Various elements must be considered when estimating the cost of a crop. It divides agricultural costs into five categories and provides calculations for each. It also gives examples of how to figure out how much a crop cost. It is a theoretical article that always guide the implementation of estimating the cost of cultivation. This theoretical study was used in the proposed model to calculate the cost of cultivation. It was very helpful as it provided elementary description to calculate the costs for cultivation. The formulas proposed in this study was used in the proposed system to estimate the costs till the year 2028. From 2004 to 2015, the goal of this research is to evaluate the gap between various costs and gross value of output , as well as the trends of input utilization and critical factors for gross value of output of gram crop across top production states. The findings demonstrate so after 2009–2010, all states’ GVO and overall costs increased significantly. The commencement of the Government of India’s agricultural waiver scheme in 2008–2009 was found to be the cause of a large increase in operational costs from 2009 to 2010. It was also obvious that the compound annual growth rate was larger in 2009–2010 than in 2014–2015 when comparing 2004–2005 to 2007–2008. Profit margins were high in Madhya Pradesh and Rajasthan, indicating a cost-cutting trend. This work provided a comparative study about the costs between different states of India. The proposed work has used Ensemble regression algorithms that is used to forecast the costs till 2028. It provides a comparative study of a crop for a specific state from 2010– 2028. Hence the user would be able to identify the trends of costs from the year 2010–2028.
The forecasting is explicitly applied on the India’s cost of cultivation survey data from 2010–2018. This provides an elaborative view of operational cost,led grow lights fixed cost, total cost, Cost Concepts were displayed in form of a graph for better understanding of the trends of the costs. Agriculture is currently a dominant use of the land and a major driver of environmental change and thus agricultural landscapes are key to achieve the United Nations Sustainable Development Goals , such as food security and environmental sustainability . As social-ecological systems, agricultural landscapes reflect the intertwined interaction between humans and nature through time . The contribution of agricultural landscapes to society goes beyond provisioning ecosystem services . Farmlands can contribute with a wide range of other key ecosystem services to society such as regulating and cultural , while providing habitat for biodiversity . However, the social-ecological outcomes of farmlands relate to the characteristics of supporting landscapes, which ultimately reflect the management practices, i.e. farming systems prevailing at the landscape level . Distinct farming systems are characterized by different field- and farm-level agricultural practices, reflecting farmers’ decisions on crop selection, livestock management and/or the maintenance of non-crop elements . Managed under intensive FS, agricultural landscapes contribute mainly to food and fibre production, but at high costs for the natural environment . Agricultural intensification has been pinpointed as major driver of land use change, causing landscape homogenization, habitat degradation and loss, and the decline of species of conservation interest . Conversely, farmlands managed under low-intensive farming systems, especially those designated in Europe as High Nature Value farmlands , have been highlighted for contributing to a wide range of ecosystem services, beyond support to biodiversity .
Characterized by low levels of agrochemical inputs and livestock stoking, minimal mechanization and the rotational use of the land, HNV farming systems maximize the use of territorial resources for agricultural production, while promoting landscape level heterogeneity. Therefore, the maintenance of HNV farming systems has been related to the occurrence of species and/or habitats, among which some of conservation concern . The European Union Common Agricultural Policy has been recognizing the role of agricultural landscapes to meet societal environmental concerns, namely by explicitly defining specific practices that farmers’ should observe or by supporting low intensity FS fostering the nature value of agricultural landscapes . Overall, CAP instruments align with other EU policy instruments such as Nature Directives and the EU Biodiversity Strategy, which, among other objectives, aim to include agricultural areas under high-diversity landscape features and organic farming management through uptaking agro-ecological practices for a positive contribution of agriculture to biodiversity and ecosystem services . Still, while the link between agriculture and biodiversity and ecosystem services has been widely described, data-driven research assessing such relationship at the landscape level across taxonomic groups and services is still scarce. Data availability is a major limitation to advance knowledge on how farming systems shape biodiversity and ecosystem services at the landscape level . The Integrated Administration and Control System database, managed by EU Member States paying agencies to monitor and control CAP payments, has been highlighted as a potential source of information on farmers’ practices at the farm-level . Coupled with the Land Parcel Information System , a spatially-explicit identification system for agricultural plots, IACS provide a high spatial and temporal resolution dataset integrating several dimensions of agricultural management, such livestock stocking, crops and land use . The value of IACS goes beyond the support to assess and monitor the impacts of CAP instruments. This comprehensive source of high-resolution data has been increasingly highlighted for its potential to support data-driven research in agricultural landscapes . Examples include mapping HNVf , analysis of crop and landscape diversity , land use change , definition and analysis of farming systems , or modelling exercises to assess the impacts of policies on FS and biodiversity . However, studies using this detailed source of data reflecting agricultural management have seldom been performed to assess and monitor patterns of biodiversity and the delivery of ecosystem services at the landscape level. Farming systems conceptual and methodological approaches have been pointed as suitable tools to explore the links between farmers’ practices and biodiversity and ecosystem services .
Considering farms as systems and units of analysis, a FS includes a set of farms sharing similar characteristics, namely in what concerns land type, labor and means of production, reflected in cropping and livestock subsystems combinations and underlying management decisions such as livestock rates and crop types, or the use of fertilizers. Such characteristics result from farmers’ decisions, which are jointly driven by policies , socio-economic factors and by biophysical conditions . Ultimately, farmers’ decisions are reflected as the dominance of a given farming system at the landscape level . Thus, exploring the links between FS and biodiversity and ecosystem services is essential to improve our understanding on the impacts of agriculture on biodiversity in landscapes under different agricultural management. In this research, we contribute to advance the state-of-the-art, by exploring the link between FS and patterns of biodiversity at the landscape level. To do that, we used IACS data to identify and characterize the spatial distribution of farming systems in a region in NW Spain and explored the relationships between the composition of FS, and species and habitats richness. More specifically, we aimed to answer the following research questions: Can IACS be used to map and characterize different farming systems at the landscape-level? and, Is the occurrence of specific FS linked with higher levels of biodiversity? Results of the analysis between FS and targeted biodiversity indicators are discussed for the Galician region, and implications drawn with respect to the assessment and monitoring of patterns of biodiversity in agricultural landscapes, including High Nature Value farmlands, across the EU. Our study area covers Galicia, an administrative NUTS 2 region located in North-western Spain, between 41◦ N and 44◦ N latitude, and, 9.5◦ W and 6.5◦ W longitude with a total area of 29,575 km2 . Most of the region is characterized by an oceanic/dry summer climate and is located in the Atlantic biogeographical region, with only a small area located in the south-east Galicia integrated in the Mediterranean region. Characterized by an elevation ranging from sea level to ~2100 m in the western mountains and a hilly topography, roughly 36% of Galician municipalities have been declared as less-favoured areas according to the Council Directive 75/268/EEC . The biophysical characteristics and historical uses of the land of Galicia are reflected in a considerable natural capital, recognized by the designation of several Natura 2000 areas, including 16 Special Protection Areas and 59 Special Areas of Conservation . SPAs cover 101,135 ha and SACs 374,435 ha, corresponding to 3.4% and 12.6% of all the area, respectively. For the last half of the twentieth century, Galicia underwent a polarization between intensification of agriculture and forest activities in the best soils and marginalization of remote, vertical grow system mountainous areas, following the trends observed at the EU scale .
The specialization of agriculture targeted mainly dairy production, while in the forest sector focused fast growing species for timber. Thus, despite representing less than 6% of total area in Spain, Galicia currently accounts for about 40% of dairy and 50% of timber production in the country. Utilized agricultural area occupies about 30% of total area of the region. Holdings are mostly small family farms, considerably smaller than the Spanish average . Moreover, there is a large fragmentation of land property , implying that most farmers manage a rather high number of different plots of land. Thus, despite a trend for agricultural intensification along the last decades, the relatively small scale of farming units and the fragmentation of farm holdings in numerous plots of land contribute to the complexity and heterogeneity of the landscape. Together with a relatively high density of linear landscape elements, these aspects contribute to a High Nature Value linked to the occurrence of cultural landscapes in the area . IACS and LPIS data on farm-level management was provided by the regional managing authority . Linked to LPIS, IACS provides spatially-explicit information on the geographic location, area and crops produced and the number and type of livestock for all agricultural plots managed by Galician farmers’ and declared under the CAP payments. For this study, the dataset contained information for the year 2015 about 1,190,714 parcels, declared by 33, 009 farms . To assure statistical confidentiality, all farms in the dataset are identified by a randomly generated code and as no relation to any farm registration codes. Information about the distribution of EU Habitats of Community Interest in the region was derived from data available from the previous work of Ramil-Rego et al. . This data is the most up-to-date data source regarding the diagnosis, description and distribution of habitat types listed in Annex I of the Habitats Directive for the study area. Moreover, it constitutes the source of data for the Galician Natura 2000 sites regional Management Plan , and for the Standard Data Forms of the designated SACs in the region. While detailed habitat mapping has been carried out exclusively for Natura 2000 sites and their surroundings, records for the presence/absence of habitats in a 10 × 10 km UTM grid were available for the whole region from the same project. Here, we considered all terrestrial Annex I habitats listed for the study area , assuming that agricultural practices may have direct and indirect impacts on their occurrence at the landscape level. We used this information to calculate the total number of Annex I habitats and the total number of priority habitats recorded per 10 × 10 km grid cell . The diversity of species of conservation interest was derived from the Biodiversity Data Bank maintained by the Institute of Agrarian Biodiversity and Rural Development of the University of Santiago de Compostela. This data bank is the most updated source regarding species occurrence for Galicia, and includes information about the suitable habitats for each species, according to the methodology proposed by Ramil Rego et al. . Species of conservation interest were selected . Then, and converging with the objectives of our research, we selected only protected species associated with agro-ecosystems . Our final dataset included 119 protected species, from which 8 plants, 1 invertebrate, 11 amphibians, 13 reptiles, 80 birds, and 6 mammals . While unbalanced, the final set of species reflects the uneven number of species of conservation interest listed across the targeted taxonomic groups for the study area .