Do rainfed production systems have lower environmental impact over irrigated production systems?: On -farm mitigation strategies.

The intensive agriculture practices improved the crop productivity but escalated energy inputs (EI) and carbon foot print (CF) which contributes to global warming. Hence designing productive, profitable crop management practices under different production systems with low environmental impact (EI and CF) is the need of the hour. To identify the practices, quantification of baseline emissions and the major sources of emissions are required. Indian agriculture has diversified crops and production systems but there is dearth of information on both EI and CF of these production systems and crops. Hence the present study was an attempt to find hot spots and identify suitable strategies with high productivity, energy use efficiency (EUE) and carbon use efficiency (CUE). Energy and carbon balance of castor, cotton, chickpea, groundnut, maize, rice (both rainfed and irrigated), wheat, sugarcane (only irrigated), pigeon pea, soybean, sorghum, pearl millet (only rainfed) in different production systems was assessed. Field specific data on different crop management practices as well as grain and biomass yields were considered. Rainfed production systems had lower EI and CF than irrigated system. The nonrenewable sources of energy like fertilizer (64 %), irrigation (78 %), diesel fuel (75 %) and electricity (67 %) are the major source of energy input. Rainfed crops recorded higher CUE over irrigated condition. Adoption of technologies like efficient irrigation strategies (micro irrigation), enhancing fertilizer use efficiency (site specific nutrient management or slow release fertilizer), conservation agriculture (conservation or reduced tillage) rice cultivation methods (SRI or Direct seeded rice) were the mitigation strategies. These results will help policy makers and stake holders in adoption of suitable strategies for sustainable intensification.

Pest scenario of Helicoverpa armigera (Hub.) on pigeonpea during future climate change periods under RCP based projections in India.

Gram pod borer, Helicoverpa armigera (Hub.) is the major insect pest of pigeonpea and prediction of number of generations (no. of gen.) and generation time (gen. time) using growing degree days (GDD) approach during three future climate change periods viz., Near (NP), Distant (DP) and Far Distant (FDP) periods at eleven major pigeonpea growing locations of India was attempted. Multi-model ensemble of Maximum (Tmax) and Minimum (Tmin) temperature data of four Representative Concentration Pathways viz., RCP 2.6, 4.5, 6.0 and 8.5 of Coupled Model Inter comparison Project 5 (CMIP5) models was adopted here. The increase in projected Tmax and Tmin are significant during 3 climate change periods (CCPs) viz., the NP, DP and FDP over base line (BL) period under four RCP scenarios at all locations and would be higher (4.7-5.1 °C) in RCP 8.5 and in FDP. More number of annual (10-17) and seasonal (5-8) gens. are expected to occur with greater percent increase in FDP (8 to 38%) over base line followed by DP (7 to 22%) and NP (5to 10%) periods with shortened annual gen. time (4 to 27%) across 4 RCPs. The reduction of crop duration was substantial in short, medium and long duration pigeonpeas at all locations across 4 RCPs and 3 CCPs. The seasonal no.of gen. is expected to increase (5 to 35%) with shortened gen. time (4 to 26%) even with reduced crop duration across DP and FDP climate periods of 6.0 and 8.5 RCPs in LD pigeonpea. More no. of gen. of H. armigera with reduced gen. time are expected to occur at Ludhiana, Coimbatore, Mohanpur, Warangal and Akola locations over BL period in 4 RCPs when normal duration of pigeonpeas is considered. Geographical location (66 to 72%), climate period (11 to 19%), RCPs (5-7%) and their interaction (0.04-1%) is vital and together explained more than 90% of the total variation in future pest scenario. The findings indicate that the incidence of H. armigera would be higher on pigeonpea during ensuing CCPs in India under global warming context.

Soil bacterial community structure and functioning in a long-term conservation agriculture experiment under semi-arid rainfed production system.

Soil microbial communities are important drivers of biogeochemical cycling of nutrients, organic matter decomposition, soil organic carbon, and Greenhouse gas emissions (GHGs: CO2, N2O, and CH4) and are influenced by crop and soil management practices. The knowledge on the impact of conservation agriculture (CA) on soil bacterial diversity, nutrient availability, and GHG emissions in semi-arid regions under rainfed conditions is vital to develop sustainable agricultural practices, but such information has not been systemically documented. Hence, studies were conducted for 10 years in rainfed pigeonpea (Cajanus cajan L.)-castor bean (Ricinus communis L.) cropping system under semi-arid conditions to assess the effects of tillage and crop residue levels on the soil bacterial diversity, enzyme activity (Dehydrogenase, urease, acid phosphatase, and alkaline phosphatase), GHG emissions, and soil available nutrients (Nitrogen, phosphorus, and potassium). Sequencing of soil DNA through Illumina HiSeq-based 16S rRNA amplicon sequencing technology has revealed that bacterial community responded to both tillage and residue levels. The relative abundance of Actinobacteria in terms of Operational Taxonomic Unit (OTUs) at phyla, class as well as genera level was higher in CA (NTR1: No Tillage + 10 cm anchored residue and NTR2 NT + 30 cm anchored residue) over CT (conventional tillage without crop residues). CA resulted in higher enzyme activities (dehydrogenase, urease, acid phosphatase, and alkaline phosphatase) and reduction in GHG emissions over CT. CA recorded 34% higher and 3% lower OC, as compared to CT, and CTR1, respectively. CA recorded 10, 34, and 26% higher available nitrogen, phosphorus, and potassium over CT and CTR1, respectively. NTR1 recorded 25 and 38% lower N2O emissions as compared to CTR1 and CTR2, respectively. Whereas only NT recorded 12% higher N2O emissions as compared to CT. Overall, the results of the study indicate that CA improves the relative abundance of soil bacterial communities, nutrient availability, and enzyme activities, and may help to contribute to the mitigation of climate change, and sustainability in rainfed areas.

Evaluating area-specific adaptation strategies for rainfed maize under future climates of India.

This study investigates the spatio-temporal changes in maize yield under projected climate and identified the potential adaptation measures to reduce the negative impact. Future climate data derived from 30 general circulation models were used to assess the impact of future climate on yield in 16 major maize growing districts of India. DSSAT model was used to simulate maize yield and evaluate adaptation strategies during mid (2040-69) and end-centuries (2070-99) under RCP 4.5 and 8.5. Genetic coefficients were calibrated and validated for each of the study locations. The projected climate indicated a substantial increase in mean seasonal maximum (0.9-6.0 °C) and minimum temperatures (1.1-6.1 °C) in the future (the range denotes the lowest and highest change during all the four future scenarios). Without adaptation strategies, climate change could reduce maize yield in the range of 16% (Tumkur) to 46% (Jalandhar) under RCP 4.5 and 21% (Tumkur) to 80% (Jalandhar) under RCP 8.5. Only at Dharwad, the yield could remain slightly higher or the same compared to the baseline period (1980-2009). Six adaptation strategies were evaluated (delayed sowing, increase in fertilizer dose, supplemental irrigation, and their combinations) in which a combination of those was found to be effective in majority of the districts. District-specific adaptation strategies were identified for each of the future scenarios. The findings of this study will enable in planning adaptation strategies to minimize the negative impact of projected climate in major maize growing districts of India.

Model and scenario variations in predicted number of generations of Spodoptera litura Fab. on peanut during future climate change scenario.

The present study features the estimation of number of generations of tobacco caterpillar, Spodoptera litura. Fab. on peanut crop at six locations in India using MarkSim, which provides General Circulation Model (GCM) of future data on daily maximum (T.max), minimum (T.min) air temperatures from six models viz., BCCR-BCM2.0, CNRM-CM3, CSIRO-Mk3.5, ECHams5, INCM-CM3.0 and MIROC3.2 along with an ensemble of the six from three emission scenarios (A2, A1B and B1). This data was used to predict the future pest scenarios following the growing degree days approach in four different climate periods viz., Baseline-1975, Near future (NF) -2020, Distant future (DF)-2050 and Very Distant future (VDF)-2080. It is predicted that more generations would occur during the three future climate periods with significant variation among scenarios and models. Among the seven models, 1-2 additional generations were predicted during DF and VDF due to higher future temperatures in CNRM-CM3, ECHams5 & CSIRO-Mk3.5 models. The temperature projections of these models indicated that the generation time would decrease by 18-22% over baseline. Analysis of variance (ANOVA) was used to partition the variation in the predicted number of generations and generation time of S. litura on peanut during crop season. Geographical location explained 34% of the total variation in number of generations, followed by time period (26%), model (1.74%) and scenario (0.74%). The remaining 14% of the variation was explained by interactions. Increased number of generations and reduction of generation time across the six peanut growing locations of India suggest that the incidence of S. litura may increase due to projected increase in temperatures in future climate change periods.