The concept of climate-smart agriculture (CSA) is gaining momentum across the globe. However, it is not specific on what should be covered under its three pillars—productivity, resilience and mitigation. Consequently, CSA encompasses many different agricultural practices/technologies, making it difficult to prioritise CSA objectives. Firstly, there is a lack of clear and workable criteria as well as methods for assessing the climate-smartness of interventions. Secondly, little information exists about the impact of the various interventions already promoted as CSA, especially in the developing world. Finally, CSA prioritisation does not take into account stakeholders’ perspectives to ensure that the interventions are applicable, suitable and of high adoption-potential. Here, we describe a new participatory protocol for assessing the climate-smartness of agricultural interventions in smallholder practices. This identifies farm-level indicators (and indices) for the food security and adaptation pillars of CSA. It also supports the participatory scoring of indicators, enabling baseline and future assessments of climate-smartness to be made. The protocol was tested among 72 farmers implementing a variety of CSA interventions in the climate-smart village of Lushoto, Tanzania. Farmers especially valued interventions that improved soil fertility and structure, reduced surface runoff, and reclaimed degraded land due to the positive impacts on yield and off-season crop agriculture. Mostly, the CSA interventions increased animal production, food production, consumption and income. The protocol is easy to adapt to different regions and farming systems and allows for the better prioritisation of interventions. But we recommend that CSA is adopted as part of a monitoring, evaluation and learning process.
Tag: agricultural systems
Methods for environment: productivity trade-off analysis in agricultural systems
Trade-off analysis has become an increasingly important approach for evaluating system level outcomes of agricultural production and for prioritising and targeting management interventions in multi-functional agricultural landscapes. We review the strengths and weakness of different techniques available for performing trade-off analysis. These techniques, including mathematical programming and participatory approaches, have developed substantially in recent years aided by mathematical advancement, increased computing power, and emerging insights into systems behaviour. The strengths and weaknesses of the different approaches are identified and discussed, and we make suggestions for a tiered approach for situations with different data availability. This chapter is a modified and extended version of Klapwijk et al. (2014).
Decision-focused agricultural research
Agriculture provides most of our food and many other products. It also affects ecosystem services, such as water regulation, soil protection, and biodiversity conservation. Decision-makers on agricultural systems, from farmers to agricultural ministers, should consider all these functions and their trade-offs, but this rarely happens. Many of agriculture’s products and services are regularly ignored in decision-making, mainly because they are difficult to appraise. This easily leads to decisions with adverse side effects, such as land degradation, pollution, or loss of cultural heritage. ‘Holistic’ decision-making needs decision support approaches that consider factors that are difficult to quantify. Decision Analysis can potentially solve this problem. It recognizes that rational decisions do not normally require precise information on all factors of interest. Decision Analysis harnesses the knowledge of system experts to produce a high-level model of a decision, which reflects the best available information on plausible decision impacts. The model should include all factors experts consider relevant and all important decision impacts, regardless of data availability. Since most variables cannot be precisely quantified, experts estimate their state of uncertainty as confidence intervals or probability distributions. These allow an initial model run, in which these inputs are translated into decision impact forecasts. These are imprecise, but they allow estimating a plausible range of decision outcomes. Often, this is sufficient for selecting one of the decision alternatives. When no clear recommendation emerges, Value of Information analysis can identify key uncertainties that decision-supporting research should address. Decision Analysis solves the problem of data gaps, which has often prevented research from comprehensively and holistically forecasting decision impacts. It also allows explicit consideration of risks and variability. We present several applications of Decision Analysis in agricultural development, demonstrating its ability to convey a holistic understanding of likely decision impacts, in the face of risk and imperfect information.
Zambia Project Inception Workshop Report
This report provides a record of the proceedings of the Project inception workshop and notes on the field visit to Solwezi. The workshop drew about 30 participants from development partners (ACIAR), representatives from Landcare International, government ministries, local government officials from Solwezi, project leaders from Makerere University, Zambia Agricultural Research Institute (ZARI), Copperbelt University and ICRAF, the civil society and different media houses.
Spatial variation of soil macrofauna and nutrients in tropical agricultural systems influenced by historical charcoal production in South Nandi, Kenya
The charcoal sector constitutes an important source of employment and revenue for many tropical agroecosystems. Better understanding of the effects of charcoal-making is thus warranted to guide actions aimed at minimising environmental externalities. Conversion of trees to charcoal eliminates canopy effects associated with the living trees while at the same time creates new conditions in and around spots where the charcoal is produced due to increased concentration of pyrogenic organic matter (PyOM). It is unclear, whether such unintentional PyOM additions play a role in the abundance and distribution patterns of soil macrofauna. A study was conducted in South Nandi (Kenya) to assess effects of PyOM on soil macrofauna, taking advantage of abandoned traditional earth-mound charcoal kilns, where Croton megalocarpus Hutch. and Zanthoxylum gilletii (De Wild.) P.G.Waterman trees were used in charcoal making. Soil and soil macrofauna samples were collected at increasing distances from the centre of the spots. Total C, non-pyrogenic C (non-PyC) and total N progressively increased with increasing distance from the centre of the spots, whereas soil pH, pyrogenic C (PyC), available P and exchangeable K decreased. The number of earthworms and centipedes in Z. gilletii spots (119 and 14 individuals m2, respectively) was twice as high as in kilns where C. megalocarpus was used. Notably, while the number of earthworms in spots rich in Z. gilletii PyOM significantly increased with increasing distance from the centre of the spots, the opposite trend was observed for centipedes. In contrast, no significant differences in the spatial distribution of earthworms or centipedes were found in spots rich in C. megalocarpus PyOM. Furthermore, beetles, termites and crickets were significantly higher in C. megalocarpus than Z. gilletii spots, but sampling distance also had no significant influence. As hypothesised, source of PyOM played a major role in determining soil properties and macrofauna distribution patterns thus showing the value of abandoned charcoal-making spots in contributing to a mosaic of soil conditions that could ultimately affect soil productivity in tropical agricultural systems.
Unravelling the effects of soil and crop management on maize productivity in smallholder agricultural systems of western Kenya—An application of classification and regression tree analysis
To guide soil fertility investment programmes in sub-Saharan Africa, better understanding is needed of the relative importance of soil and crop management factors in determining smallholder crop yields and yield variability. Spatial variability in crop yields within farms is strongly influenced by variation in both current crop management (e.g. planting dates, fertilizer rates) and soil fertility. Variability in soil fertility is in turn strongly influenced by farmers’ past soil and crop management. The aim of this study was to investigate the relative importance of soil fertility and crop management factors in determining yield variability and the gap between farmers’ maize yields and potential yields in western Kenya. Soil fertility status was assessed on 522 farmers’ fields on 60 farms and paired with data on maize-yield and agronomic management for a sub-sample 159 fields. Soil samples were analysed by wet chemistry methods (1/3 of the samples) and also by near infrared diffuse reflectance spectroscopy (all samples). Spectral prediction models for different soil indicators were developed to estimate soil properties for the 2/3 of the samples not analysed by wet chemistry. Because of the complexity of the data set, classification and regression trees (CART) were used to relate crop yields to soil and management factors. Maize grain yields for fields of different soil fertility status as classified by farmers were: poor, 0.5-1.1; medium, 1.0-1.8; high, 1.4-2.5 t ha-1. The CART analysis showed resource use intensity, planting date, and time of planting were the principal variables determining yield, but at low resource intensity, total soil N and soil Olsen P became important yield-determining factors. Only a small group of plots with high average grain yields (2.5 t ha-1; n = 8) was associated with use of nutrient inputs and good plant stands, whereas the largest group with low average yields (1.2 t ha-1; n = 90) was associated with soil Olsen P values of less than 4 mg kg-1. This classification could be useful as a basis for targeting agronomic advice and inputs to farmers. The results suggest that soil fertility variability patterns on smallholder farms are reinforced by farmers investing more resources on already fertile fields than on infertile fields. CART proved a useful tool for simplifying analysis and providing robust models linking yield to heterogeneous crop management and soil variables.
Buffering soil water supply to crops by hydraulic equilibration in conservation agriculture with deep-rooted trees: application of a process-based tree-soil-crop simulation model to parkland agroforestry Systems in Burkina Faso
Farmers deal with risks such as weather, pests, diseases, costs of inputs, market prices of products, (family) labour availability, policies regulating land use and, in some contexts, open interpersonal conflict. Perennial components of agricultural systems, especially trees, provide buffer and filter functions that modify, and generally reduce, the farmers’ sensitivity to such external variables. Maintaining a diversity of activities is a time-tested approach to reducing risks (van Noordwijk et al. 1994). The inclusion of trees that provide annual harvests of fruits or longterm high-value timber can reduce risk, even if the trade-off in resource capture is essentially neutral (Santos-Martin and van Noordwijk 2011). Trees shelter farmers from climate variability and assist in adaptation to longer-term trends (van Noordwijk et al. 2011a).
Suitability analysis and projected climate change impact on banana and coffee production zones in Nepal
The Government of Nepal has identified opportunities in agricultural commercialization, responding to a growing internal demand and expansion of export markets to reduce the immense trade deficit. Several cash crops, including coffee and bananas, have been identified in the recently approved Agriculture Development Strategy. Both of these crops have encouraged smallholder farmers to convert their subsistence farming practices to more commercial cultivation. Identification of suitable agro-ecological zones and understanding climate-related issues are important for improved production and livelihoods of smallholder farmers. Here, the suitability of coffee and banana crops is analyzed for different agro-ecological zones represented by Global Environmental Stratification (GEnS). Future shifts in these suitability zones are also predicted. Plantation sites in Nepal were geo-referenced and used as input in species distribution modelling. The multi-model ensemble model suggests that climate change will reduce the suitable growing area for coffee by about 72% across the selected emission scenarios from now to 2050. Impacts are low for banana growing, with a reduction in suitability by about 16% by 2050. Bananas show a lot of potential for playing an important role in Nepal as a sustainable crop in the context of climate change, as this study indicates that the amount of area suited to banana growing will grow by 40% by 2050. Based on our analysis we recommend possible new locations for coffee plantations and one method for mitigating climate change-related problems on existing plantations. These findings are expected to support planning and policy dialogue for mitigation and support better informed and scientifically based decision-making relating to these two crops. © 2016 Ranjitkar et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Homegardens and the future of food and nutrition security in southwest Uganda
Governments around the world seek to create programs that will support sustainable agriculture and achieve food security, yet they are faced with uncertainty, system complexity and data scarcity when making such choices. We propose decision modeling as an innovative approach to help meet these challenges and offer a case study to show the effectiveness of the tool. We use decision analysis tools to model the possible nutrition-related outcomes of the Ugandan government’s long term agricultural development plan termed ‘Vision 2040’. The analysis indicates potential shifts in household nutritional contributions through the comparison of the current small-scale diverse systems and the envisioned industrial agricultural systems that may replace them. A Monte Carlo simulation revealed that Vision 2040 plans outperform homegardens in terms of energy and some macronutrients, yet homegardens are likely to be better at producing key vitamins and micronutrients, such as Vitamin A. Value of information calculations applied to Monte Carlo outputs further revealed that gathering more data on the annual yields and nutrient contents of staples, pulses, vegetables, and fruits could improve certainty about the nutrition contribution of both scenarios. We conclude that the development of Uganda’s agricultural sector should consider the role that agrobiodiversity in the current small-scale agricultural systems plays in national food and nutrition security. Any changes according to Vision 2040 should also include farmers’ voices and current crop management systems as guides for a sustainable food supply in the region. This modeling approach may be a tool for governments to consider agricultural policy implications, especially given the data scarcity and agricultural variability in regions such as East Africa.
Some suitable grasses and legumes for ley pastures in Sudanian Africa: the case of the Borgou region in Benin
In Sudanian region of Africa, agricultural systems are under increasing pressure because of human and animal population growth, climate changes, extensive practises, decreasing prices for cash crops, etc. Possibilities of intensification in smallholder fanning systems of the Borgou region in Benin are limited due to difficulties to pay for external inputs. Therefore, rural communities rely heavily on low input technologies to increase crop production and animal feed. Cultivated forages are of better-feed quality for ruminants compared to weed fallows. Their integration in cropping system through ley pastures has the potential to increase not only animal feed availability but also to improve soil fertility. This paper reviews some possible grass and legume species that can be used for that purpose with a special focus on the strengths and weaknesses of the species in terms of soil and climate suitability, forage production, nutritive value and soil fertility restoration. The choice of one or several among them as leys in pure stands or mixed forage crops must be taken carefully considering the balance between advantages and disadvantages of the species, the available financial and technical inputs and the adaptation to the local environment.