Understanding farm trajectories and development pathways: Two decades of change in southern Mali

Institutional support for smallholders has been the motor for the expanding cotton production sector in southern Mali since the 1970s. Smallholder farms exhibit diverse resource endowments and little is known on how they benefit from and cope with changes in this institutional support. In this paper we explore farm trajectories during two decades (1994 to 2010) and their link with farm resource endowment and government support. We distinguished a favourable period for cotton production and an unfavourable period during which institutional support collapsed. A panel survey that monitored 30 farms in the Koutiala district in southern Mali over this period was analysed. Based on indicators of resource endowment and using Ascending Hierarchical Classification (AHC), farms were grouped into four types: High Resource Endowed farms with Large Herds (HRE-LH), High Resource Endowed (HRE) farms, Medium Resource Endowed (MRE) farms and Low Resource Endowed (LRE) farms. Average yield, labour productivity and food self-sufficiency status of each type were calculated. Farms remaining in the same type were classified as ‘hanging in’, while farms moving to a type of higher yields, labour productivity and food self-sufficiency status were classified as ‘stepping up’, and farms following the opposite trajectory of deteriorating farming conditions were classified as ‘falling down’. The LRE farms differed from all other farm types due to lower yields, while both LRE and HRE farms differed from the MRE and HRE-LH farm types due to a combination of less labour productivity and less food self-sufficiency. During those two decades, 17% of the farms ‘stepped up’, while 70% of the farms remained ‘hanging in’, and only 13% of the farms ‘fell down'. We found no obvious negative impact of the collapse of government support on farm trajectories. For MRE, HRE and HRELH farms, average N and P use intensity increased from 1994 to 2004 and then decreased during the following cotton crisis. On the other hand, organic fertilizer use intensity increased continuously over the entire monitoring period for HRE-LH and MRE farms. Crop yields did not change significantly over time for any farm type and labour productivity decreased. We discuss how technical options specific for different farm types (increase in farm equipment, sale of cereals, incorporation of legumes and intensification of milk production) and broader institutional change (improvement in finance system and infrastructure, tariffs) can enhance ‘step up’ trajectories for farming households and avoid stagnation (‘hanging in’) of the whole agricultural sector

Exploring options for sustainable intensification in different farming system types of four Africa RISING countries

Sustainable intensification is proposed as a promising way to increase the productivity of agricultural systems while reducing pressure on ecosystems, safeguarding equitable relations among societal groups, and supporting the economic viability of households, enterprises, and communities. In sub-Saharan Africa, the identification and dissemination of options for sustainable intensification is hampered by the large diversity within and between farming systems, and their complexity arising from the interactions among different farm components and external factors. This study therefore uses an integrated farming systems approach to identify and assess context-specific improvements that can then be implemented and tested on-farm to foster experiential learning and facilitate adoption. We conducted a farming systems analysis for nine Africa RISING intervention sites across four countries, based on rapid and detailed farm characterizations, followed by model-supported diagnosis, and exploration of options for sustainable intensification. Farm diversity was described and analyzed by means of typologies and cross-site comparisons. Identified constraints varied depending on site and farming system type, but commonly included low input availability, climatic variability, poor soil fertility, sub-optimal livestock feeding, biotic stresses, and poor access to training and technical advice, all impairing farm productivity, returns to labor and capital inputs, income generation and food security. We investigated entry points that tackle the above constraints by exploring alternative farm configurations, technologies and practices for representative farms. By assessing potential impact of these changes on indicators beyond productivity, trade-offs were identified and assessed, for instance between profitability and household food self-sufficiency, and between nitrogen availability for crop uptake and increased nutrient losses. Taking a systems perspective during the entry point evaluation allowed differentiating potential effects on indicators at the field level versus the farm and household level. The exploration of options for specific farming system types now enables more targeted testing of promising innovations with farmers in the second project phase

Long-term soil organic carbon and crop yield feedbacks differ between 16 soil-crop models in sub-Saharan Africa

Food insecurity in sub-Saharan Africa is partly due to low staple crop yields, resulting from poor soil fertility and low nutrient inputs. Integrated soil fertility management (ISFM), which includes the combined use of mineral and organic fertilizers, can contribute to increasing yields and sustaining soil organic carbon (SOC) in the long term. Soil-crop simulation models can help assess the performance and trade-offs of a range of crop management practices including ISFM, under current and future climate. Yet, uncertainty in model simulations can be high, resulting from poor model calibration and/or inadequate model structure. Multi-model simulations have been shown to be more robust than those with single models and help understand and reduce modelling uncertainty. In this study, we aim to perform the first multi-model comparison for long-term simulations of crop yield and SOC and their feedbacks in SSA. We evaluated the performance of 16 soil-crop models using data from four long-term maize experiments at sites in SSA with contrasting climates and soils. Each experiment had four treatments: i) no exogenous inputs, ii) addition of mineral nitrogen (N) fertilizer, iii) use of organic amendments, and iv) combined use of mineral and organic inputs. We assessed model performance in two steps: through blind calibration involving a minimum level of experimental data provided to the modeling teams, and subsequently through full calibration, which included a more extensive set of observational data. Model ensemble accuracy was greater with full calibration than blind calibration. Improvement in model accuracy was larger for maize yields (nRMSE 48 vs 18%) than for topsoil SOC (nRMSE 22 vs 14%). Model ensemble uncertainty (defined as the coefficient of variation across the 16 models) increased over the duration of the long-term experiments. Uncertainty of SOC simulations increased when organic amendments were used, whilst uncertainty of yield predictions was largest when no inputs were applied. Our study revealed large discrepancies among the models in simulating i) crop-to-soil feedbacks due to uncertainties in simulated carbon coming from roots, and ii) soil-to-crop feedbacks due to large uncertainties in simulated crop N supply from soil organic matter decomposition. These discrepancies were largest when organic amendments were applied. The results highlight the need for long-term experiments in which root and soil N dynamics are monitored. This will provide the corresponding data to improve and calibrate soil-crop models, which will lead to more robust and reliable simulations of SOC and crop productivity, and their interactions