Climate Change Modelling, Planning and Policy for Agriculture

Climate change impacts on agriculture have been dealt at several national and international fora wherein it has always been indicated as a vulnerable ecosystem to climate change and reports do indicate that these ecosystems to contribute to the growing CO₂ level in the atmosphere, whilst a few studies do establish negative impact on the productivity of a few crops and also positive impact on crop movement along altitudinal gradient. With projected increase in water requirements, sustaining production in the rainfed areas is a challenge and in a country like India where a major junk of agricultural practices are monsoon-dependent, and has a strong socio-cultural and socio-economic bondages with farms and farming communities. Within the paradox of climate resilience in agriculture, opportunities for adaptation and mitigation strategies have been discussed in this paper with specific reference to rainfed agriculture.

Agriculture in its present form face challenges to meet the growing needs of the Indian population and therefore warrants convergence of policies and technologies to handhold the farmer for increased production, productivity and income. Therefore, diversification has been dealt as a way out to address the above challenge and to provide a kind of safety-net for the Indian farms and farming communities. The changes over the last few decades with respect to crop diversification has been addressed to indicate the potential in the scenario of reduced land availability and also the need for diversifying Indian agricultural systems to make it more remunerative while balancing the ecological principles of conservation and diversity.

In this study, the rainfall of Orissa, a tropical region in eastern India, and the cyclonic disturbances over the Bay of Bengal were analyzed to assess the trends and variations using parametric statistical procedures. This study revealed noticeable changes in the last century (1901–2000) in regard to the rainfall of Orissa and in the cyclonic disturbances over the Bay of Bengal. Test of structural changes and the locally weighted regression technique (LOWESS) indicated that the spatially averaged annual rainfall of Orissa experienced three significant regime shifts: 1,487 mm with standard deviation 167 mm during 1901–1940, 1,412 mm with standard deviation 184 mm during 1940–1969, and 1,354 mm with standard deviation 241 mm during 1970–2000, respectively. The study of structural breaks in rainfall during the period 1901–1940 and 1941–1969 indicated a significant change in both slope and the intercepts. However, structural break analysis during the period 1941–1969 and 1970–2000 indicated a nonsignificant change. The annual depression in the Bay of Bengal also exhibited a structural change with occurrence of an average of 10.7 depressions during 1901–1948 in comparison to 9.6 during 1949–2000.

A 20-year analysis (1990–2009) of rainfall data of Cuttack district of coastal Odisha indicated an average annual rainfall of 1,649.8 mm with a standard deviation of 375.9 mm. The standard deviation of rainfall is higher in the monsoon months, whereas the coefficient of variation is higher in the non-monsoon months. The rainfall data was analyzed to study the monthly and yearly drought of the study area. Out of the 20 years, there were three drought years with the year 1996 being the most severe drought year wherein only an annual rainfall of 797 mm was received. The dry spell and wet spell analysis by the Markov chain model was done, and it was found that there is a high probability of availability of assured water for irrigation water during the 24th to 38th week. The probability analysis of the monthly rainfall data indicated that the two-parameter log-normal distribution was found to be best fit to the rainfall data of January, February, March, and December; Pearson type III distribution for April and November; log-Pearson type III distribution for May, June, August, and October; Gumbel type 1 extremal distribution for the month of July; and normal distribution for the month of September.

Soybean [Glycine max (L.) Merrill] has emerged as one of the major rainy season oilseed cash crops in central India. Despite its phenomenal growth in this agro-climatic zone, the average productivity of soybean has remained more or less at 1 t ha⁻¹ due to several abiotic, biotic and socio-economic factors. The climate change (increase in temperature, CO₂ concentration and rainfall) will affect this rainfed crop in the future. So, proper management practices which include crop management (use of nutrients, planting time and plant population) will play a major role in future productivity in these regions. Simulation models with demonstrated accuracy and reliability provide an alternative method of investigating both short- and long-term agricultural practices with less time requirements and low cost. They have been evaluated and used as a research tool to study risks associated with various management strategies and to assist in decision-making. Hence, the present study aims at using the APSIM model in the decision-making process to evaluate the impact of climate change on soybean yield.

For the simulation study, the optimum date of sowing was chosen based on the literature available for this region. A well-calibrated and validated APSIM model was used for a long-term simulation study on the impact of rainfall pattern on soybean yield. The long-term prediction revealed that there was an interannual variation in soybean yield due to the variation in rainfall pattern. The distribution of rainfall rather than the amount during the soybean growing season is important for soybean yield. There was a significant decrease in soybean yield (as high as 96 %) when the rainfall receded during the initiation of flowering to maximum pod stage. The yield reduction was 56 % when a drought spell of around 2 weeks occurs during mid-vegetative stage. There was a significant decrease in yield (37 %) from the maximum when the drought spell occurs at some parts of the growing season. The validated APSIM model was also used to simulate the impact of climate change on soybean production in central India. The projected temperature scenarios for the Indian subcontinent as reported by IPCC have been used in the present study. There was a decrease (ranging between 20 and 35 %) in soybean yield when the effect of the rise in surface air temperature during soybean growing season was considered. The simulation results obtained on the mitigatory option for reducing the negative impacts of temperature increases indicate that delaying the sowing dates would be favourable for increased soybean yields for this region. This will help in recommending a better alternative management options to improve the productivity of soybean in the region.

The simulation models, with its complete ability, the soil-plant-atmospheric system, offer an ideal tool to analyze the response of wheat to the changing climatic conditions. Crop simulation model not only saves the accurate time but also the huge cost of experimentation. The CERES-Wheat vs. 4.5 crop growth simulation models was calibrated and evaluated for the different agroclimatic conditions of the wheat-growing area of India. In the present study, efforts are made to estimate the wheat yield for the years 2009–2010 using CERES-Wheat crop growth simulation model embedded in the DSSAT v4.5 software. Simulations were made under irrigated condition for the 15 wheat-growing locations representing different agroclimatic zones of the country. Daily weather data on maximum and minimum temperatures, rainfall, and radiation were used for 15 locations for the two winter seasons, current season (2009–2010) and previous season (2008–2009). Solar radiation required by the crop model was calculated from bright sunshine hours. Two dates of sowing representing normal (north India, 15 November; Uttar Pradesh, 10 December, and East India, 1 December) and late sown (north India, 25 November; Uttar Pradesh, 25 December, and East India, 15 December) conditions for each zone were taken up for the study. The genetic coefficient required for running the CERES-Wheat model was derived for the commonly grown cultivars, i.e., PBW-343, RAJ-3765, Malviya-234, HUW-234, and C-306, at different locations.

Model simulation results indicate that wheat crop yield in Uttar Pradesh during the crop season (2009–2010) is slightly affected due to the rise in temperature encountered at the grain-filling stage in the month of March. The simulated average yield for Uttar Pradesh was 47.8 and 46.8 q/ha for the years 2008–2009 and 2009–2010, respectively, under normal sown condition. Similarly in the case of East India, the simulated average yield is 34.7–42.3 q/ha and 52.2 and 54.5 q/ha for Northwest India. Under normal sown condition, wheat yield prediction during 2009–2010 is ~1 % less compared to 2008–2009 in Uttar Pradesh while there was an increase in grain yield by ~4 % in Northwest India and ~18 % in East India, respectively.

In case of a late sown condition, the crop model shows reduction in grain yield in Northwest India and Uttar Pradesh except for East India. During March 2010, maximum and minimum temperatures remained above normal almost throughout the month over most parts of the country. Maximum temperatures were above normal by 4–8 °C over northwest, east, central, and adjoining peninsular India.

Simulated average yield and deviation percentage for Northwest India and Uttar Pradesh were 53.6 and 51.2 q/ha (~4 %) and 50.2 and 38.3 q/ha (~23 %) for the years 2008–2009 and 2009–2010, respectively. Wheat yield production realized during 2009–2010 is higher compared to 2008–2009 in east India that shows 32.1 and 35.4 q/ha (~8 %). The objective of this study is to analyze the temperature trend so as to help the government agencies to design and adopt a suitable corrective measure of agricultural system.

To take full advantage of site-specific variable rate technology (VRT) systems, highly accurate digital mapping of weed infestations within fields via scouting, GPS, GIS and remote sensing technologies will be necessary. When combined, these tools can increase weed control efficiency and reduce herbicide use and residues, thereby avoiding excess applications that lead to increased costs, potential herbicide resistance in the field and runoff into the environment. Keeping this in view, a field experiment was conducted at the Research Farm, Department of Agronomy, Punjab Agricultural University, Ludhiana, Punjab, for 2 years to study the multispectral remote sensing to distinguish the little seed canary grass (Phalaris minor) from wheat crop under field conditions for environmental sustainability and precision weed management. The experimental site during both the seasons were sandy loam in texture, with normal soil reaction and electrical conductivity, low in organic carbon and available nitrogen and medium in available phosphorus and potassium. The experiment consisted of five treatments, viz, T₁,_, control (weedy check); T₂, half of the recommended dose of herbicide for partial control of Phalaris minor; T₃, recommended dose of herbicide to obtain economic threshold level to control Phalaris minor; T₄, manual weeding (partial), done after a month of sowing of crop; and T₅, weed free (manual). The treatments T₃ (recommend dose of herbicide) and T₅ (weed free, manual) being at par with one another recorded highest plant height, dry matter accumulation and number of tillers per plant by wheat at all observational dates during both the years in experiments, whereas minimum plant height, dry matter accumulation and number of tillers per plant were recorded in control treatment (T₁). Reduction in dry matter production, number of tillers as well as effective number of tillers and ultimately yield of wheat are mainly attributed to the reduction in the number of effective tillers, lesser number of grains per spike and lesser 1000-grain weight. The weed-free treatments (T₃ and T₅) had lower red reflectance percentage as compared to other weed control treatments. The control treatment recorded the highest red reflectance. On the other hand, the two weed-free treatments T₃ and T₅ had higher IR reflectance percentage as compared to other three weed control treatments, and the lowest IR reflectance was recorded under control treatment. Highest RR and NDVI values were obtained in treatments T₃ and T₅ where there was no competition between wheat and weeds, and control treatment had the lowest RR value amongst all the treatments during both years. Differences in RR between these three treatments are mainly due to dark green colour of wheat, more leaf area index (LAI) and more biomass of wheat as compared to Phalaris minor. The RR value increases in the early stages of crop growth which is maximum at maximum crop canopy cover and after that decreases as the leaves senesce. The highest RR values were obtained at 95 days after sowing almost in all the treatments. It is feasible to distinguish pure wheat from weeds just 34 days after sowing, but amongst different weed control treatments, i.e. pure Phalaris minor plot and less/partial Phalaris minor weeds 52 days after sowing amongst themselves, and they remain distinguished up to 107 days after sowing based on their NDVI values. After 52 DAS, the differences in the NDVI of different weed control treatments were very clear. So, from such type of information, we can discriminate/define the areas which are heavily or partially infested with weeds so that timely weed control measures can be taken which can help the farmers in preventing yield losses due to weeds.

The study was carried out to developed stochastic model for weekly temperature, humidity and precipitation in Solapur (Latitude 17°40′N, Longitude 75°54′E and altitude 483.50 m amsl) station of western part of Maharashtra, India. In the present study, 42 years data (1969–2010) of daily temperature, relative humidity and precipitation of Solapur station have been used for time series analysis. Weekly mean temperature, relative humidity and monthly precipitation values were used to fit the ARIMA class of models for different orders. ARIMA models of first and second orders were selected based on autocorrelation function (ACF) and partial autocorrelation function (PACF) of the time series. The parameters of the selected models were obtained with the help of maximum likelihood method. The diagnostic checking of the selected models was then performed with the help of three tests (i.e. standard error, ACF and PACF of residuals and AIC) to know the adequacy of the selected models. The ARIMA models that passed the adequacy test were selected for forecasting. One year ahead forecast (i.e. for 2010) of temperature, relative humidity and precipitation values were obtained with the help of these selected models and compared with the values of temperature, relative humidity and precipitation obtained from the climatological data of 2010 by root mean square error (RMSE). According to the Seasonal ARIMA model, ACF, PACF and evaluation of all eventual parameters, the results from analysis show that the model fitted is weekly temperature: ARIMA (111) (011) ₅₂ , weekly relative humidity: ARIMA (111) (111) ₅₂ and monthly precipitation: ARIMA (211)(201) ₁₂ and hence are the best stochastic model for generating and forecasting of weekly temperature, relative humidity and monthly precipitation values for Solapur station, Maharashtra, India.

The studies reveal that if sufficient spread and depth of data are used in model building, frequent updating of model may not be necessary. The study also showed the utility of forecast of climatic parameter values in estimating the irrigation quantity and monitoring the insect pest and disease 1 year ahead for pomegranate orchards. It is concluded that seasonal ARIMA model is a viable tool which can successfully be used for generation and forecasting of climatic parameters having inbuilt seasonal patterns.

Apple is the predominant horticulture crop of Himachal Pradesh and Jammu and Kashmir states in India. Efforts are in progress to further strengthen this crop by bringing more areas under cultivation and improving the condition of the existing orchards. However, future changes in the climatic parameters projected under the global climate change scenario will have significant impact on the apple orchard viability. This is mainly due to its sensitivity to availability of chilling units. Temperate fruits like apple have a specific chilling unit requirement for fruit set and quality of fruit. In the Indian context, the chilling requirement is related to the elevation range of the orchards. This study analyses the current distribution pattern of the apple orchards in relation to elevation ranges and simulates the change under the climate change scenario. Remote sensing data of IRS-P6 LISS-III and AWiFS sensor was used to map the orchards. Digital elevation model (DEM) was used to generate the elevation, slope and aspect in spatial domain. Ancillary data district/state boundary, weather data, soil and drainage were integrated using geospatial technique. Terrain analysis showed that the orchards in Jammu and Kashmir were distributed in the elevation range of 1,600–2,100 m. The equal proportion of orchards was observed in the elevation range of 1,600 − 1,800 m as well as 1,800–2,000 m. In case of Himachal Pradesh (Shimla, Kullu, Mandi), the orchards are distributed from 1,600 to 3,000 m.

To predict the suitable elevation of apple growth under the climate change scenario, a modelling method known as GARP (Genetic Algorithm for Rule-Set Production) was used. It is a genetic algorithm that creates ecological niche models for species distribution from presence-only occurrence data. The model gives final solution as environmental conditions under which the species should be able to maintain populations. The simulation showed significant upward shift of the existing belt to higher elevation. The paper describes the results in detail. The use of geospatial technique and simulation model enhances the scope for preparing a road map to plan mitigation measures to combat climate change impact on the vast tracts of apple and stone fruit belt in India.

Geographic information system (GIS) is used for location-based analysis and decision-making. GIS professionals in agriculture typically employ it to examine selected geographic datasets in detail, viz., land use and land cover (LULC), soil data, weather parameters with crop data such as variety distribution, and sowing date which are combined for the comprehensive study and analysis of spatial problems. Web GIS is a platform for distributing and mapping GIS data and services on the Web. It can be used to distribute geographic data to many concurrent users and allow them to do location-based analyses. Open-source GIS software and map server are rescued by the users from high-cost geospatial software and provide better functional environment through editing and customization of application programming interfaces (API).

A rubber dam (flexible check dam) is an inflatable structure build across a stream used for water conservation, flood control, and regulating flow of water in the stream. When it is inflated, it serves as a check dam/weir, and when it is deflated, it functions as a flood mitigation device and sediment flushing. Generally, most of the check dams in watersheds are made of concrete, steel, stone, soil, or vegetation. The use of rubber as a construction material is a technological innovation in materials application. At the same time, the check dams are rigid one and they cannot allow more water to flow over it at times of heavy flood/runoff or store sufficient runoff to conserve the rainfall at lean season for use by farmer for different rabi crops like pulses, oilseeds, and vegetables. To give more flexibility in release and control of water flow across the streams, research efforts were made at Directorate of Water Management, Bhubaneswar, in collaboration with Indian Rubber Manufacturers Research Association, Central Institute for Research on Cotton Technology, and Kusumgar Corporate Private Limited, Mumbai, to design, fabricate, and install rubber sheets instead of cement material for check dams and to study their impact on crop performance. Five rubber dams were installed as different hydraulic structures for various uses in watersheds at different locations of Khurda district, Odisha, i.e., Mendhasal, Baghamari, Badapokharia, and Chandeswar with innovative manufacturing, fabrication, and installation technology. This is the first indigenous rubber dam in our country. The installation of rubber dams in watersheds has increased the production and productivity of rice crop, helped in taking second crop thus increasing cropping intensity and net profit of the farmers.

The impact of climatic variability on crop production was studied in the Mahanadi delta area where upland, midland and lowland sites were selected covering Dhenkanal, Kendrapara, Nayagarh, Cuttack and Puri district. The monthly run-off (10⁶ m³) data of 19 gauging stations adequately representing the sub-basins of the Mahanadi River basin with a command area of 1,41,589 km² during the period 1972–2004 was analysed to investigate the run-off variability and trends of the basin. Analysis of hydroclimatic parameters using advanced statistical tools revealed that due to alteration in the hydrological cycle the severity of droughts and intensity of floods increased and the quantity of the available water resources also gets affected. The average annual result run-off during the period 1990–2004 exhibits an increased run-off, in comparison to the previous subseries 1972–1989 for all the stations. However, this increased run-off is also associated with an increase in the variability (standard deviation), suggesting that the wet period is more prone to uncertainty in comparison to the corresponding dry period. The rainfall status of the experimental sites on the Mahanadi delta showed occurrence of frequent drought and flood in the last 25 years (Table 1). Uneven distribution of rainfall affected the crop yield adversely. Increase in rainfall in May and September and severe drought in November and December affected the paddy yield. It has been observed that in spite of canal irrigation sources, water is unavailable during the critical stages of crop growth and moisture stress condition. To mitigate the vagaries caused by climate change, water harvesting structures for assured irrigation, microirrigation techniques to increase the water use efficiency, crop diversification and multiple use of water to improve water productivity are some of the technological interventions for resilience and sustainable identified production.

Indian agriculture, for long, was characterized as subsistence farming, carried out by small and marginal farmers adopting primitive techniques and thus incurring low yields. Importantly, Indian agriculture has also been negotiating the difficulties in food grain production. A series of changes in agricultural technology introduced in the 1960s, referred to as green revolution technology, helped India overcome the problems of food grain production. Green revolution technology not only offered new, high yielding seeds but also fertilizers which replaced organic manures and nitrogen-fixing crops, pesticides replacing biological, cultural, and mechanical methods for controlling pests. With the introduction of green revolution, the structure of Indian agriculture also underwent changes, bringing agriculture into the fold of capitalism.

Without having access to credible advice, the farmers have been adopting unscientific cultivation practices mostly on the advice of the dealers of pesticides and seeds. As a result, farmers have been relying on wrong, untimely, unnecessary information leading to losses. The net result is very devastating for farmers, environment, and public health. For farmers, they are facing severe crisis due to reduced crop output or crop failure. The task of making agriculture sustainable is of no less importance to India than to any other country. Emerging technologies need to be profitable and ecologically sound. Half of India’s total land area is estimated to suffer from problems of degradation on account of unsustainable practices like excess use of chemical fertilizers, pesticides, herbicides, etc.

Sustainability agriculture in India is achievable when the farmers are supplied with adequate, appropriate, accurate and timely information. Agricultural information has the key role in facilitating the participation of people relating to sustainable development. Several attempts have been made in the dissemination of agricultural information to the farmers using the advances in information and communication technologies (ICTs), one such attempt is e-Sagu, which uses ICTs in providing continuous, relevant, and latest technological information to farmers through computer and the Internet in Andhra Pradesh.

This study analyses vegetation carbon pools and fluxes and soil carbon storage in grassland systems on sodic soils in northern India. The grassland systems on highly sodic soils show low species diversity and single-species dominance. The plant species composition and soil conditions influence above-ground and belowground carbon pools and fluxes in different grassland systems along a range of soil pH from 8.0 to 10.2. The soil organic carbon content is low ranging from 3.42 to 0.51 g kg⁻¹ across soil depths. The carbon pool (Mg C ha⁻¹) in the primary producer compartment of the grassland ecosystems at Bichian was 4.945–1.721 above-ground biomass and 4.336–1.40 belowground biomass. The carbon flux through total net primary productivity ranged from 0.954 to 0.375 Mg C ha⁻¹ year⁻¹. The organic carbon storage (up to 1-m soil depth) in soils of the natural grassland ecosystems at Bichian was 24.713–16.649 Mg C ha⁻¹ over a period of 15 years of vegetation protection. By integrating trees with the naturally occurring grassland systems on highly sodic soils at Bichian, the soil organic carbon content increased by 15–57 %. After long-term protection of grassland vegetation on a sodic soil at Karnal, the soil carbon pool in 0–30-cm soil depth was 6.683 Mg C ha⁻¹(year 1982) and 13.91 Mg C ha⁻¹ (year 2006). The microaggregates (250, 53 and <53 μm) formed a large fraction of soil aggregates and protected most of the soil organic carbon. In the biologically reclaimed sodic soil at Karnal, the total carbon storage in soil (SOC + SIC) up to 1-m soil depth was 89.511 Mg C ha⁻¹. Thus, the protection of native grassland vegetation on sodic soils has the potential for carbon sequestration by increasing plant biomass production and improving soil organic matter. Implementing practices to build up soil carbon stocks in grasslands could lead to considerable mitigation, adaptation and development benefits.

The paper focuses on three important dimensions of climate resilient agriculture. (1) The various missions launched by the Government of India have set the context to mutually reinforce climate resilient agriculture. At this juncture, it is important to prepare the farming community across the country to understand the benefits of preventive practices including conservation and help implement appropriate measures. A framework for capacity building of communities is presented duly considering the various initiatives launched by the State Agriculture Universities and the Department of Agriculture. This framework clearly states the synergies across programmes and helps avoid duplication of efforts. (2) It is also well known that farming community across the country has periodically shown that it is feasible to develop and implement locally relevant and innovative nutrient, bio resources and soil and water conservation strategies aligned with the goals of the missions. It is however important to sustain these initiatives to upscale them and help adapt them suitably for the benefit of a larger number of communities. It is equally important to define policies and plans to foster them far beyond the tenure of State/national initiatives so that positive results can be maintained. The architecture of some policies to meet this objective is presented for the consideration of the national missions. Communities and their leadership therefore are important in this context. Some institutional mechanisms to involve elected and non-elected leaders are also indicated. (3) The framework for capacity building and the architecture proposed highlight the integrated benefit of agriculture to tackle impacts of climate change. The photosynthetic ability of crops and associated vegetation has to be recognized and further strengthened by recruiting larger tracts of land. This will also expand livelihood opportunities and meet emerging food and nutritional security related challenges. These are critical aspects of sustainability and have to be addressed on a priority basis to complement the national missions through a bottom-up intervention. The spread and depth of specific and cross-cutting interventions are discussed in the paper.

This paper has examined the impact of change in climate variables on the productivity of wheat and rice, with the help of agricultural dataset, spanning 1971–2005, for agricultural yield at the district level. The expected impact of productivity of wheat and rice due to climate variables has been turned to declined wheat production and increased rice production in some regions, while the magnitude of impacts is varied. Any change in climate variables directly affected inputs such as water for irrigation, amounts of solar radiation that affect plant growth, as well as the prevalence of pests. The study finds that impact of climate change is not identical; it varied in all the geographical regions because agricultural activities in India mainly depend on climate variables: monsoon, rainfalls, temperature, growing degree days, etc. Any changes in these variables projected to have adverse effects on agricultural productivity, water resources, coastal ecosystems, and biodiversity.

Climate change is the greatest challenge before the global society impacting the ecology, economy, and society in several ways. Rajasthan has reason to be concerned about the impact of climate change as its large population depends upon climate-sensitive sectors like agriculture and forestry for livelihood. Rajasthan shows a significant warming of 0.5 °C which is comparable to the global mean trend of 0.3 °C and all India mean of 0.4 °C per 100 years. The climate of Rajasthan has exhibited a continuing trend towards desiccation, particularly after the severe drought of 1987. For example, in February and March, 2006 and 2008, there was high-temperature stress, and there was heavy frost in January, 2007 and 2010. In December and January, 2008–2009, there was unusual rise in temperature, and the yields of wheat and mustard were adversely affected. Recently, in kharif, 2009, there was severe drought in most of the areas causing large-scale crop failure in western Rajasthan. Impact of climate change on agriculture is visualized in terms of increased problem of water stress in major kharif crops; heat stress in wheat, barley, mustard, and gram; occurrence of frost in mustard, gram, and pea; virus- and root-related diseases; sucking pests, mites, leaf minor, and gram pod borer in selected crops; emergence of new weeds; etc. These are creating suboptimal and stressful environment for agricultural crop production. Green agriculture, water-saving agriculture including water harvesting and land treatment for in situ moisture conservation, adoption of integrated farming system, use of crop simulation models, emergency response system, and crop and weather insurance are some of the mitigation options available for ameliorating adverse impact of climate change for sustainable agriculture.

The climate change issue is part of the larger challenge of sustainable development and one of the most important global environmental challenges facing humanity which go far beyond its effect on the environment. In order to understand the climatic change happening in the different agro-climatic regions of Tamil Nadu and to develop strategies to mitigate climatic stress and to optimise productivity monthly, Maximum and Minimum Temperature and Relative Humidity Data during the period 1955–2005 were obtained from the Indian Meteorological Department, Pune. From the basic temperature data, mean maximum, mean minimum and Temperature Humidity Index was computed for each month for the seven agro-climatic regions of Tamil Nadu. Mean maximum temperature was observed at the month of May, and Cauvery Delta zone showed maximum temperature of 38.22 ± 1.33 °C with significant difference of P < 0.01 between the regions. Mean minimum temperature was observed at the month of January, and hilly zone showed minimum temperature of 5.59 ± 1.31 °C with a significant difference of P < 0.01 between the regions. The long-term mean and annual compounded growth rates of T_max, T_min and THI were worked out on the basis of the representative areas selected for the study. The annual growth rate of maximum and minimum temperatures showed different patterns for different agro-climatic zones in the study area. The T_max showed an increase in all the agro-climatic zones except in north-western zone and southern zone. T_min also showed a positive growth pattern in all agro-climatic zones except in north-western zone and southern zone. The annual growth rate of THI showed different patterns for different agro-climatic zones. Five agro-climatic zones, viz., north-eastern zone, western zone and hilly zones, Cauvery Delta zone and high rainfall zone, were showing a positive annual compounded growth rate for both morning and evening THI. In north-western zone the growth in THI was limited to morning and the evening THI showed a negative growth. Southern zone showed a negative annual compounded growth rate for both morning and evening THI.

The climate, land use and land cover show the changing behaviour over a period of time. These factors influence the water resources. However, it is necessary to know their impact on the quantification of surface water and groundwater resources to enable to plan and manage these resources appropriately. Therefore, it is necessary to build the water resource models for the catchment area. Water resource modelling of an entire catchment is a complex phenomenon but of a great importance for obtaining a better quantitative understanding of water uses and arriving at important water management decisions. Therefore, the use of water resource models such as MIKE SHE in conjunction with remote sensing and GIS techniques is helpful in planning and management of land and water resources and managing water resources. In this study, it is proposed to use the surface water-groundwater resource model MIKE SHE for the estimation of groundwater resources, and the methodology was developed for this purpose.

The MIKE SHE model was calibrated for a small catchment using remote sensing data and GIS. Mapping of the study area was carried out with the help of Geocoded (1:50000) IRS-1C/1D LISS-III and PAN images. The input data that was used for preprocessing and set-up preparation of the MIKE SHE model were catchment boundary map, topography, land use/land cover map, soil distribution map, climatological data and crop characteristic (LAI, root depth) and hydraulic properties of soils in the catchment. Roughness coefficient of overland and channel flow, initial water depth, maximum profile water balance error and storing time steps were considered as calibration parameters. The roughness coefficient was varied in the range of 5 to 15, initial water depth in 0 to 0.1 mm, maximum profile water balance error in 0.0001 to 0.02 and storing time steps in 120 to 720 h, respectively. The groundwater fluctuations were simulated for 31 well locations for each combination of calibration parameters. The RMSE between the simulated and actual groundwater fluctuations was calculated, and the set of calibration parameters that yielded the lowest values of RMSE (3.83) was selected for the calibrated MIKE SHE model. These were 10, 0.01 mm, 0.0001 and 360 h for roughness coefficient, initial water depth, maximum profile water balance error and storing time steps, respectively.

Dhor-nani catchment which is a small part of the upper Godavari river basin in Ahmednagar district of Maharashtra state (upstream of Jaikwadi dam) was used to evaluate the effect of changes in land use/land cover and climate on surface water and groundwater resources. Two model set-ups of calibrated MIKE SHE were prepared for the years 1998 and 2005, respectively. The remote sensing images of IRS 1-D (Wifs) for the year 1998 (January) and IRS 1-D (A-Wifs) for the year 2005 (November), climate and other data on various aspects of the catchment – namely, topography, soil distribution and cropping pattern of the respective years, i.e. 1998 and 2005 – were used.

The results of Dhor-nani catchment for the years 1998 and 2005 for influence of change in land use/land cover and climate on water resources indicate that groundwater levels and overland water depths were influenced by change in land use/land cover and climate. The groundwater levels were shallower in the year 1998 compared to 2005, and the depth of overland water increased in 2005 as compared in the year 1998. This may be due to increased values of rainfall (climate) and the changes in land use/land cover between 2 years.

In recent times, several studies around the globe have indicated that climate change is likely to significantly impact freshwater resources availability. In India, demand for water has already increased manifold over the years due to urbanization, agricultural use, increasing population, rapid industrialization, and economic development. At present, the change in cropping and land-use pattern, overexploitation of water, storage, and change in irrigation and drainage are modifying the hydrological cycle in many climate regions and river basins of India. An assessment of the availability of water resources in the context of future national requirements and expected impacts of climate change and its variability is critical for relevant national and regional long-term development strategies and sustainable development. This article examines the potential for sustainable development of surface water and groundwater resources within the constraints imposed by climate change and envisages future research needs of relevance to India.

Climate change has been recognized as a potential threat to livelihood of the poor farmers in the marginal agricultural productive environment especially in the semiarid tropics of India. The impacts may vary spatially, and the rural poor are more challenged of its impacts. Initiatives at national level are underway to address the consequences especially rural and agriculture. The research initiative coordinated by the International Crops Research Institute for the semiarid tropics (ICRISAT) tracked the climate change impacts, adaptation strategies, and constraints at the households’ level through a rigorous quantitative and qualitative analysis in the semiarid tropics of India. This explorative exercise identified challenges and opportunities towards climate resilience through recommendations and policy directive for action. This chapter comprehends the policy needs that emerged from the regional study in identifying the impacts and constraints to effective adaptation by climate change. This evidence-based indicative policy stresses the need to channelize resources effectively in enhancing the grassroot-level resilience to climate change.