Coffee agroforestry is a conservation strategy that has shown promise to support the diversity of bird, bat, and insect communities, but few studies have focused on non-volant mammals in coffee farms. We assessed mammal diversity within coffee agroforestry systems in Kodagu, India and investigated the impacts of the non-native shade tree species, Grevillea robusta, on mammal diversity. Twenty farms, with varying amounts of G. robusta planted within the coffee farm, were sampled throughout three rainfall zones during the 4-month study period. We captured six species of small mammals, with indirect methods yielding an additional five species, totaling 11 mammal species. Contrary to current ecological thought, we found that increased amounts of G. robusta did not have a negative impact on either abundance or richness of mammals. Small mammal abundances were higher at farms with greater amounts of herbaceous ground cover and larger, mature shade trees, while small mammal species richness was found to increase with an increase in tree species richness as well as greater amounts of herbaceous ground cover. Additionally, small mammal abundance was higher at coffee farms closer to forested areas. Based on these findings, we suggest the maintenance or cultivation of shade tree richness, mature shade trees, and herbaceous ground cover within coffee farms and preservation of forested areas within the landscape to enhance coffee agroforestry habitat for non-volant mammals. We hope that these habitat requirements will be incorporated into conservation strategies for the promotion of biodiversity within coffee agroforestry systems.
Tag: data analysis
Back to baselines: measuring change and sharing data
International research for development is under increasing pressure to demonstrate development outcomes that enhance people’s food security and well-being while preserving the natural resource base. The CGIAR Research Programme on Climate Change, Agriculture and Food Security (CCAFS) integrates thematic research at several levels with multiple global, regional and local partners, with the aim of identifying and testing pro-poor adaptation and mitigation practices, technologies and policies for food systems, adaptive capacity and rural livelihoods. We describe a process to design and implement baselines across 2,095 households and 108 villages spread over 15 sites in 12 countries of West and East Africa and South Asia, and to archive, document and analyse the data and make them publicly available. We critically examine the process in relation to design, institutional arrangements, and partnerships. The process was long, complex and expensive. We share important lessons learned regarding how to obtain baseline data for rural populations in agricultural systems, as a basis for prioritising research and monitoring progress towards enhanced food security and increased household welfa re, in part through uptake of sustainable changes in agricultural practices
Participatory evaluation of integrated pest and soil fertility management options using ordered categorical data analysis
Maize is becoming the major food crop around Lake Victoria. Major constraints to its production are Striga, stem borer, and declining soil fertility. Innovative integrated technologies have been developed: the’push-pull’ system (intercropping with Desmodium and surrounded by Napier grass), soybean and Crotalaria rotations, and imidazolinone-resistant (IR) maize seed. In 12 demonstration trials in four villages in Siaya and Vihiga districts (Kenya) and two villages in Busia (Uganda) in 2003 and 2004, 504 farmers evaluated all cropping systems and a mono-cropped continuous maize, each cropped with IR or local maize, and supplemented or not with fertilizer, totaling 16 treatments. Farmers evaluated all treatments for yield, resistance to Striga and stem borer, improvement of soil fertility, and provided an overall evaluation score, using an ordered scale of 1 (very poor) to 5 (very good). Data were analyzed using ordinal regression, estimating log odds ratios. The results show significant preferences for all treatments over the control. Push-pull with IR and fertilizer had the highest log odds ratio (2.93), so the odds of farmers preferring this treatment are 18.7 times the odds that farmers prefer the control. The odds ratios for the other push-pull combinations were generally highest (9-15), followed by the rotation systems with Crotalaria (3.5-7.0), and soybeans, especially with IR maize and fertilizer (odds ratio of 5.7). In mono-cropping systems, IR maize was only appreciated in combination with fertilizer, and then only in 2004. Push-pull and Crotalaria were more appreciated in 2004 than in 2003. Farmers in Vihiga had a stronger preference for push-pull, and those in Busia for soybean rotations. Significant differences among farmers were observed, but the effects were small. Women appreciated push-pull more than men, while other technologies were gender-neutral. Older farmers were more likely to prefer push-pull and Crotalaria with fertilizer. Livestock ownership was not found to have an effect on technology preferences. Measured yield, stem borer and Striga infestation all had significant but small effects, although their inclusion did not eliminate the treatment effects, indicating that other factors are still important. OLS of the scores for different criteria on the overall score shows yield to be the most important criterion (coefficient of 0.40), followed by soil fertility enhancement (0.25) and Striga resistance (0.13). Labor saving (0.09) and stem borer resistance (0.03) are relatively minor criteria. This research shows that scoring and analysis with ordinal regression is a convenient way to solicit and analyze farmers’ preferences for new technologies, with wide applicability in farming systems and participatory research. Its application here shows that farmers like the new technologies, especially push-pull, but that there are substantial differences between years, sites and farmers. The use of this method can be very helpful to define and focus further research and formulate specific and targeted recommendations for agricultural extension.
Effects of corn straw or mixed forage diet on rumen fermentation parameters of lactating cows using a wireless data logger
The objective of this study was to evaluate the effect of two different forage types on rumen fermentation parameters and profiles using a wireless data logger. Eight lactating cows were randomly assigned to one of two dietary treatments with a low forage diet with corn straw (CS) or a high forage diet with mixed forage (MF) as the forage source, respectively. Dietary physically effective neutral detergent fiber (peNDF) content was 11.3% greater in CS. Dry matter intake and milk fatty acid content decreased upon CS (P<0.05). Ruminal pH, temperature and oxidation reduction potential (ORP) were monitored for 14weeks. The CS group had significantly higher pH but lower temperature and ORP compared to MF (P<0.01). With the CS diet regime, pH at the time before morning feeding, rumination and post-ingestion were significantly higher than those in the MF group (P<0.05). However, times with the ruminal pH below 6.0 and 5.8 were significantly reduced (P<0.05), whereas ruminal pH below 5.6 tended to be lower (P=0.07). The results indicated that rumen fermentation parameters were affected by forage types and dietary peNDF content might be predominant in ruminal pH regulation. © 2016 Japanese Society of Animal Science.
Characterizing local adaptive capacity at various scales: A framework for adaptation planning
An assessment of recent peat forest disturbances and their drivers in the Cuvette Centrale, Africa
The largest tropical peatland complex in the Cuvette Centrale is marked by persistent knowledge gaps. We assessed recent peat forest disturbances and their direct drivers from 2019 to 2021 in Cuvette Centrale, spanning the Republic of Congo (ROC) and the Democratic Republic of Congo (DRC). Utilizing peatland maps and Radar for Detecting Deforestation alert data, we analyzed spatial and temporal patterns of disturbances. Further, we examined 2267 randomly sampled peat forest disturbance events through visual interpretation of monthly Planet and Sentinel 2A data to identify direct drivers. Our findings revealed that between 2019 and 2021, about 91% of disturbances occurred in DRC, with hotspots concentrated in the northwest Sud-Ubangi district. Disturbances predominantly followed a sharp seasonal pattern, recurring during the first half of each year with temporal hotspots emerging between February and May, closely associated with smallholder agriculture activities. Smallholder agriculture accounted for over 88% of disturbances in Cuvette Centrale, representing a leading role both in ROC (∼77%) and DRC (∼89%). While small-scale logging contributed 7% to the disturbances in the region, it constituted an important driver (18%) in the ROC. Other drivers included floods, roads, and settlements. Approximately 77% of disturbances occurred outside managed forest concessions in Cuvette Centrale, with 40% extending into protected areas. About 90% of disturbances were concentrated within 1 km of peat forest edges and ∼76% of the disturbances occurred within 5 km of road or river networks. The insights underscore the crucial need for effective peat forest conservation strategies in Cuvette Centrale and can inform national policies targeting peatland protection, aligning with commitments in the Brazzaville Declaration and the Paris Agreement. Further, our findings on direct driver assessment could serve as a reference dataset for machine learning models to automate the visual interpretation and upscale the assessment across the entire region.
A mixed application of Geographically Weighted Regression and unsupervised classification for analyzing latex yield variability in yunnan, China
This paper introduces a mixed method approach for analyzing the determinants of natural latex yields and the associated spatial variations and identifying the most suitable regions for producing latex. GeographicallyWeighted Regressions (GWR) and Iterative Self-Organizing Data Analysis Technique (ISODATA) are jointly applied to the georeferenced data points collected from the rubber plantations in Xishuangbanna (in Yunnan province south China) and other remotely-sensed spatial data. According to the GWR models Age of rubber tree Percent of clay in soil Elevation Solar radiation Population Distance from road Distance from stream Precipitation and Mean temperature turn out statistically significant indicating that these are the major determinants shaping latex yields at the prefecture level. However the signs and magnitudes of the parameter estimates at the aggregate level are different from those at the lower spatial level and the differences are due to diverse reasons. The ISODATA classifies the landscape into three categories: high medium and low potential yields. The map reveals that Mengla County has the majority of land with high potential yield while Jinghong City and Menghai County show lower potential yield. In short the mixed method can offer a means of providing greater insights in the prediction of agricultural production. © 2017 by the authors.
Ecosystems Services from Community Forestry: Prospects and Challenges for Improving Local Livelihoods in Nepal
Nepal’s community forestry is touted as a development success, especially in relation to how local community groups can be empowered by the state to conserve and manage communal forest areas for local livelihoods improvements. However, this success is limited in relation to the production and supply of conventional forest products or a narrow range of ecosystem services (ES). The concept of ES has recently landed in Nepal’s policy domain and an evolving body of research has also highlighted the potential of applying ES in community forestry for augmenting livelihoods improvements, climate change adaptation and mitigation, and biodiversity conservation. Yet, there is still a lack of evidence on the degree to which community forest user groups are expanding their forest management practices by including additional range of ES. Drawing on 14 case studies of community forests from across the country, as well as a recent review of literature and analysis of secondary data, this chapter assesses the potential of an ecosystem centred approach to community forest management for local community livelihoods in Nepal. We identify a variety of emerging initiatives towards ES-based management of community forests, demonstrating the need for a strategic shift away from the narrowly focused, subsistence-oriented forest management to a more holistic management of forest ES. We also identify priority challenges that need to be tackled in facilitating this shift to achieve the sustainable production of ES for livelihoods improvements and its contribution towards climate change mitigation and sustainable development goals.
Oil palm and rubber-driven deforestation in Indonesia and Malaysia (2000-2021) and efforts toward zero deforestation commitments
This study evaluates the oil palm and rubber-driven deforestation in Indonesia and Malaysia between 2000–2021 through secondary data analysis from a rich data set by collecting and integrating secondary data from multiple sources. Results show a significant tree cover loss, including 82% and 250% of primary forest loss in Indonesia and Malaysia. Oil palm harvested area increased by 650% in Indonesia and 50% in Malaysia, while palm oil production rose by 566% in Indonesia and 65% in Malaysia over the same period. The study also find that rubber-harvested areas increased by 54% in Indonesia but decreased by 20% in Malaysia, with rubber production increasing by 108% in Indonesia but decreasing by 49% in Malaysia. Over the past two decades, Indonesia’s palm oil export volume has risen by 619%, while Malaysia’s has increased by 83%. Indonesia’s natural rubber export volume also increased by 69%, but Malaysia’s decreased by 33%. The study reveals that the impact of oil palm and rubber on deforestation varies by region and period. However, plantation expansion and associated forest conversion have slowed, which can be associated with the consumer demand for no deforestation oil palm and rubber products. Many of the world’s largest traders and producers have pledged zero deforestation commitments, guaranteeing the elimination of deforestation from their supply chains in Indonesia and Malaysia. Such commitments can only be accomplished through government, private sector, farmers, and community collaboration. Finally, to ensure sustainability in these sectors, comprehensive policies and practical solutions that support farmers’ livelihoods are also crucial.
The emergy-data envelopment analysis (EM-DEA) approach handbook: An illustrated guide on how to use the EM-DEA approach to assess resource- and energy-use efficiency and the sustainability of agricultural and forestry ecosystems
Emergy-Data Envelopment Analysis (EM-DEA) is a methodological approach for achieving complete environmental-economic accounting of different production systems. In an age when resources are scarcer than ever before, and the environmental impact of humanly designed systems of production is a major concern when deciding which system could better contribute to human and economic development without compromising the future of the global environment, using a reliable method for the comparative assessment of the efficiency and sustainability of different production systems is critical when making smart decisions. This handbook provides a step-by-step instruction to help users apply the EM-DEA approach to simultaneously assess the resource and energy use efficiencies, and sustainability of agricultural and forestry ecosystems as a whole. This approach was developed to address the lack of a singular method to assess complete environmental accounting and compare the sustainability performance of agro-ecosystems. The EM-DEA approach does so by combining emergy analysis (EMA) and data envelopment analysis (DEA) methods. By offering flexibility to account for various natural, human and economic resources such as land or input contributions from farm animals, it provides a means to do a comprehensive environmental accounting throughout the lifetime of agricultural and forestry systems. This approach was empirically tested with a comparative analysis of five maize production systems in Ghana, Africa. The results demonstrated that the application of the EM-DEA approach leads to complete environmental-economic accounting. Thus, EM-DEA is an innovative approach that could be used to support decision making when comparing different production systems as a whole.