Buffer capacity: capturing a dimension of resilience to climate change in African smallholder agriculture

Building resilience to climate change in agricultural production can ensure the functioning of agricultural-based livelihoods and reduce their vulnerability to climate change impacts. This paper thus explores how buffer capacity, a characteristic feature of resilience, can be conceptualised and used for assessing the resilience of smallholder agriculture to climate change. It uses the case of conservation agriculture farmers in a Kenyan region and examines how their practices contribute to buffer capacity. Surveys were used to collect data from 41 purposely selected conservation agriculture farmers in the Laikipia region of Kenya. Besides descriptive statistics, factor analysis was used to identify the key dimensions that characterise buffer capacity in the study context. The cluster of practices characterising buffer capacity in conservation agriculture include soil protection, adapted crops, intensification/irrigation, mechanisation and livelihood diversification. Various conservation practices increase buffer capacity, evaluated by farmers in economic, social, ecological and other dimensions. Through conservation agriculture, most farmers improved their productivity and incomes despite drought, improved their environment and social relations. Better-off farmers also reduced their need for labour, but this resulted in lesser income-earning opportunities for the poorer farmers, thus reducing the buffer capacity and resilience of the latte

Making climate information useable for forest-based climate change interventions in South Africa

Understanding knowledge systems, that is the combination of agents, practices, and institutions that organize the production, transfer, and use of knowledge and their role in making climate information useable for forest-based climate responses, is critical for building resilience to climate change. This study used the concept of a knowledge system to examine how organizational collaboration, in the processes of forecast translation, influences the production of useable information in forest-based climate change interventions in South Africa. Twenty-two key informant interviews were conducted with actors in the fields of climate change and forestry. Results reveal that carbon sequestration and landscape management are the dominant forest-based climate interventions. Consequently, the information translated from the forecasts is tailored towards facilitating the implementation of these two interventions. Network analysis reveals that actors in the categories of small-scale forest companies and community-based enterprises are less integrated into the process of information production. A concerted effort towards the meaningful integration of all categories of actors in the process of information production, as well as the production of information that encourages the implementation of other types of forest-based climate change interventions such as forest bioenergy, is thus recommended

Key dimensions of land users’ perceptions of land degradation and sustainable land management in Niger State, Nigeria

Declining land productivity remains a challenge for agriculture-based livelihoods and for achieving food security. Yet identifying how land users perceive land degradation and their capacity to manage land in an environmentally sustainable manner can influence the measures initiated to address it. Using the case of Niger State, Nigeria, this study examines land users’ perceptions of land degradation and land management measures to address it in the Nigerian Guinea Savannah. We used the Moderate-resolution Imaging Spectroradiometer derived Normalized Difference Vegetation Index as a proxy for degradation status, selecting 30 communities based on the extent of degraded areas. We adapted the World Overview of Conservation Approaches and Technologies, Sustainable Land Management questionnaires to capture perceptions and administered 225 questionnaires to land users. Through key informant interviews, we collected narrative insights and data on perspectives and motivations of land users to understand land degradation situations and to interpret the questionnaire surveys. We analysed data through descriptive statistics, Principal Component Analysis and qualitative analysis. Our analysis identified four perceptions dimensions of land degradation characteristics, two perceptions dimensions of land degradation drivers, and six perceptions dimensions of sustainable land management. The results also confirmed that degradation in Niger State is both due to widespread unsustainable human activities within Niger state and those by migrant farmers and pastoralism from adjoining Sudan Sahelian states that push people further south, a leakage of ongoing land degradation and conflicts in other areas. To deal with local land degradation in Niger State, improved land tenure, alternative livelihood strategies, poverty eradication and awareness, nature-based sustainable land management practices such as tree-based initiatives, environmentally friendly agriculture such as Farmer Managed Natural Regeneration supported by the necessary political will and institutions are critical

Dataset variability and carbonate concentration influence the performance of local visible-near infrared spectral models

The application of visual and near infrared soil spectroscopy (vis–NIR) is an easy and cost-efficient way to gain a wide variety of soil information to cover high spatial and temporal resolution in large-scale soil surveys and in local field-scale studies. However, unlike for conventional methods, the prediction accuracy of vis–NIR spectral models cannot yet be estimated before the data collection, which hampers its application at the local scale where often a high precision is required (e.g., field experiments). In this study we used soil data from six agricultural fields in Eastern Switzerland and calibrated i) field-specific (local) models and ii) general models (combining all fields) for organic carbon, total carbon, total nitrogen, permanganate oxidizable carbon and pH using partial least squares regression. 24 out of 30 local models showed an accurate or even excellent performance (ratio of performance to deviation (RPD) > 2) and the root mean square errors (RMSE) of prediction were, except for pH, maximum five times higher than the lab measurement error. The variability of a specific soil property and the mean carbonate concentration in the dataset were the two factors influencing the performance of the local models. We found a significant relationship between the coefficient of variation in the dataset and the metrics for model performance (R2, percental RMSE and RPD). Starting from a tolerable prediction error for the spectral measurements, the regressions can be used to develop a sampling design that matches the corresponding target variability. The five inaccurately performing local models with RPD < 2 were on the two fields with highest carbonate content raising the question if local vis–NIR models are suitable for soils with high carbonate concentration. General models combining the datasets from all six fields showed an accurate overall performance but the RMSE on the field level were higher compared to the local models

A Remote Sensing-Based Inventory of West Africa Tropical Forest Patches: A Basis for Enhancing Their Conservation and Sustainable Use

The rate of tropical deforestation is increasing globally, and the fragmentation of remaining forests is particularly high in arable landscapes of West Africa. As such, there is an urgent need to map and monitor these remnant forest patches/fragments and so identify their multiple benefits and values. Indeed, recognizing their existence will help ensure their continued provision of ecosystem services while facilitating their conservation and sustainable use. The aim of this study is therefore to inventory and characterise the current extent and change of remnant forest patches of West Africa, using multi-source remote sensing products, time-series analyses, and ancillary datasets. Specifically, we collate and analyse descriptive and change metrics to provide estimates of fragment size, age, biophysical conditions, and relation to social-ecological change drivers, which together provide novel insights into forest fragment change dynamics for over four decades. We map forests patches outside protected areas with a tree cover ≥30%, a tree height of ≥5 m, an area ≥1 km2 and ≤10 km2. Appended to each patch are descriptive and change dynamics attributes. We find that most fragments are small, secondary forest patches and these cumulatively underwent the most forest loss. However, on average, larger patches experience more loss than smaller ones, suggesting that small patches persist in the landscape. Primary forest patches are scarce and underwent fewer losses, as they may be less accessible. In 1975 most patches were mapped as secondary, degraded forests, savanna, woodland, and mangrove, and relatively few comprised cropland, settlements, and agriculture, suggesting that new forest patches rarely emerged from arable land over the past 45 years (1975–2020), but rather are remnants of previously forested landscapes. Greening is widespread in larger secondary fragments possibly due to regrowth from land abandonment and migration to urban areas. Forest loss and gain are greater across fragments lying in more modified landscapes of secondary forests, while forest loss increases with distance to roads. Finally, larger forest patches harbour a denser tree cover and higher trees as they may be less impacted by human pressures. The number and extent of West African forest patches are expected to further decline, with a concurrent heightening of forest fragmentation and accompanying edge effects. Lacking any conservation status, and subject to increasing extractive demands, their protection and sustainable use is imperative

Assessing climate smart agriculture and its determinants of practice in Ghana: a case of the cocoa production system

Open Access Journal; Published online: 4 March 2018Agriculture in Africa is not only exposed to climate change impacts but is also a source of greenhouse gases (GHGs). While GHG emissions in Africa are relatively minimal in global dimensions, agriculture in the continent constitutes a major source of GHG emissions. In Ghana, agricultural emissions are accelerating, mainly due to ensuing deforestation of which smallholder cocoa farming is largely associated. The sector is also bedevilled by soil degradation, pests, diseases and poor yields coupled with poor agronomic practices. Climate Smart Agriculture (CSA) thus offers a way to reduce the sector’s GHG emissions and to adapt the sector to the adverse impacts of climate change. This study assesses the potential of CSA vis-à-vis conventional cocoa systems to enhance production, mitigate and/or remove GHG emissions and build resilience, in addition to understanding key determinants influencing CSA practices. Using a mixed methods approach, data was collected in Ghana’s Juabeso and Atwima Mponua districts through semi-structured household questionnaires administered to 80 household heads of cocoa farms, two focus group discussions and expert interviews. A farm budget analysis of productivity and economic performance for both scenarios show that CSA practitioners had a 29% higher income per ha compared to the conventional farmers. Estimations using the FAO Ex-Ante Carbon-Balance Tool (EX-ACT) indicate CSA practices preserve forest resources without which the effect on carbon balance as presented by conventional farming would remain a source of GHG emissions. Farm tenure, age of farmers, location of farm, residential status and access to extension services were the main determining factors influencing CSA practices among cocoa farmers. An in-depth understanding of these indicators can help identify ways to strengthen CSA strategies in the cocoa sector and their contributions to climate change mitigation and resilience