Does litterfall from native trees support rainfed agriculture? Analysis of Ficus trees in agroforestry systems of southern dry agroclimatic zone of Karnataka, southern India

Trees of the genus Ficus, integral components of indigenous rainfed agro-ecosystems of the southern dry agro-climatic zone of Karnataka, southern India, have traditionally been associated with the ecological service of soil quality enhancement in addition to various direct use benefits. We assessed the soil enrichment service of Ficus benghalensis L. a common Ficus species in these agroforestry systems, by quantifying nutrient return via litter fall. Litterfall estimation and chemical analysis of litter showed that F. benghalensis trees produce 3,512 kg ha-1 of litter annually which, on decomposition, can satisfy up to 76.70 % of N, 20.24% of P and 67.76% of K requirements of dryland crops annually per hectare. This can lead to an avoided cost of compost of US $ 36.46 ha-1·a-1 in dryland farming systems. The slow rate of decay of Ficus litter, as revealed in litter decomposition studies indicates its potential as ideal mulch for dryland soils. We discuss the complementarity between Ficus litterfall and cropping patterns in Mandya, and its implications for rainfed agricultural systems.

Climate Change Induced Vulnerability of Smallholder Farmers: Agroecology-Based Analysis in the Muger Sub-Basin of the Upper Blue-Nile Basin of Ethiopia

Ethiopia is also frequently identified as a country that is highly vulnerable to climate variability and change. The potential adverse effects of climate change on Ethiopia’s agricultural sector are a major concern, particularly given the country’s dependence on agricultural production, which is sensitive to climate change and variability. This problem calls the need to understand agroecology based vulnerability to climate change and variability to better adapt to climate risks and promote strategies for local communities so as to enhance food security. The objective of this study is to estimate and compare the level of vulnerability of smallholder farmers’ to climate change and variability from three agroecology representing Muger River sub-Basin of the upper Blue Nile basin using Livelihood Vulnerability Index. The research used quantitative and qualitative data collected through Focussed Group Discussions, key informant interviews and a questionnaire survey of 442 sampled households across three different agro-ecologies in the sub-basin. The results reveal that along with the different agro-ecological zone, households and communities experienced different degrees of climate vulnerability. These differences are largely explained by differences in exposure, sensitivity and adaptive capacity of smallholder farmers. The livelihood vulnerability analysis reveals that Kolla agroecology exhibits relatively low adaptive capacity, higher sensitivity and higher exposure to climate change and variability that is deemed to be the most vulnerable agroecology. These contributing factors to a vulnerability in Kolla agroecology are largely influenced by assets, livelihood diversification, innovation, infrastructure, socio-demographic factors, social capital, agriculture, food security, and natural disasters and climate variability. The result furthermore shows that Dega agroecology has least vulnerable owing to its higher adaptive capacity. These results suggest that designing agroecology based resilience-building adaptation strategies is crucial to reduce the vulnerability of smallholder farmers to climate change and variability.

Soil Quality Mapping Studies Using Nematodes as Bioindicators

Soil quality is one of the most important factors in sustaining the global biosphere and developing sustainable agricultural practices. Land use and management practices greatly impact the direction and degree of soil quality changes in time and space. Understanding the effects of land use and management practices on soil quality and its indicators has been identified as one of the most important goals for modern soil science. Soil quality mapping study represents a method for assessing and mapping soil quality changes in time and space in small units. For the present study, changes in the physical, chemical parameters and nematode density of the soils in the rural and urban areas of Thiruvananthapuram district, Kerala, were determined. The soil samples were collected from seven different categories of contaminated soils namely coastal area, sewage disposal area, industrial area, road-side area, agricultural area, market area and gasoline station area, and also from two control stations in rural and urban areas. The soil physico-chemical parameters and nematode density were determined. Geostatistics combined with GIS was applied to analyze the spatial variability of soil physico-chemical characteristics and nematode density. This soil quality mapping study provides a basis for identifying tension zones and serves as a triggering mechanism for implementation of soil contamination mitigating strategies.

Modeling effects of abiotic factors on the abundances of eight woody species in the Harana forest using artificial networks,random forest,and generalized linear models

Background Abiotic factors exert different impacts on the abundance of individual tree species in the forest but little has been known about the impact of abiotic factors on the individual plant,particularly,in a tropical forest.This study identified the impact of abiotic factors on the abundances of Podocarpus falcatus,Croton macrostachyus,Celtis africana,Syzygium guineense,Olea capensis,Diospyros abyssinica,Feliucium decipenses,and Coffea arabica.A systematic sample design was used in the Harana forest,where 1122 plots were established to collect the abundance of species.Random forest(RF),artificial neural network(ANN),and generalized linear model(GLM)models were used to examine the impacts of topographic,climatic,and edaphic factors on the log abundances of woody species.The RF model was used to predict the spatial distribution maps of the log abundances of each species.Results The RF model achieved a better prediction accuracy with R^(2)=71%and a mean squared error(MSE)of 0.28 for Feliucium decipenses.The RF model differentiated elevation,temperature,precipitation,clay,and potassium were the top variables that influenced the abundance of species.The ANN model showed that elevation induced a nega-tive impact on the log abundances of all woody species.The GLM model reaffirmed the negative impact of elevation on all woody species except the log abundances of Syzygium guineense and Olea capensis.The ANN model indicated that soil organic matter(SOM)could positively affect the log abundances of all woody species.The GLM showed a similar positive impact of SOM,except for a negative impact on the log abundance of Celtis africana at p<0.05.The spatial distributions of the log abundances of Coffee arabica,Filicium decipenses,and Celtis africana were confined to the eastern parts,while the log abundance of Olea capensis was limited to the western parts.Conclusions The impacts of abiotic factors on the abundance of woody species may vary with species.This ecological understanding could guide the restoration activity of individual species.The prediction maps in this study provide spatially explicit information which can enhance the successful implementation of species conservation.

Spatial distribution of above ground carbondensity in Harana Forest,Ethiopia

Background:The need for understanding spatial distribution of forest aboveground carbon density(ACD)hasincreased to improve management practices of forest ecosystems.This study examined spatial distribution of theACD in the Harana Forest.A grid sampling technique was employed and three nested circular plots were establishedat each point where grids intersected.Forest-related data were collected from 1122 plots while the ACD of each plotwas estimated using the established allometric equation.Environmental variables in raster format were downloadedfrom open sources and resampled into a spatial resolution of 30 m.Descriptive statistics were computed to summarize the ACD.A Random Forest classification model in the R-software package was used to select strong predictors,and to predict the spatial distribution of ACD.Results:The mean ACD was estimated at 131.505 ton per ha in this study area.The spatial prediction showed thatthe high class of the ACD was confined to eastern and southwest parts of the Harana Forest.The Moran’s statisticsdepicted similar observations showing the higher clustering of ACD in the eastern and southern parts of the studyarea.The higher ACD clustering was linked with the higher species richness,species diversity,tree density,tree height,clay content,and SOC.Conversely,the lower ACD clustering in the Harana Forest was associated with higher soilcation exchange capacity,silt content,and precipitation.Conclusions:The spatial distribution of ACD in this study area was mainly influenced by attributes of the forest standand edaphic factors in comparison to topographic and climatic factors.Our findings could provide basis for bettermanagement and conservation of aboveground carbon storage in the Harana Forest,which may contribute to Ethiopia’s strategy of reducing carbon emission.