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The aim of this study was to assess the impacts of climate smart agricultural practices on household income and food security in Doyogena and Basona climate smart landscapes in Ethiopia. Data were collected from 796 randomly selected smallholder farmers from the two climate-smart villages of the Doyogena (399) and Basona (397) districts. Using a propensity score matching approach, this study examines the contribution of CSA practices to farm household income and food security. Our study finds that the adoption of CSA practices enhances household food security in Doyogena, whereas in Basona, the implementation of CSA practices improved the average annual income of households. This study suggests that the introduction of CSA practices and scaling up of these practices should be site-specific, but not a one-size-fits-all approach. Promoting and scaling up these practices may require assessing the needs and priorities of communities in different locations. [ABSTRACT FROM AUTHOR]

SUMMARY: Household surveys are one of the most commonly used tools for generating insight into rural communities. Despite their prevalence, few studies comprehensively evaluate the quality of data derived from farm household surveys. We critically evaluated a series of standard reported values and indicators that are captured in multiple farm household surveys, and then quantified their credibility, consistency and, thus, their reliability. Surprisingly, even variables which might be considered 'easy to estimate' had instances of non-credible observations. In addition, measurements of maize yields and land owned were found to be less reliable than other stationary variables. This lack of reliability has implications for monitoring food security status, poverty status and the land productivity of households. Despite this rather bleak picture, our analysis also shows that if the same farm households are followed over time, the sample sizes needed to detect substantial changes are in the order of hundreds of surveys, and not in the thousands. Our research highlights the value of targeted and systematised household surveys and the importance of ongoing efforts to improve data quality. Improvements must be based on the foundations of robust survey design, transparency of experimental design and effective training. The quality and usability of such data can be further enhanced by improving coordination between agencies, incorporating mixed modes of data collection and continuing systematic validation programmes. [ABSTRACT FROM AUTHOR]

Agricultural development must integrate multiple objectives at the same time, including food security, income, and environmental sustainability. To help achieve these objectives, development practitioners need to prioritize concrete livelihood practices to promote to rural households. But trade-offs between objectives can lead to dilemmas in selecting practices. In addition, heterogeneity among farming households requires targeting different strategies to different types of households. Existing diversity of household resources and activities, however, may also bear solutions. We explored a new, empirical research method that identifies promising options for multi-objective development by focusing on existing cases of strong multi-dimensional household performance. The “Positive Deviance” approach signifies identifying locally viable livelihood practices from diverse households that achieve stronger performance than comparable households in the same area. These practices are promising for other local households in comparable resource contexts. The approach has been used in other domains, such as child nutrition, but has not yet been fully implemented for agricultural development with a focus on the simultaneous achievement of multiple objectives. To test our adapted version of the Positive Deviance approach, we used a quantitative survey of over 500 rural households in South-Eastern Tanzania. We identified 54 households with outstanding relative performance regarding five key development dimensions (food security, income, nutrition, environmental sustainability, and social equity). We found that, compared to other households with similar resource levels, these “positive deviants” performed strongest for food security, but only slightly better for social equity. We then re-visited a diverse sub-sample for qualitative interviews, and identified 14 uncommon, “deviant” practices that plausibly contributed to the households’ superior outcomes. We illustrate how these practices can inform specific recommendations of practices for other local households in comparable resource contexts. The study demonstrates how, with the Positive Deviance approach, empirical observations of individual, outstanding households can inform discussions about locally viable agricultural development solutions in diverse household context. [ABSTRACT FROM AUTHOR]

To target food security interventions for smallholder households, decision makers need large-scale information, such as maps on poverty, food security and key livelihood activities. Such information is often based on expert knowledge or aggregated data, despite the fact that food security and poverty are driven largely by processes at the household level. At present, it is unclear if and how household level information can contribute to the spatial prediction of such welfare indicators or to what extent local variability is ignored by current mapping efforts. A combination of geo-referenced household level information with spatially continuous information is an underused approach to quantify local and large-scale variation, while it can provide a direct estimate of the variability of welfare indicators at the most relevant scale. We applied a stepwise regression kriging procedure to translate point information to spatially explicit patterns and create country-wide predictions with associated uncertainty estimates for indicators on food availability and related livelihood activities using household survey data from Uganda. With few exceptions, predictions of the indicators were weak, highlighting the difficulty in capturing variability at larger scale. Household explanatory variables identified little additional variation compared to environmental explanatory variables alone. Spatial predictability was strongest for indicators whose distribution was determined by environmental gradients. In contrast, indicators of crops that were more ubiquitously present across agroecological zones showed large local variation, which often overruled large-scale patterns. Our procedure adds to existing approaches that often only show large-scale patterns by revealing that local variation in welfare is large. Interventions that aim to target the poor must recognise that diversity in livelihood activities for income generation within any given area often overrides the variability of livelihood activities between distant regions in the country. [ABSTRACT FROM AUTHOR]

Abstract: Farmers in Africa have long adapted to climatic and other risks by diversifying their farming activities. Using a multi‐scale approach, we explore the relationship between farming diversity and food security and the diversification potential of African agriculture and its limits on the household and continental scale. On the household scale, we use agricultural surveys from more than 28,000 households located in 18 African countries. In a next step, we use the relationship between rainfall, rainfall variability, and farming diversity to determine the available diversification options for farmers on the continental scale. On the household scale, we show that households with greater farming diversity are more successful in meeting their consumption needs, but only up to a certain level of diversity per ha cropland and more often if food can be purchased from off‐farm income or income from farm sales. More diverse farming systems can contribute to household food security; however, the relationship is influenced by other factors, for example, the market orientation of a household, livestock ownership, nonagricultural employment opportunities, and available land resources. On the continental scale, the greatest opportunities for diversification of food crops, cash crops, and livestock are located in areas with 500–1,000 mm annual rainfall and 17%–22% rainfall variability. Forty‐three percent of the African cropland lacks these opportunities at present which may hamper the ability of agricultural systems to respond to climate change. While sustainable intensification practices that increase yields have received most attention to date, our study suggests that a shift in the research and policy paradigm toward agricultural diversification options may be necessary. [ABSTRACT FROM AUTHOR]

Tropical land use is one of the leading causes of global environmental change. Sustainable agricultural development aims to reduce the negative environmental impacts of tropical land use whilst enhancing the well-being of the smallholder farmers residing in those areas. Interventions with this goal are typically designed by scientists educated in the Western tradition, and often achieve lower than desired uptake by smallholder farmers. We build on work done in farm type classification and studies of factors that influence adaptation, trialling a suite of household survey questions to elucidate the motivational factors that influence a farmer's willingness to adapt to external change. Based on a sample of 1015 households in the rubber growing region of Xishuangbanna, South-west China, we found that farm types based on structural characteristics (e.g. crops, livelihoods) could not be used to accurately predict farmers' motivations to adapt. Amongst all six farm types identified, the full range of motivational typologies was found. We found six motivational types, from most to least likely to adapt, named: Aspirational Innovators, Conscientious, Copy Cats, Incentive-centric, Well Settled, and Change Resistant. These groups roughly corresponded with those identified in literature regarding diffusion of innovations, but such classifications are rarely used in development literature. We predict that only one third of the population would be potentially willing to trial a new intervention, and recommend that those sectors of the population should be identified and preferentially targeted by development programs. Such an approach requires validation that these motivational typologies accurately predict real behaviour – perhaps through a panel survey approach. Dedicated data gathering is required, beyond what is usually carried out for ex-ante farm typologies, but with some refinements of the methodology presented here the process need not be onerous. An improved suite of questions to appraise farmers' motivations might include value orientations, life satisfaction, and responses to various scenarios, all phrased to be locally appropriate, with a scoring system that uses the full range of potential scores and a minimum of follow up and peripheral questions. [ABSTRACT FROM AUTHOR]

Accountability and adaptive management of recent global agreements such as the Sustainable Development Goals and Paris Climate Agreement, will in part rely on the ability to track progress toward the social and environmental targets they set. Current metrics and monitoring systems, however, are not yet up to the task. We argue that there is an imperative to consider principles of coherence (what to measure), standardization (how to measure) and decision-relevance (why to measure) when designing monitoring schemes if they are to be practical and useful. New approaches that have the potential to match the necessary scale of monitoring, with sufficient accuracy and at reasonable cost, are emerging; although, they represent a significant departure from the historical norm in some cases. Iterative review and adaptation of analytical approaches and available technology will certainly be needed to continuously design ways to best track our progress. [ABSTRACT FROM AUTHOR]

Oil palm, currently the world’s main vegetable oil crop, is characterised by a large productivity and a long life span (≥25 years). Peak oil yields of 12 t ha −1 yr −1 have been achieved in small plantations, and maximum theoretical yields as calculated with simulation models are 18.5 t oil ha −1 yr −1 , yet average productivity worldwide has stagnated around 3 t oil ha −1 yr −1 . Considering the threat of expansion into valuable rainforests, it is important that the factors underlying these existing yield gaps are understood and, where feasible, addressed. In this review, we present an overview of the available data on yield-determining, yield-limiting, and yield-reducing factors in oil palm; the effects of these factors on yield, as measured in case studies or calculated using computer models; and the underlying plant-physiological mechanisms. We distinguish four production levels: the potential, water-limited, nutrient-limited, and the actual yield. The potential yield over a plantation lifetime is determined by incoming photosynthetically active radiation (PAR), temperature, atmospheric CO 2 concentration and planting material, assuming optimum plantation establishment, planting density (120–150 palms per hectares), canopy management (30–60 leaves depending on palm age), pollination, and harvesting. Water-limited yields in environments with water deficits >400 mm year −1 can be less than one-third of the potential yield, depending on additional factors such as temperature, wind speed, soil texture, and soil depth. Nutrient-limited yields of less than 50% of the potential yield have been recorded when nitrogen or potassium were not applied. Actual yields are influenced by yield-reducing factors such as unsuitable ground vegetation, pests, and diseases, and may be close to zero in case of severe infestations. Smallholders face particular constraints such as the use of counterfeit seed and insufficient fertiliser application. Closing yield gaps in existing plantations could increase global production by 15–20 Mt oil yr −1 , which would limit the drive for further area expansion at a global scale. To increase yields in existing and future plantations in a sustainable way, all production factors mentioned need to be understood and addressed. [ABSTRACT FROM AUTHOR]

Agricultural production in the Sudano–Sahelian zone of west Africa is highly vulnerable to the impacts of climate variability and climate change. The present study aimed to understand farmers’ perceptions of climate variability and change and to evaluate adaptation options together with farmers, including tactical management of planting date in combination with the use of mineral fertilizer. Farmers perceived an increase in annual rainfall variability, an increase in the occurrence of dry spells during the rainy season, and an increase in temperature. Overall, this is in line with the observed meteorological data. Drought tolerant, short maturing crop varieties and appropriate planting dates were the commonly preferred adaptation strategies to deal with climate variability. On-farm trials confirmed that planting delays significantly reduce crop yields. The use of mineral fertilizer is often promoted, but risky for smallholders: although larger fertilizer applications increased the yield of maize (Zea mays) and millet (Pennisetum glaucum) significantly, a gross margin analysis indicated that it did not lead to more profit for all farmers. We conclude that integrating management of nutrients and planting time with improved farmer access to timely weather information, especially on the onset of the rains, is critical to enhancing adaptive capacity to increased climate variability and change. [ABSTRACT FROM PUBLISHER]

Highlights: [•] Variety by planting date interact strongly for millet and sorghum but not for maize and cotton. [•] Late planting resulted in large loss of yield for maize, sorghum and cotton, but not for millet. [•] Short duration variety of millet is better adapted for late planting. [•] If rains start late, sorghum can be sown from 1st June to early July without major yield loss. [•] Cotton yield at early planting is larger than yield at medium and late planting. [ABSTRACT FROM AUTHOR]

Colombia has a total of 27.2 million heads of cattle, ranking fourth among the Latin American countries. Identifying sustainable strategies to mitigate greenhouse gas emissions (GHGE) will help the Colombian government meet their goal of a 51% reduction in national emissions by 2030. Estimation of yield gaps for identifying the potential to improve cattle farms productivity and efficiency in Colombia help on reducing the GHGE intensities from the cattle sector. This paper aims to calculate the gap between attainable and actual milk and meat yields for specialized dairy, dual-purpose, cow-calf, and fattening production systems in 3 agro-ecological zones (AEZ) in Colombia; to identify the main aspects that restrict the meat and milk yields in these production systems; and analyze how closing yield gaps affect the carbon footprint (CF) of meat and milk production. The most suitable AEZs for cattle activities were identified by considering environmental, climatic, edaphic, and land characteristics. From a dataset of 1505 surveyed farms, a yield gap benchmarking analysis for estimating the potential to increase meat and milk yields in each of the identified AEZ was applied. The most productive farms were included in the "best farms" while the rest of the farms belonged to the "farms operating below potential". A "cradle to farm-gate" Life Cycle Assessment was used to calculate the CF. Three scenarios were proposed for closing the yield gaps by 50, 75, and 100%, between the two groups of farms. Three AEZs likely to support cattle activities in Colombia were identified. Average milk production from the farms operating below potential was 45–50% of potential production, and meat was 34–51%, indicating that a potential to achieve increases in milk and meat productivity exists. CFs of 1 kg milk or meat were lower in the groups of best-performing farms than in the groups of farms operating below potential. Yield gaps for milk and meat production can be closed by improving cattle management practices and better technologies. As a general trend, closing the yield gaps decreases the CFs. Our findings contribute to understand the farms' current productive performance and provides key insights into the possible technological and managerial changes for improving the productivity of cattle systems in Colombia. In addition, the study showed how milk and meat CFs can be lowered with the adoption of proper cattle management practices, and better technologies. [Display omitted] • Cattle are responsible for 21% of Colombian GHG emissions, identifying sustainable strategies for its mitigation is crucial. • Yield gap analysis is useful for exploring opportunities to increase yields by identifying factors constraining production. • This study calculated the yield gaps for Colombian cattle systems, their carbon footprints, and proposed mitigation options. • Substantial cattle yield gaps were found, hence there is a considerable scope to improve milk and meat yields in Colombia. • Closing the yield gaps by increasing cattle productivity is an important mitigation strategy of climate change in Colombia. [ABSTRACT FROM AUTHOR]

Arctic vegetation is characterized by high spatial variability in plant functional type ( PFT) composition and gross primary productivity ( P). Despite this variability, the two main drivers of P in sub- Arctic tundra are leaf area index ( LT) and total foliar nitrogen ( NT). LT and NT have been shown to be tightly coupled across PFTs in sub-Arctic tundra vegetation, which simplifies up-scaling by allowing quantification of the main drivers of P from remotely sensed LT. Our objective was to test the LT- NT relationship across multiple Arctic latitudes and to assess LT as a predictor of P for the pan- Arctic. Including PFT-specific parameters in models of LT- NT coupling provided only incremental improvements in model fit, but significant improvements were gained from including site-specific parameters. The degree of curvature in the LT- NT relationship, controlled by a fitted canopy nitrogen extinction co-efficient, was negatively related to average levels of diffuse radiation at a site. This is consistent with theoretical predictions of more uniform vertical canopy N distributions under diffuse light conditions. Higher latitude sites had higher average leaf N content by mass ( NM), and we show for the first time that LT- NT coupling is achieved across latitudes via canopy-scale trade-offs between NM and leaf mass per unit leaf area ( LM). Site-specific parameters provided small but significant improvements in models of P based on LT and moss cover. Our results suggest that differences in LT- NT coupling between sites could be used to improve pan-Arctic models of P and we provide unique evidence that prevailing radiation conditions can significantly affect N allocation over regional scales. [ABSTRACT FROM AUTHOR]

Abstract: African smallholder farming systems are complex, dynamic systems with many interacting biophysical subcomponents. In these systems the major inputs and outputs are managed by human agency – the farmers. To analyse potential developmental pathways of smallholder farming systems in sub-Saharan Africa (SSA), we recognised the need for a tool that can capture the effects and consequences of decision-making on the use of resources. Here we describe and apply such a new modelling tool, developed within the NUANCES framework (Nutrient Use in ANimal and Cropping systems: Efficiencies and Scales), called NUANCES-FARMSIM (FARM SIMulator), an integrated crop – livestock model developed to analyse African smallholder farm systems. NUANCES-FARMSIM was used to analyse a representative case study farm in the highlands of Western Kenya, a site for which each of the components of FARMSIM has been thoroughly tested. We present the results of a sensitivity analysis which showed the model to be sufficiently robust to identify key management options that explain most of the variability in farm productivity, and the long-term consequences of these options for the case study farm. The analyses showed clearly that the most important decisions are those related to the interactions between the different components of the farm and therefore justify the need of integrating crop and livestock components within one modelling tool. The allocation of limited resources across the farm, and the way organic matter is recycled or redistributed within the farm determines the long-term production capacity of the system. The results of the sensitivity analyses further showed that for the case study farm in Western Kenya a strong focus on improving the reliability of the subsystem level or process descriptions will only result in minor improvement in simulating productivity at farm level. [Copyright &y& Elsevier]

The article focuses on measuring leaf area index (LAI) in low vegetation of Arctic ecosystem using optical instruments. LAI is a powerful diagnostic of plant productivity. Despite the fact that many methods have been developed to quantify LAI, both directly and indirectly, leaf area index remains difficult to quantify accurately, owing to large spatial and temporal variability. The gap-fraction technique is widely used to estimate the LAI indirectly. However, for low-stature vegetation, the gap-fraction sensor either cannot get totally underneath the plant canopy, thereby missing part of the leaf area present, or is too close to the individual leaves of the canopy, which leads to a large distortion of the LAI estimate. The LAI of arctic vegetation could be estimated accurately and rapidly by combining field measurements of canopy reflectance and light penetration through the canopy. By combining the two methodologies, the limitations of each could be circumvented, and a significantly increased accuracy of the LAI estimates was obtained.