India has the second largest population in the world and is characterized by a broad diversity in climate, topography, flora, fauna, land use, and socioeconomic conditions. To help ensure food security in the future, agricultural systems will have to respond to global change drivers such as population growth, changing dietary habits, and climate change. However, alterations of how food is produced in the future may conflict with other UN Sustainable Development Goals (SDGs), such as the protection of land resources and climate change mitigation. It is crucial for decision‐makers to understand potential trade‐offs between these goals to find a balance of human needs and environmental impacts. In this paper, we analyze pathways of agricultural productivity, land use, and land‐cover changes in India until 2030 and their impacts on terrestrial biodiversity and carbon storage. The results show that in order to meet future food production demands, agricultural lands are likely to expand, and existing farmlands need to be intensified. However, both processes will result in biodiversity losses. At the same time, the projections reveal carbon stock increases due to intensification processes and decreases due to conversions of natural land into agriculture. On balance, we find that carbon stocks increase with the scenarios of future agricultural productivity as modeled here. In conclusion, we regard further agricultural intensification as a crucial element to help ensure food security and to slow down the expansion of cropland and pasture. At the same time, policies are required to implement this intensification in a way that minimizes biodiversity losses.

This study was carried out to understand the ecological and economic sustainability of floriculture and other main crops in Indian agro-ecosystems. The cultivation practices of four major flower crops, namely Jasminum multiflorum, Crossandra infundibuliformis, Chrysanthemum and Tagetes erecta, were studied in detail. The production cost of flowers in terms of energy was calculated to be 99,622-135,996 compared to 27,681-69,133 MJ ha for the main crops, namely Oryza sativa, Eleusine coracana, Zea mays and Sorghum bicolor. The highest-energy input amongst the crops was recorded for Z. mays (69,133 MJ ha) as this is a resource-demanding crop. However, flower cultivation requires approximately twice the energy required for the cultivation of Z. mays. In terms of both energy and monetary inputs, flower cultivation needs two to three times the requirements of the main crops cultivated in the region. The monetary inputs for main crop cultivation were calculated to be ₹ 27,349 to ₹ 46,930 as compared to flower crops (₹ 62,540 to ₹ 144,355). Floriculture was found to be more efficient in monetary terms when compared to the main crops cultivated in the region. However, the energy efficiency of flower crops is lower than that of the main crops, and the energy output from flower cultivation was found to be declining in tropical agro-ecosystems in India. Amongst the various inputs, farmyard manure accounts for the highest proportion, and for its preparation, most of the raw material comes from the surrounding ecosystems. Thus, flower cultivation has a direct impact on the ecosystem resource flow. Therefore, keeping the economic and environmental sustainability in view, this study indicates that a more field-based research is required to frame appropriate policies for flower cultivation to achieve sustainable socio-ecological development.

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The study was conducted in Honnaver taluka (14°8′ 0″ N to 14°25′ N Latitude and 74°25′ E to 74°45′ E Longitude) of Uttar Kannada district, Karnataka India to assess the carbon sequestration in soils of different land use systems. The IRS P6 LISS-III imageries of the study area was procured from NRSC, Hyderabad and different land use systems in Honnaver taluka were identified with the ground truth data collected from GPS and processed in ERDAS software. The land use land cover (LULC) classes, viz., dense forest, sparse forest, agriculture and open land were identified. The total area in each class was assessed through supervise classification. The soil samples at 1 m depth were drawn at grid point in flat land and along the profile in sloppy land in different land use system. The SOC was estimated using Walkley and Black rapid titration method. The total area in four land use classes is 68520 ha with SOC pool of 12.112 million tonnes. Among different classes dense forest covers highest area (44053 ha) and highest SOC pool (8.82 million tonnes). Among the different land use classes, higher SOC was sequestered in dense forest (200.10 t/ha) followed by sparse forest (166.89 t/ha).The SOC in open land and agriculture land is 145.78 and 82.79 t/ha, respectively. The carbon mitigation potential of dense forest is 2.42 times higher compared to agricultural land followed by sparse forest (2.02 times).

Background Studies on dynamics of land use change still rely on survey questionnaires to collect the data. This study was undertaken to explore the possibility of an appropriate method for estimating land use land cover (LULC) changes for temporal periods for which there is no direct evidence of change spatially explicit. Purpose The hypothesis of this study is that the data on land use generated by official records, empirical field studies, and analysis of satellite imagery resemble with each other. However, in case of inconsistencies the aim of the present study was to identify a scientific and effective method for obtaining precise data on land use dynamics as part of developing appropriate strategies for a sustainable land use management. Methods The land use information on taluk (block) level was collected from the official records. A questionnaire-based survey was done for collecting household data on land use during the period 2014-2016. Furthermore, satellite data were analysed for land use change estimation. In this study, the conventional methods as well as advanced techniques have been used for estimating crop area at various spatial scales. Per pixel classification techniques were used for generating thematic maps from raster data sets. Data collected from GPS were converted into a shape file for generating a signature file for classification of satellite data sets. While generating thematic maps, the maximum likelihood and parallelepiped classification methods were used. Results The inconsistencies were found between land use data obtained from field studies/official records and the data obtained from analysis of remote-sensing imagery. The satellite data outputs are validated using ground-truth data. Various accuracy assessment metrics such as overall accuracy and kappa coefficient (K hat ) are computed from the validation procedure. The overall accuracy and K hat of the generated thematic maps on LULC are found to be ranging from 79 to 84% and 0.76 to 0.83 for various points of time. However, data obtained from official records and results obtained through questionnaire-based survey could not be cross-checked for an accuracy assessment as the physical conditions of land significantly differ year by year. Conclusion An overall spatial and temporal assessment of land use data, using only a questionnaire-based survey, is not scientifically appropriate. Therefore, this study advocates a trans-disciplinary research in social science based on the convergence of social science and space science and technology.