandry and management based control strategies. Seven hundred and ninety-two goats were examined in 228 households across 10 peri-urban communities surrounding Blantyre in southern Malawi. The prevalence of Ctenocephalides felis fleas was 18.3, 37.1 and 100% at the levels of individual goats, households and communities, respectively, highlighting a neglected human and animal health concern. Constant introduction of new livestock coupled to a lack of biosecurity within communities, the ubiquitous presence of dog and cat hosts for C. felis, the frequency and thoroughness of cleaning overnight goat accommodation, and goat age less than 12 months old were identified as risk factors for flea infestation. This focal cross-sectional study highlights the significance of fleas in peri-urban communities and uncovers trends and commonalities that are needed to inform sustainable disease management. The majority of the peri-urban goat keepers were female, had resided in the same community throughout their whole life and had primary level education. Advice on the planned management of fleas in livestock needs to be tailored towards this demographic group. This approach affords an opportunity to promote public health measures to address household flea infestations and zoonotic disease spread.