Increasing agroforestry areas is an important step to adapt to climate change, increase food security, and have many ecological and socio-economical benefits. Proper planning and strategies are required for the assessment of land potential and selection of suitable land for the multifunctional benefits of agroforestry. Remote sensing (RS) and geographical information system (GIS) tools are widely used to identify the priority areas for agroforestry and policy-making. The multi-high resolutions of Google pro mosaicked images were used as a base map for precision, detailed analysis, and valid interpretation. To identify the farm landscape suitability areas in the Belpada block of Belangiri district, Odisha, a GIS modeling approach was used based on satellite data measurement. The post-monsoon multi-date monthly cloud-free Landsat-8 data and products of the Digital Elevation Model were used to understand the farm landscape characteristics of agroforestry. Soil wetness, slope, drainage, and Normalized Difference Vegetation Index (NDVI) were used in the preparation of landscape suitability analysis. Overall 27.8% (134.16 sq. km) of land was highly suitable, 50.0% (241.85 sq. km) of land was moderately suitable and 19.7% (94.98 sq. km) was marginally suitable and the remaining 2.5% (12.01 sq. km) of land was found unsuitable for agroforestry. Out of 116 villages, 14 villages are found with high (greater than 70%) farmland potentiality, the highest is found in the Jalia village. The moderate and highly suitable land/villages should be given preference for tree-based farming in various agroforestry arrangements. The high-resolution farm landscape potential grid maps were produced for the first time which was earlier a research gap in the past that will support micro-level agroforestry planning. There is a need for a robust synergic approach when integrated with native and multifunctional trees in potentially suitable agroforestry farmland with adequate watershed management and conservation practices enriched with indigenous knowledge that will significantly support achieving the many sustainable development goals (SDGs) up to the smallest unit (village) level.
Tag: Odisha
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.