Homegardens are considered a sustainable production system in the tropics, which contributes to biodiversity conservation. The aim of this study was the description of crop diversity, structure and management of homegardens in Central Sulawesi and their classification. In 30 homegardens randomly selected from three villages adjacent to the Lore Lindu National Park, species diversity and abundance were assessed and the Shannon index was calculated. Overall 149 crop species were identified, mainly fruit, vegetable, spice, or medicinal plants. The number of vegetation layers differed depending on age and size of homegardens. Cluster analysis of crop species composition was used to classify different garden types. Not only the spectrum of species cultivated in the homegardens but also the occurrence of these garden types was different among the three villages. This finding was supported by Sørensen’s coefficient. Homegardens from one village, mainly inhabited by transmigrants, contrasted strongly with those from the other two. A markedly lower number of crop species was cultivated there, and species composition was clearly different. The number of crop species and species composition found in homegardens may be attributed to socio-economic conditions of garden keepers as well as to soil quality. Both productivity and sustainability can be enhanced, e.g., by improved soil fertility management such as applying available farm yard manure.
Tag: Home gardens
Land use change patterns and livelihood dynamics on the slopes of Mt. Kilimanjaro, Tanzania
This study is about changes in land use and interactions of land use change and livelihoods in the Chagga farming system on the slopes of Mt. Kilimanjaro, Tanzania. An aerial photo interpretation and fragmentation analysis of the years 1961, 1982 and 2000 was conducted covering approximately the Kirua Vunjo Division, a transect of 152 km2 from the forest reserve edge to the plains. Earlier changes were traced from literature review. The results show the expansion of cultivation to more marginal land down the slope, the disappearance and extreme fragmentation of bush land and appearance and expansion of settlements. The home garden area has experienced some specific internal change, but has not expanded down the slope. In the 1960s there were small open fields and patches of grazing lands amongst home gardens. In the 1980s the area was more uniformly covered by homegardens. Since then it has become patchy again as new homesteads have been built on subdivided farms and more food is produced on the higher slopes. Population pressure and the ensuing expansion of agriculture to more marginal land, intensification of the homegarden system, together with climate changes affecting the water supplies, have caused changes in farmers’ livelihoods. As land scarcity now hinders expansion of agriculture, farm size has seriously decreased, common resources have become scarce, and prices of coffee in the world market remain low, farmers are trying to intensify and diversify their farm production. Local initiative is leading to change, but the locally conceived alternatives are too few and lack integrated approaches of technical agricultural research, economic analysis, and policy studies and reforms. Non-agricultural activities and paid employment are becoming increasingly important. However, due to considerable entry barriers to remunerable off-farm jobs, not all households enjoy equal access to attractive non-farm opportunities. The future welfare of the area will depend on increasing the marketable knowledge and skills of the population that will enable it to become integrated in the economy of the region and the country.
A preliminary classification of fruit-based agroforestry in a highland area of northern Thailand
Tree fruit crops are an increasingly important component of highland cropping systems in northern Thailand. A survey was conducted in three highland hill tribe villages in an upland watershed in Mae Hong Son Province to examine and classify the fruit-based cropping activities used by villagers. Members of ten households in each village were interviewed to establish activities and crop histories for each plot of land held by the household. From the sample of 85 ‘gardens‘ (plots with ten or more fruit trees), a field-level classification structure was developed reflecting function of trees, use and nature of herbaceous intercrops, and pattern of components. Through the classification process, four groups and 11 subsystems of highland tree fruit-based agroforestry were identified. The single most abundant subsystem was ‘mixed home gardens‘. A strong commercial element was also obvious. The survey indicates a very diverse ‘customized’ use of the fruit cropping system. The classification has potential for use in more extensive surveys of the nature of fruit cropping activities in the highlands and as a tool for further analysis in the study area.
Application of combined pixel-based and spatial and spatial-based approaches for improved mixed vegetation classification using ikonos
Classifying a mosaic of coffee systems, each in a different stage of structural complexity is not obvious when that ranges from monoculture to a complex agro-ecosystem, with various shade and fruit trees mixed in different degrees of density. Distinction into different sub-classes incorporating tree complexity and tree cover, is important as tree density and the generally related amount of litter are important from a soil erosion perspective. In this study, the objective was to classify different coffee garden systems plus several other minor vegetation classes existing in the area using IKONOS in Sumberjaya district, Lampung Province, Indonesia. Pixel-based classification approach was integrated with spatial-based approach to reach an improved classification result. In the supervised pixel-based approach training samples are collected to generate statistical parameters for the classifier to classify the whole image. The spatial-based approach refers to segmentation procedure, known also as object-based classification. Two methods of integration were explored and pure pixel-based-approach was as well conducted for comparison purpose. Results were then tested using ground check data. The methods tested are: pure spectral approach of (a) supervised classification using maximum likelihood classifier, integration with segmentation which was done in two ways, by (b) classifying the segments and by (c) combining the pixel-based classified image with segment image using majority rule. Of all the three methods the combination using majority rule showed the highest overall accuracy. Several points were discussed as feedback to the methods tried as well as to improve the classification result
Agroforestry systems improvement in Southeast Asia: Annual Report for 1994 – Project 4.6
Agroforestry Systems Improvement research in Southeast Asia focuses on the development of alternatives to unsustainable slash-and-burn agriculture, and the rehabilitation of degraded uplands. This work is targeted to three of the region’s key ecosystems: The forest margins, the imperata grasslands, and the sloping permanent farmlands.This was the second year of ICRAF’s program in Southeast Asia. We formulated a central hypothesis to focus the work in each of our three key ecosystems. We began implementing research in Indonesia and the Philippines to addresses the three issues.
Agroforestry Suitability for Planning Site-Specific Interventions Using Machine Learning Approaches
Agroforestry in the form of intercropping, boundary plantation, and home garden are parts of traditional land management systems in India. Systematic implementation of agroforestry may help achieve various ecosystem benefits, such as reducing soil erosion, maintaining biodiversity and microclimates, mitigating climate change, and providing food fodder and livelihood. The current study collected ground data for agroforestry patches in the Belpada block, Bolangir district, Odisha state, India. The agroforestry site-suitability analysis employed 15 variables on climate, soil, topography, and proximity, wherein the land use land cover (LULC) map was referred to prescribe the appropriate interventions. The random forest (RF) machine learning model was applied to estimate the relative weight of the determinant variables. The results indicated high accuracy (average suitability >0.87 as indicated by the validation data) and highlighted the dominant influence of the socioeconomic variables compared to soil and climate variables. The results show that >90% of the agricultural land in the study area is suitable for various agroforestry interventions, such as bund plantation and intercropping, based on the cropping intensity. The settlement and wastelands were found to be ideal for home gardens and bamboo block plantations, respectively. The spatially explicit data on agroforestry suitability may provide a baseline map and help the managers and planners. Moreover, the adopted approach can be hosted in cloud-based platforms and applied in the different agro-ecological zones of India, employing the local ground data on various agroforestry interventions. The regional and national scale agroforestry suitability and appropriate interventions map would help the agriculture managers to implement and develop policies.