GIS IN AGRICULTURE Rajesh N L 1 1 Assistant Professor, University of Agricultural Sciences, Raichur Dept. of Soil Science, Agri. College, UAS Raichur) Abstract: Use of GIS in Agriculture has become quintessential in understanding spatial and temporal variability of the factors influencing crop growth. The World Bank funded detailed Land Resources Inventory (LRI) carried out for selected micro-watersheds (MWS) of Karnataka State, India, at 1:8000 scale using Cadastral overlaid Cartosat + LISS IV merged satellite imagery (2.5 m) as a base map. In Karekal MWS, 8 mapping units viz., NGBhB2, NGBhB3, TERhB3, TERhB3S1, TERcC3g2S2, KARmB2go and KARmB2g1 were derived. Based on the limitations in the land, four LCC (Land Capability Classes) were derived viz., IIIewf, IIIewsf, IVef and IVesf. The crop suitability maps for field crops viz., Cotton, Pigeon pea, Ground nut, Sorghum, Pearl millet, Green gram and Chick pea and horticultural crops viz., Mango, Guava and Sapota were derived based on the soil fertility, climatic regime and land quality. These detailed LRI spatial maps generated in ArcGIS environment made possible to overlay cadastral maps on LCC and crop suitability to deliver parcel based information to the farmers. The spatial and temporal variation in the soil nutrient status under RKVY funded precision farming; pest & pathogen incidences at agro-ecological zones were drawn using Kriging under Geo-statistical wizard in ArcGIS. Most of these showed lowest nugget value with exponential model having mean square error of 0.90. The pest incidence prediction maps were overlaid on weather data to understand the pest occurrence pattern. About the Author: Mr. Rajesh N L Post graduate in MSc (Ag) soil science from college of agriculture, Pune, M.P.K.V, Maharastra, in 2003, & Doctorate in Management Studies (IT) from NIM, Mumbai, in 2010. Working as Assistant Professor of Soil Science, at UAS Raichur, Karnataka, India. Has 13+years of experience in the area of Geospatial technologies as a Lead Scientist/PI/Co-PI/Team Leader/Technical adviser. Mr. Rajesh N L has completed several national and international advanced trainings on Geospatial/Geo-statistics techniques. Has secured Excellence in Teaching and Young Scientist awards in the area of RS&GIS for his outstanding contribution in the field GIS in Agriculture. Has authored & Co-authored 25 publications (book chapters, journal articles, proceedings, bulletins, popular press articles). Presently involved in providing GIS solutions to various verticals of Agriculture viz., Soils, Hydrology, Entomology, Pathology, Agronomy etc., both in research and in higher education. In addition, he is Coordinating detailed LRI survey as a Lead Scientist (RS&GIS) under Sujala-III, World Bank Funded Project. Also looking after GIS requirements in Precision Agriculture and e-surveillance of Agricultural Pest (e- SAP) projects as a Co-PI. He is also working as core committee member for various GIS/Geo-portal technical evaluations at Karnataka state govt. level. E mail ID: rajesh.neralikere@gmail.com Contact: +918867372733 Page 1 of 7
Introduction Spatial information is exemplary for assessment of biotic and abiotic factors influencing the crop growth and yield in agriculture and allied areas. The GIS and Geo-statistics offers platforms for describing spatial continuity of many natural phenomena in agriculture research viz., soil, pest & diseases incidences for making estimates in un-sampled locations (Cressie, N. 1993 and Isaaks E H & Srivastava, R. M, 1989), which help in adopting right management practices in right time for maximizing the yield with minimum agricultural inputs. Therefore, an exertion is made to explore GIS in agricultural research and education in the field of soil science, entomology, pathology, watershed development, and in precision agriculture. Detailed LRI for Crop Suitability and Land Capability of Karekal-1 Micro Watershed Using Geospatial Technology The detailed land resource inventory of Karekal-1 micro watershed was carried at 1:8000 scale under World Bank funded Sujala-III project at Shorapur taluk Yadgir district Karnataka state, India. Traversing was done using cadastral map, SOI toposheet and IRS LISS IV (5.8 m) merged Cartosat imagery (2.5 m). Physiographic units were identified and initial legend was prepared by studying soils in representative places. The total four series such as Dhoni, Karekal, Nagarabavi, and Teerth (Fig. 1, eight mapping units viz., NGBhB2, NGBhB3, TERhB3, TERhB3S1, TERcC3g2S2, KARmB2go and KARmB2g1) were generated. Four LCC were derived viz., IIIewf, IIIewsf, IVef and IVesf (Fig. 2). The crop suitability maps for field crops viz., Cotton, Pigeon pea, Ground nut, Sorghum, Pearl millet, Green gram and Chick pea (Fig. 3) and horticultural crops viz., Mango, Guava and Sapota were derived based on the soil fertility, climatic regime and land quality. These detailed LRI spatial maps generated in ArcGIS environment made possible to overlay cadastral maps on LCC and crop suitability to deliver parcel based information to the farmers. Similar works have been taken up in all the State Agricultural and Horticultural universities of Karnataka, covering 11 districts of the state. The different spatial thematic layers of LRI data along with hydrological studies overlaid on cadastral maps helps farmers to understand their land fertility status, constraints and the crop suitability. Further, it will also help farmers to decide appropriate soil and water conservation structures and site-specific land management, crops/cropping systems to get maximum returns with minimum /optimal agricultural inputs. It is envisaged that these MWS-wise detailed LRI data to be uploaded in Digital Library and will be made accessible to the end users, such as farming communities, policy makers and scientific communities through LRI portal with robust GIS server; it will help gather parcel-wise information and make qualitative decisions. Fig. 1, Soil mapping units of Karekal-1 MWS Page 2 of 7
Fig. 2, Land capability classification of soils in Karekal-1 MWS Fig. 3, Land Suitability Map of Karekal - 1 MWS for Cotton, groundnut, Bajra, Redgram, Sorghum, and Green gram Precision Agriculture Today we live in a world with many problems, including growing population, resource shortages and loss of biodiversity (Jack Dangermond, 2007). Managing with-in field variation of soil and crop will increase crop productivity, save time, increase financial returns and benefit the environment (Raj Khosla, 2012). Soil samples were collected at 10m X 10m grids of one-hectare area in Agriculture Research Station, Raichur, Karnataka Page 3 of 7
State, India, to study the soil variability with respect to soil physico-chemical parameters. Variable rate of input applications were made based on the four site year soil analysis values for NPK. Mapping of spatial variability of soil and yield parameters was done using Kriging with exponential model having lowest nugget value in Geostatistical Wizard tool of ArcGIS 10.4. The variability in soil nutrients were observed both in color coded grid cells and as well in the kriged output (Fig. 4). This has helped in adopting soil management practices in individual grid cells and also to derive management zones from the four site years data assessed during 2011-12 to 2014-15. Fig. 4, Spatial variability of Soil Potassium (Grid based and Kriged output) Spatial and Temporal Assessment of Bacterial Leaf Blight (Xanthomonas oryzae pv. Oryzae) of Rice in Sothern India GIS has been used extensively for mapping distributions of disease or specific genotypes of plant pathogens (Nelson et al., 1994). Spatiotemporal change of occurrence of Bacterial Leaf Blight (BLB) in Godavari agroclimatic zone of southern India, at different agro-climatic conditions for two Kharif seasons (2013-2014) was studied. The results revealed that the severity distribution of BLB per cent disease index (PDI) spatially and temporally in Kharif 2013 and Kharif 2014 varied with 65.90% and 86.74%. The GIS based spatial and temporal assessment of BLB severity during Kharif -2013 and Kharif -2014, revealed that BLB has become more severe in major rice growing areas of Godavari zone (Fig. 5) and it has increased by 20.84% incidence in Kharif -2014 when compared to Kharif -2013. This study will help assess the cause for increases in the pathogen incidence when overlaid this spatial distribution maps on weather parameters and biotic & abiotic parameters that influence the crop growth and yield. Page 4 of 7
Fig. 5, Spatial & Temporal variability of BLB pest incidence in Godavari agroclimatic zone Spatio-temporal assessment of Helicoverpa of pigeonpea Spatio-temporal distribution of Helicoverpa armigera, lepidopteran pest of pigeonpea was assessed using GIS technology in Kalburgi districts of North Eastern Karnataka. Pest surveillance data gathered through GPS enabled e-sap (Electronic Surveillance of Agricultural Pests) for site years 2013, 2014 and 2015. A total of 3577 location points of Helicoverpa armigera incidence were collected. The pest incidence location data was imported to ArcGIS 10.4 and distribution maps (Fig. 6) were generated using Kriging with exponential model having lowest nugget value in Geostatistical Wizard tool. The incidence level of target pests was on par with economic threshold level (ETL) during 2013 and 2014 compared to 2015. When the data was looked through spatial analysis, H. armigera attained ETL in Chincholi, Sedam, and Kalaburagi talukas. The pest incidence was correlated with weather parameters viz., rainfall, maximum and minimum temperature, RH and crop stage. The incidence of H. armigera was found to have positive correlation with total rainfall and negative relation with minimum temperature. Coefficient of determination (R 2 ) was 7.9. Using the pest distribution and weather data, risk assessment map (Fig. 7) were developed for target pests across space and time. There was a clear indication of the influence of rainfall on incidence of H. armigera. Using the surveillance data to develop GIS map enable clear and reliable pest distribution maps and risk assessment maps, then providing an opportunity for area wide pest management. Page 5 of 7
Fig. 6, Spatial & temporal variation in Helicoverpa armigera incidence, Kalburgi district, Karnataka, Fig. 7, Risk assessment map on incidence of Helicoverpa armigera overlaid on rainfall distribution, Kalburgi district, Karnataka, during 2013, 2014 and 2015 Conclusion It is a proven fact that GIS has helped in understanding spatial and temporal variability of biotic and a-biotic factors influencing the crop growth and yield. After a thorough ground truthing of these outputs and data validation, it is understood that the GIS has simplified the representation and management of complexity in natural resources. The RS & GIS lab, Agri. College, UAS Raichur, established under World Bank funded Sujala-III project, helping many students to include GIS objectives in various field of agriculture research. This may be as simple as generating a location boundary map overlaid with GPS points of surveyed data/ color coded maps to visualize the variability or even spatial/3d analysis modules in ArcGIS interpolation/hydrology/watershed delineation etc. The spatial and temporal variability of soil, pest & diseases distribution maps overlaid on weather parameters will help to understand the behavior of these biotic and a-biotic factors to take up right management practices in right time. In recent decade, the advent of GIS in all the sectors has made spatial data necessary for decision making. The cadastral level spatial information on detailed land resources, will facilitate comprehensive decision making on crop loss due to flood or drought to compensate farm holdings suitably to the right victims. The policy makers can help farmers to improve their economic status through Page 6 of 7
integrating socio-economic data, land constraints and develop appropriate programs or can converge with state or national level schemes to benefit the farmers. The scientific communities can make use of these data to plan and execute experiments including Precision Agriculture, Integrated Farming System, and integrated watershed development to elevate living standards and enhance livelihood security to farming communities. References 1. Cressie, N. 1993. Geostatistics: A tool for environmental modelers. Pages 414-421 in: Environmental Modeling with GIS. M. F. Goodchild, B. O. Parks, and L. T. Steyaert, eds. Oxford University Press, London. 2. Isaaks, E. H., and Srivastava, R. M. 1989. An Introduction to Applied Geo-statistics. Oxford University Press, London. 3. Jack Dangermond, 2007, ArcNews Online, A Framework for Understanding, Managing, and Improving Our World, GIS The Geographic Approach, (online). http://www.esri.com/news/arcnews/fall07articles/gis-the-geographic-approach.html 4. Nelson, M. R., Felix-Gastelum, R., Orum, T. V., Stowell, L. J., and Myers, D. E. 1994. Geographic information systems and geostatistics in the design and validation of regional plant virus management programs. Phytopathology 84:898-905. 5. Raj Khosla, 2012, Impact, New technologies boost crop yield, save money, time and resources, Colorado State University Extension, (Online). http://extension.colostate.edu/docs/comm/impact/precisionagriculture.pdf Page 7 of 7