Agroforestry suitability mapping of India: geospatial approach based on FAO guidelines

Ahmad, Firoz; Uddin, Meraj; Goparaju, Laxmi

doi:10.1007/s10457-018-0233-7

Cited by 50 publications

(29 citation statements)

References 28 publications

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“…AGF capacity (shade of color from green to red: high to low) and approximate percent contribution (red line) for achieving different SDG goals compiled by authors food security, improving in creating a viable healthy city and in providing a sustainable overall prosperous environment in the prevailing climate change setup whereas indirectly it can serve several others SDGs goals (because of their complex linkage) simultaneously that aim to provide better health and education, women empowerment, effective contribution towards clean water and energy, capacitybuilding and improving the effectiveness and accountability for all section of society/citizen. The AGF priority for achieving SDG goals significantly varies geographically which needs an appropriate prior evaluation of land, soil, topography, climate, and socioeconomic potentiality/suitability for AGF setup and then scaling/magnifying it for local/regional/national levels (Ahmad et al, 2019). More than 130 countries have shown their interest through national development plans to show their priorities for achieving SDGs (Chimhowu et al, 2019) and hope agroforestry will get sufficient space in their priorities with adequate policy attention and investment to fulfill its potential (Tsioumani, 2018).…”

Section: Agroforestry For Achieving Sustainabilitymentioning

confidence: 99%

Agroforestry: An effective multi-dimensional mechanism for achieving Sustainable Development Goals

Goparaju¹,

Ahmad²,

Uddin

et al. 2020

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The ecological footprint has far surpassed the biocapacity of our globe to fulfill the current requirement of the people living on the earth which needs sustainable vision with synergic alternative/option to meet the current human demand without compromising the need for the future generation. We examined the various SDGs goals with respect to agroforestry capacity and contribution based on available literature and knowledge. The study provides a better understanding of the synergic approach/strategies with retrospective and prospective ways for choosing agroforestry exercise which is an effective mechanism for providing multi-dimensional ecosystem services without interruption in achieving the majority of SDGs goals. The outcome of the evaluation highlights that agroforestry can contribute very significantly and can play a vital role in SDG-1, SDG-2, SDG-11, SDG-13, and SDG-15 directly in mitigating poverty, contributing towards food security, improving in creating a viable healthy city and in providing a sustainable overall prosperous environment in the prevailing climate change setup whereas indirectly it can serve others SDGs goals simultaneously that aim to provide better health and education, women empowerment, effective contribution towards clean water and energy for all sections of the society/citizen. The analysis further concluded although agroforestry has a vibrant future and hope it will get adequate priority in various countries with a focus on policy and investment.

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Section: Agroforestry For Achieving Sustainabilitymentioning

confidence: 99%

Agroforestry: An effective multi-dimensional mechanism for achieving Sustainable Development Goals

Goparaju¹,

Ahmad²,

Uddin

et al. 2020

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“…The agro-ecological region map of India (Bandyopadhyay et al, 2009) which was cited by Ahmad et al (2018) and all categories were brought under the GIS domain. The high Greenhouse gas (GHG) emission (>1000) category was overlaid over the agro-ecological region map and was given in figure for further analysis (Fig.…”

Section: Data Acquisition and Processingmentioning

confidence: 99%

“…The Rajasthan, Gujarat, Maharashtra and Karnataka, part of Tamil Nadu and Andhra Pradesh states will be the highest sufferer in the future. Furthermore, these areas fall on arid and semi-arid agro-ecosystem region characterized by severe weather conditions such as low rainfall pattern, high solar radiation, high wind velocity, and increased evapotranspiration whereas productivity potential of the land is also usually low due to immature, structure-less, and very coarse texture of soil with low water retention capacity and poor nutrient status (Ahmad et al, 2018 prone area analysis shows that 38% of the geographical area of India is drought-prone. The spatial pattern is more scattered over India whereas it is more pronounced over the western and southern regions of India characterized over the arid and semi-arid agro-ecosystem region (Fig.…”

Section: The Analysis Of Agriculture Vulnerabilitymentioning

confidence: 99%

The Geospatial Understanding of Climate-Smart Agriculture and REDD+ Implementation: Indian Perspective

Ahmad¹,

Farooq

Goparaju³

et al. 2020

Ekológia (Bratislava)

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Geospatial technology has an enormous capacity to analyze large and diversified datasets for evaluating the hidden spatial relationship which provides a better comprehension of the subject and helps significantly in policymaking and planning future strategies.This study has examined the relationship among diversified remote sensing and GIS datasets such as GHG emission from cropland, rice cultivation area, agro-ecological region, Land use/Land cover (LULC) categories, long-term NDVI (1982−2006) based negative changes, agriculture vulnerability, drought-prone area and future (2021, 2050) climate change anomalies (RCP-6) of India for better understanding and knowledge of the GHG emission scenario, vegetation health, LULC, agriculture vulnerability, and future climate change impact. The LULC analysis revealed that 49.6% (1 628 959 km²) of the geographical area was found to be under category ‘cropland’. The 32.5% of the total cropland areas are used for rice cultivation whereas around 76% of this rice cultivation area is producing high GHG emission (>1000 Mg CO2 e/yr.). LULC categories ‘Cropland’ and ‘Plantation’ show the long-term (1982−2006) negative change equivalent to 19.7 and 70.2% respectively. Similarly, around 56% of LULC categories representing the forest show the long-term negative change whereas the maximum change (139 867 km²) was found in the category of ‘Deciduous Broadleaf Forest’. The 30.6% of the LULC category of ‘cropland’ falls in very high agriculture vulnerable areas whereas 31.7% of the same category falls in the drought-prone area. The significant increase in temperature and abrupt rainfall patterns were observed during Kharif and Rabi seasons in the future. Such variation of climate parameter in the future not only adversely affect the agriculture crop production but also the natural vegetation of India.The outcomes of the present study would support the policymakers of India to implement the climate-smart agriculture (CSA) and REDD+ on an urgent priority based on a proper evaluation of the socio-economic condition of the poor people. It will certainly help in the reduction of GHG emission, forest amelioration, will bring the resilience in livelihood and mitigate the poverty among the rural communities for the betterment of people.

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“…Few researchers and scientists have evaluated land in GIS Domain for agroforestry (Ritung et al 2007;Reisner et al 2007). The Remote Sensing data and GIS modeling concept were successfully used for Agroforestry suitability mapping (Ahmad et al 2018a;Ahmad et al 2018b) The present study aims at applying Remote Sensing data and GIS towards visualizing the spatial features of land, soil, and topographical data to reveal the trends and their association and to achieve an agroforestry suitability mapping of Samastipur district of Bihar, India. The agroforestry map was further evaluated based on the various land use/ land cover categories.…”

Section: Most Of the States Of India Including Biharmentioning

confidence: 99%

“…The Normalized Difference Vegetation Index (NDVI) is widely used vegetation index which significantly reflects the vegetation health and vigour from remote sensing data (Gomes et al 2017) and can be successfully used as one of the parameters for agroforestry suitability mapping (Ahmad et al 2018a). NDVI map produced from Senetal datasets were used in this study is given in figure 4.…”

Section: Ndvi Mapmentioning

confidence: 99%

Assessment of remote sensing and GIS application in identification of land suitability for agroforestry: A case study of Samastipur, Bihar, India

Ahmad¹,

Uddin²,

Goparaju³

2018

Contemporary Trends in Geoscience

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Agroforestry provides the foundation for climate-smart agriculture to withstand the extreme weather events. The aim of the present study was to identify the land of Samastipur, Bihar, India for agroforestry, based on GIS modeling concept utilizing various ancillary (soil fertility) and satellite data (DEM, wetness, NDVI and LULC) sets. This was achieved by integrating various thematic layers logically in GIS domain. Agroforestry suitability maps were generated for the Samastipur district of Bihar, India which showed 48.22 % as very high suitable, 22.83 % as high suitable, 23.32% as moderate suitable and 5.63% as low suitable. The cross evaluation of agroforestry suitability with LULC categories revealed that the 86.4 % (agriculture) and 30.2% (open area) of land fall into a very high agroforestry suitability category which provides the huge opportunity to harness agroforestry practices if utilized scientifically. Such analysis/results will certainly assist agroforestry policymakers and planner in the state of Bihar, India to implement and extend it to new areas. The potentiality of Remote Sensing and GIS can be exploited in accessing suitable land for agroforestry which will significantly help to rural poor people/farmers in ensuring food and ecological security, resilience in livelihoods.

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Agroforestry suitability mapping of India: geospatial approach based on FAO guidelines

Cited by 50 publications

References 28 publications

Agroforestry: An effective multi-dimensional mechanism for achieving Sustainable Development Goals

Agroforestry: An effective multi-dimensional mechanism for achieving Sustainable Development Goals

The Geospatial Understanding of Climate-Smart Agriculture and REDD+ Implementation: Indian Perspective

Assessment of remote sensing and GIS application in identification of land suitability for agroforestry: A case study of Samastipur, Bihar, India

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