Agricultural Water Management

Transcription

1 Agricultural Water Management 202 (2018) Contents lists available at ScienceDirect Agricultural Water Management journal homepage: Performance of portfolios of climate smart agriculture practices in a ricewheat system of western Indo-Gangetic plains T S.K. Kakraliya a, H.S. Jat b, Ishwar Singh a, Tek B. Sapkota b, Love K. Singh c, Jhabar M. Sutaliya b, Parbodh C. Sharma d, R.D. Jat b, Meena Choudhary e, Santiago Lopez-Ridaura f, M.L. Jat b, a Chaudhary Charan Singh Haryana Agriculture University (CCSHAU), Hisar, India b International Maize and Wheat Improvement Centre (CIMMYT), NASC Complex, New Delhi, India c Borlaug Institute for South Asia (BISA), CIMMYT, Ludhiana, Punjab, India d ICAR-Central Soil Salinity Research Institute (CSSRI), Karnal, India e SKN Agriculture University, Jobner, Jaipur, Rajasthan, India f International Maize and Wheat Improvement Centre (CIMMYT), El Batan, Texcoco, Mexico ARTICLE INFO Keywords: Portfolio of management practices Systems productivity Water productivity Economic profitability Energy efficiency Global warming potential ABSTRACT Several resource use efficient technologies and practices have been developed and deployed to address the challenges related to natural resource degradation and climatic risks management in rice-wheat (RW) rotation of Indo- Gangetic Plains (IGP). However, the practices applied in isolation may not be effective as much as in combination due to changing input responses under varied weather abnormalities. Therefore, a multi-location farmer s participatory strategic research was conducted to evaluate the effects of layering key technologies, practices and services in varied combinations and compared with business as usual (farmer s practice) for productivity (crop, water and energy), profitability and global warming potential (GWP) in a RW system. Altogether, six scenarios were compared that includes; Farmer s practice (FP); Improved FP (IFP) with low intensity of adaptive measures; IFP with high intensity of adaptive measures (IFP-AM); Climate smart agriculture (CSA) with low intensity of adaptive measures (CSA-L); CSA with medium intensity of adaptive measures (CSA-M); CSA with high intensity of adaptive measures (CSA-H). Results revealed that climate smart agricultural practice with high intensity of adaptive measures (CSA-H) recorded 7 9 and 19 26% higher system productivity and profitability, respectively compared to farmers practice in all the three years. CSAPs (mean of CSA-L, CSA-M and CSA-H) improved the system productivity and profitability by 6 and 19% (3 yrs mean) whereas, IFPs (mean of IFP and IFP-AM) by 2 and 5%, respectively compared to farmer s practice (11.79tha 1 and USD 1833 ha 1 ). CSA with high (CSA-H) and medium (CSA-M) intensity of adaptive measures saved 17 30% of irrigation water and improved irrigation and total water productivity (WP I and WP I+R )by29 54 and 21 38%, respectively compared to FP in the study years. Across the years, CSA-H improved the energy-use-efficiency (EUE) and energy productivity (EP) by and 44 56% respectively, compared to farmers practice. On 3 years mean basis, CSA-H lowered global warming potential (GWP) and greenhouse gas intensity by 40 and 44% respectively, compared to FP (7653 kg CO 2 eq ha 1 yr 1 and 0.64 kg kg 1 CO 2 eq ha 1 yr 1 ). On 3 years mean basis, our study revealed that CSA with high intensity of adaptive measures (CSA-H) increased 8% in system productivity, 23% in profitability, 31% in total water productivity and 53% in energy productivity with 24% less water while reducing the GWP by 40%. The improvement in yield, income as well as use efficiency of water and energy and reduction in GHGs was increasing with layering of portfolio of practices on farmers practice. This study helps in prioritizing the technological practices from the portfolio of CSAPs for maximizing crop productivity, profitability and input use efficiency while improving the adaptive capacity and reducing the environmental footprints. 1. Introduction In South Asia, home to about 1.5 billion people, slowdown in the growth rate of cereal production and increasing population pressure have emerged as formidable challenges for the future food and nutritional security. These challenges will be more intense under emerging scenarios of natural resource degradation, energy crisis, volatile markets and risks associated with global climate change (Jat et al., 2016; Corresponding author at: CIMMYT, CG Block, National Agriculture Science Center (NASC) Complex, Pusa, New Delhi , India. address: m.jat@cgiar.org (M.L. Jat). Received 5 July 2017; Received in revised form 4 February 2018; Accepted 17 February / 2018 Elsevier B.V. All rights reserved.

2 Lal, 2016). During , in process of achieving multi-fold increase in crop production in the region, inefficient use and inappropriate management of non-climate production resources (water, energy, agro-chemicals) have vastly impacted the quality of the natural resources and also increased vulnerability to climatic variability affecting farming adversely. The natural resources in South Asia especially in Indo-Gangetic plains (IGP) are 3 5 times more stressed due to population, economic and political pressures compared to rest of the world (Jat, 2017). This can potentially add to climatic risks, and making a large number of people more vulnerable to climatic hazards in the region. Further, in south Asia during last one decade, the growth rate of the agriculture production is not significantly increased with the population which leads to more risk. With no scope for horizontal expansion of farming, the future food demand of growing population has to be met mainly through increasing yield per unit area with lesser external inputs (labor, water and energy) while protecting the environment. Achieving all these under more variable and uncertain climatic condition at present and future is the foremost challenges of present time all across the IGP (Lal, 2013; Jat et al., 2016; Campbell et al., 2016). Having high risks of climate change induced extreme weather events, the crop yields in the region are predicted to decrease from 10 to 40% by 2050 with risks of crop failure in several highly vulnerable areas (Jat, 2017). Increase variability in both temperature and rainfall patterns, changes in water availability, shift in growing season, rising frequency of extreme events such as terminal heat, floods, storms, droughts, sea level rise, salinization and perturbations in ecosystems have already affected the livelihood of millions of people. Rice-wheat (RW) is the most important cropping system for food security in South Asia (13.5 Mha), providing food for more than 400 million people (Ladha et al., 2003). The concerns of natural resource degradation and increased intensity of risks associated with weather variability in the intensively cultivated IGP, the food bowl of South Asia, are multiplying. The area under the RW system covers 32 and 42% of total rice and wheat area, respectively (Saharawat et al., 2012) and is almost static and the productivity and sustainability of the system are threatened because of the inefficiency of current production practices, shortage of resources such as water and labour, open field burning of crop residues and socioeconomic changes (Ladha et al., 2003; Chauhan et al., 2012; Lohan et al., 2018). Further, climate change on the one hand, and changing land use pattern, natural resource degradation (especially land and water), urbanization and increasing pollution on the other hand could affect the ecosystem in this region directly and also indirectly through their impacts on climatic variables (Lal, 2016). For example, about 51% of the IGP may become unsuitable for wheat crop, a major food security crop for India, due to increased heat-stress by 2050 (Lobell et al., 2012; Ortiz et al., 2008). Similarly, water table in north western IGP being depleted at km 3 yr 1 (Rodell et al., 2009) due to over-pumping for rice will have serious impacts on rice production. Therefore, adaptation to climate change is no longer an option, but a compulsion to minimize the loss due to adverse impacts of climate change and reduce vulnerability (IPCC, 2014). Moreover, while maintaining a steady pace of development, the region would also need to reduce its environmental footprint from agriculture. Management practices that provide opportunities to reduce GHGs emission and increasing carbon sequestration is required for resilience in production systems (Sapkota et al., 2014b). Considering these multiple challenges, agricultural technologies that promote sustainable intensification and adapting to emerging climatic variability yet mitigating GHG emissions are scientific research and development priorities in the region (Dinesh et al., 2015). Climate smart agriculture practices (CSAPs) related to water (e.g. direct seeded rice, laser land leveling, alternate wetting and drying and weather forecast based irrigation), nutrient (e.g. SSNM through nutrient expert tools, green seeker, slow release nitrogen fertilizer), carbon (e.g. residue retention and incorporation), weather (index based crop insurance), energy (e.g. laser land leveling, direct seeded rice, zero tillage) and information and knowledge (e.g. ICTs) have been developed and validated (Ajani, 2014; Amin et al., 2016; FAO, 2010; Jat et al., 2016). However, these CSAPs in isolation may or may not play their potential role in adapting to climate risks and mitigating GHG emissions in RW production system. Therefore, layering of these practices and services in optimal combinations may help in adapting to climate risks and building resilience to extreme weather and climate variability, under diverse production systems and ecologies to ensure future food security. To the best of our knowledge, there is no systematic research evidence available on layering of different management practices (CSAPs) on productivity, profitability, resource use efficiency under favorable as well as unfavorable climate risk scenarios in most of the production systems. Rice-wheat system of IGP being important for food security and challenged by projected climate change consequences, we conducted participatory strategic research trials to evaluate the portfolios of agriculture practices (CSAPs) under six scenarios to understand what combination of practices (portfolio of practices) are more important in terms of maximizing crop productivity and profitability, water and energy use efficiency while reducing the greenhouse gases (GHGs). 2. Material and methods 2.1. Experimental site and weather condition Participatory strategic research trial was conducted during for three years ( to ) at farmers fields in three different climates smart villages (CSVs; of the Indian Haryana state in the Northwest IGP:, Birnarayana (29 75 N, E), Anjanthali (29 83 N, E) and Chandsamand (29 80 N, E) in Karnal district of Haryana, India. The research sites are typically ricewheat system dominated and has semi-arid tropical climate, characterized by hot and dry summer (April-September) and cold winters (October March). The average annual rainfall of the area is 700 mm, of which 80 per cent occurs during the month of June to September. The mean annual maximum and minimum temperature is 34 and 18 C, respectively and relative humidity remains 60 90% throughout the year. Seasonal weather data of study period and long term average data are presented in Fig Soil sampling and analysis Before starting the experiment, baseline soil samples were collected from 0 to 5, 5 to 15 and 15 to 30 cm soil depths using an auger of 5-cm internal diameter. For soil sampling, each plot was divided into four grids. Within each grid cell, soil was collected from four spots and composited for each depth. Bulk Density (BD) was measured using pistons auger (Chopra and Kanwar, 1991) and textural class was determined by the United States Department of Agriculture (USDA) system. Soil ph and electrical conductivity (EC) was determined in the saturation extract of 1: 2 (soil: water suspension) solution as described by Richards (1954). Soil organic carbon was analysed using Walkley and Black s (1934) rapid titration method. The available N in soil was determined by alkaline permanganate method (Subbiah and Asija, 1956), available P in 0.5 M NaHCO 3 extracts by Olsen et al. (1954) method and exchangeable K in IM NH 4 OAc-extracts by flame photometer method (Jackson, 1973). The experimental soil was silty loam in texture and low in nitrogen and medium in available phosphorus and potassium. The initial soil characteristics of the experimental sites are given in Table Experimental details and scenarios description The experiment was started in the summer season 2014 with six treatments combinations layered with different management protocols/ interventions over farmer practice. These scenarios consisted of 9 123

3 Fig. 1. Annual ( , and ) and long-term ( ) weather data related to rainfall and temperature (maximum and minimum). Table 1 Mean initial soil characteristics of on-farm experimental sites (Birnarayana, Anjanthali and Chandsamand), Karnal, India. Soil properties 0 5 cm 5 15 cm cm Clay (%) 9.33 ± ± ± 3.8 Slit (%) 60.2 ± ± ± 7.8 Sand (%) 30.5 ± ± ± 8.9 Soil texture silt loam silt loam silt loam ph (1:2 soil: water) 8 ± ± ± 0.15 EC (ds m 1 ) (1:2 soil: water) 0.49 ± ± ± 0.09 Bulk density (g cm 3 ) 1.54 ± ± ± 0.05 Organic carbon (%) 0.66 ± ± ± 0.02 Available N (kg/ha) 185 ± ± ± 4.6 Available P (kg/ha) 26.2 ± ± ± 2.6 Available K (kg/ha) 314 ± ± ± 53.7 management portfolio of practices/interventions related to enhance adaptive capacity to climate risks, yield and income efficiency of the system. First scenario was based on farmer s practice (FP) and scenario 2 (IFP) and 3 (IFP-AM) were layered with different technologies related to tillage, crop establishment, residue and nutrient management and designated as improved farmer s practices (IFPs). Remaining three scenarios (CSA-L, CSA-M and CSA-H) were based on climate smart agriculture practices (CSAPs) with available range of technologies/ practices viz., tillage, crop establishment, laser land levelling, residue, water and nutrient management, information and communication technology (ICT) and crop insurance. Before the start of the experiment, about 60 random farmer s families were surveyed from nearby villages to find out the portfolios of their current practices (FP) in RW rotation. Manual transplanting of rice in puddled fields and manual broadcasting of wheat seeds after intensive tillage operation is common practice in this region. In reduced tillage, operation was reduced by 33 and 40% in rice and wheat, respectively compared to FP. Scenario description and summary of various management protocols under different scenario are presented in Tables 2 and 3. Each scenario was replicated thrice in production scale plots (> 1000 m 2 ) in a randomized complete block design Crop residue management Crop residues of rice and wheat were removed or retained as per the scenario description given in Table 2. Both the crops were harvested using combine harvester (an agricultural machine that reaps, threshes, and cleans a cereal crop in one operation) in all the scenarios. The harvesting was done at a ground level clearance of and cm in rice and wheat, respectively except FP. A total of 33.8 t ha 1 of crop residues was incorporated in IFP during to (Table 4). Similar amount of crop residues were retained in all the five scenarios ranged from 11.3 to 12.1 t ha 1 yr 1. In IFP-AM, 1.6 t ha 1 (3 yrs mean) wheat residues was incorporated and 9.73 t ha 1 (3 yrs mean) of rice residue were retained on soil surface every year. A total of 35.4 and 36.2 t ha 1 crop residues were retained in CSA-M and CSA-H, respectively in 3 years (Table 4) Nutrient management In this region, recommended dose of nitrogen, phosphorus and potash (NPK) for both rice and wheat crop is 150:60:60 kg ha 1, but farmers in this region apply excess N, optimum P and no K (Table 3). In farmers fertilizer practice (FFP; Scenario 1 and 2), DAP and ZnSO 4 was applied as basal at the time of transplanting, whereas, urea was applied in three equal splits at early establishment (7 10 DAT; days after transplanting), active tillering (20 25 DAT), and at panicle initiation (40 45 DAT) in rice (TPR). In wheat, DAP was applied as basal and urea was applied in three equal splits manually at first, second and third irrigation (Tables 2 and 3). In recommended dose of fertilizers (RDF) (Scenario 3 and 4), DAP, MOP and ZnSO 4 was applied as basal while urea was top-dressed in three splits at early crop establishment (15 18 DAS; days after sowing), active tillering stage (40 DAS) and panicle stage (55 60 DAS) in rice (DSR). A foliar spray of iron sulphate (FeSO 4 was also done at 20 DAS in rice. In wheat, complex fertilizer NPK and MOP was applied as basal and remaining part of N was applied through urea (scenario 3) and neem coated urea (scenario 4) in two equal splits at first (25 DAS) and second irrigation (45 DAS) (Table 3). In scenario 5, RDF was given to both the crops as in scenario 3 except the third dose of nitrogen that was given based on Green Seeker reading at 62 and 65 days after sowing in rice and wheat, respectively (Singh et al., 2011, 2015). In scenario 6, site-specific nutrient management (SSNM) approach was used to tailor the recommended nutrient doses using Nutrient Expert (NE) instead of RDF layered with Green Seeker guided N. Fertilizers were applied as scenario 5. Nutrient Expert is an interactive, computer-based decision-support tool that enables implementation of SSNM in individual fields without soil test data (Pampolino et al., 2012; Sapkota et al., 2014a; Jat et al., 2016a) Other management protocols (laser land levelling, ICTs and crop insurance) Before the start of the experiment, the area was levelled (zero gradient) using a laser-equipped drag scraper (Trimble, USA) with an 124

4 Table 2 Scenario notations and description of management protocols under different scenarios in rice-wheat (RW) rotation. Cultivars Residue Management Water Management Nutrient Management ICT Crop Insurance Scenarios Tillage Crop Establishment Laser Land levelling Name Details FP, Residue removed FP FFP None None No Pusa44; PBW343 FP Business as usual (FP) CT TPR with random geometry. CTW using seed broadcasting FP FFP None None 100% of rice and 25% of wheat residue incorporated No Pusa44; PBW343 CT TPR with random geometry. CTW using seed broadcasting IFP FP with low intensity of adaptive measures (IFP) Same as in IFP SR RDF None None No Pusa44; HD2967 RT DSR sown with MCP. RTW sown with RDD IFP-AM IFP with high intensity of adaptive measures (IFP-AM) SR RDF None None 100% rice residue retained and 25% wheat residue incorporated Yes PR114; HD2967 RT-ZT DSR sown with MCP. ZTW sown with HS CSA-L CSA with low intensity of adaptive measures (CSA-L) Tensiometer based RDF + GS guided N Yes Yes 100% of rice residue and 25% of wheat retained ZT DSR and ZTW sown with HS Yes PR114; HD2967 CSA-M CSA with medium intensity of adaptive measures (CSA-M) Yes Yes Same as in CSA-M Tensiometer based NE + GS guided N + NCU ZT Same as in CSA-M Yes PR114; HD2967 CSA-H CSA with high intensity of adaptive measures (CSA-H) Where: FP: Farmer s practice, IFP: Improved farmer s practice, CSA: Climate smart agriculture, CT: Conventional tillage, RT: Reduced till, ZT: Zero till, TPR: Transplanted rice, CTW: Conventional till wheat, DSR: Direct seeded rice, MCP: Multi crop planter, RTW: Reduced till wheat, RDD: Rotary disc drill, ZTW: Zero till wheat, HS: Happy Seeder, SR: State recommendation for irrigation, FFP: Farmer s fertilizer practice, RDF: Recommended dose of fertilizer, NCU: Neem coated urea, GS: Green Seeker, NE: Nutrient expert based fertilizer recommendation, ICT: Information and communication technology. automatic hydraulic system powered by a 60-HP tractor in scenario 5 and 6 (Table 2). Application of irrigation, herbicides and insecticides were tailored based on short term weather forecast (STWF) bulletin. Information and communication technologies (ICTs) like Indian Farmers Fertiliser Cooperative (IFFCO) Kisan Sanchar limited (IKSL) aired STWF through voice and text messages on registered farmer s cell number. Both the crops were insured with weather based crop insurance during both the years. Weather Based Crop Insurance was done through Agriculture Insurance Company (AIC) of India limited ( to mitigate the financial loss on account of anticipated crop yield losses from incidence of adverse conditions of weather variability like excess rainfall, cold and heat stress. For this, 2 and 1.5% of total sum insured (INR 62,500 and 55,000 ha 1 for rice and wheat, respectively) were paid to AIC as basic premium for rice and wheat, respectively. In both the seasons, crops were insured for abnormality but we did not face the weather abnormalities Weed management Selective and non-selective herbicides were used to control all sedges, grassy and broad leaf weeds in RW rotation. Under ZT conditions, g a.i. ha 1, non-selective herbicides were used to control the weeds prior to sowing. In puddled transplanting rice (PTR), Butachlor 50 g a.i. ha 1 was used as pre-emergence herbicide at one day after transplanting. In direct seeded rice (DSR), pre-emergence spray of g a.i. ha 1 or 1000 g a.i. ha 1 was applied just one day after seeding followed by another herbicide mix spray of Bispyribac Sodium 10% SP + Pyrazosulfuronethly 10% WP (8 10 g + 6 g a.i. ha 1, respectively), at days after sowing to control all grassy and broad leaf weeds and sedges. Tank mix solution of Pinoxaden 5% g a.i. ha 1 or Clodinafop-ethyl + Metsulfuron ( g a.i. ha 1 ) was applied at days after wheat sowing to control weeds Crop data and economics Crops were harvested manually from 8 5 m 2 randomly selected quadrate from 3 places within each plot for grain and straw yields. To express the overall impact of treatments on system productivity was calculated on rice equivalent yield (REY) basis for wheat grain yield. Grain yield of rice and wheat were recorded at 14% moisture content basis. System productivity (t ha 1 ) was computed using Eq. (1). REY (t ha 1 ) = [{Wheat yield (t ha 1 ) MSP of Wheat (INR t 1 )}/ MSP of Rice (INR t 1 )] (1) where, MSP is the Minimum Support Price (Table 5); INR is the India National Rupee; Economics was calculated using both fixed and variable costs. The fixed cost included land rent, depreciation of machinery and interest on working capital. Variable costs included human labour, tractor operational charges and cost of production inputs such as seed, fertilizer, pesticide, irrigation, harvesting, threshing, cleaning etc. The cost of human labour used was based on labour-days/ha assuming an 8-h working day (as per labour law of India). All these fixed and variable costs were summed up to calculate total cost of production. Gross returns were obtained as per the prevailing market prices of the commodity (grain and straw) over the different years (Table 5). Net returns were calculated by deducting the total cost of cultivation from the gross returns Irrigation management A, 6- inch polyvinyl chloride (PVC) pipeline was installed in a 60 cm deep trench with an outlet for each plot separately. On-line water meter (Woltman helical turbine) was fitted for irrigation water measurement. 125

5 Table 3 Crop management practices for rice-wheat (RW) rotation under different scenarios. Scenarios a / Management practices Scenario 1 (FP) Scenario 2 (IFP) Scenario 3 (IFP-AM) Scenario 4 (CSA-L) Scenario 5 (CSA-M) Scenario 6 (CSA-H) Field preparation Rice- 2 pass of harrow, 1 pass of rotavator, 2 pass of puddle harrow followed by (fb) planking; Wheat- 2 pass of harrow and rotavator each fb planking Same as in FP Seed rate (kg ha 1 ) b Same as in FP Crop geometry Random geometry Same as in FP Source of fertilizers Urea (46:0:0) and Di-ammonium phosphate (DAP) (18:46:0) Same as in FP Fertilizer (N:P:K) in kg Rice-195:58:00 Same as in ha 1 FP Rice-1 pass of harrow, 1 pass of cultivator fb planking; Wheat- 1pass of harrow, 1 pass of cultivator fb planking Rice- Same as in IFP- AM; Wheat- Zero tillage Zero tillage Same as in CSA-M Same as in IFP-AM Same as in IFP-AM Same as in IFP-AM 22 cm 20 cm Same as in IFP-AM Same as in IFP-AM Same as in IFP-AM Urea, DAP, Muriate of potash (MOP) (0:0:60), and NPK complex (12:32:16) Neem coated urea (46:0:0), DAP, MOP and NPK complex Same as in CSA-L Same as in CSA-L Rice- 150:60:60; FeSO Same as in IFP-AM Rice- 147:60:60 (in 1st yr) 153:60:60 (in 2nd yr) and 158:60:60 (in 3rd yr) + per year Wheat- 185:58:00; ZnSO kg ha 1 Wheat- 150:60:60; ZnSO Wheat- 143:60:60 (in 1st yr), 25 kg ha 1 120:60:60 (in 2nd yr) and 134:60:60 (in 3rd yr) Water management Rice- Continuous flooding of 5 6 cm depth for days after transplanting fb irrigation applied at alternate wetting and drying. Same as in FP Rice- Soil was kept wet up to 20 days after sowing fb irrigation applied at hair-line cracks. Wheat- 4 6 irrigation as per requirement Wheat- 4 6 irrigation as per critical crop growth stages Same as in IFP-AM Rice- Soil was kept wet till germination fb irrigation at 20 to 30 kpa matric potential; Wheat- Irrigation at 50 to 55 kpa matric potential Rice- 138:39:70 (in 1st yr), 140:42:57 (in 2nd yr) and 145:44:57 (in 3rd yr); + per year Wheat- 135:62:60 (in 1st yr), 111:58:55 (in 2nd yr) and 122:56:55 (in 3rd yr) Same as in CSA-M a Refer Table 1 for scenario description. b Seed treatment was done with Bavistin g per 10 kg seed-raxil Tebuconazole 2DS (2% w/w) at 0.2 g a.i. kg 1 seed. 126

6 Table 4 Total residue load (t ha 1 ) under different scenarios over the years. Scenarios a Residue incorporated/retained (t ha 1 ) Rice Wheat System Rice Wheat System Rice Wheat System FP -NA b - -NA- -NA- -NA- -NA- -NA- -NA- -NA- -NA- IFP IFP-AM CSA-L CSA-M CSA-H a Refer Table 1 for scenario description. b Not applicable. Table 5 Cost of key inputs and outputs used for economic analysis during the different years. distance from bund. Water management protocols for each scenario are presented in Table 3. Item/Commodity Cost (INR) Energy analysis Rice grain (kg 1 ) Rice residue (ha 1 ) -NA- -NA- -NA- Rice seed (kg 1 ) Wheat grain (kg 1 ) Wheat residue (kg 1 ) Wheat seed (kg 1 ) PBW 343/HD / / /28.5 Urea (kg 1 ) Di-ammonium-phosphate (DAP) (kg 1 ) Muriate of potash (MOP) (kg 1 ) NPK Complex (kg 1 ) Zinc sulphate (ZnSO 4 ) (kg 1 ) Harrowing (ha 1 ) Cultivator (ha 1 ) Planking (ha 1 ) Puddler (ha 1 ) Rotavator (ha 1 ) Turbo Happy Seeder (ha 1 ) Seed drill (ha 1 ) Minimum support price (MSP) for rice (kg 1 ) Minimum support price (MSP) for wheat (kg 1 ) Wages Rate (person 1 day 1 ) USD ($) to INR Conversation rate a Refer Table 1 for scenario description. b Not applicable. Water meter readings were recorded at the start and at the end irrigation to calculate the amount of irrigation water applied per plot. The amount of irrigation water applied was calculated as water depth (mm ha 1 ) by using Eqs. (2) and (3), while, irrigation water productivity (WP I ) and total (irrigation + rainfall) water productivity (WP I+R ) using Eqs. (4) and (5). Volume of irrigation water (kilolitre ha 1 ) = {(Final water meter reading-initial water meter reading)/plot area in m 2 } * (2) Irrigation water (mm ha 1 ) = Volume of irrigation water (kilolitre ha 1 )/10 (3) WP I (kg grain m 3 ) = Grain yield (kg ha 1 )/Irrigation water used (m 3 ha 1 ) (4) WP I+R (kg grain m 3 ) = Grain yield (kg ha 1 )/Irrigation + rainfall water used (m 3 ha 1 ) (5) where, 1 ha-mm irrigation depth = 10 kl = 10,000 l = 10 m 3 In laser levelled plots (CSA-L, CSA-M and CSA-H) 4 cm depth of irrigation was applied, however, in non-laser levelled plots (FP, IFP and IFP-AM) 5 6 cm of irrigation was applied. To monitor soil metric potential a gauge-type soil tensiometer were installed with the 5 m All the crop inputs (human labor, machinery, diesel, fertilizer, pesticides, seed, irrigation etc.) and outputs (grain and straw) were used to estimate the energy of crops and cropping system. The energy efficiency of crops and system was calculated as ratio between energy outputs and inputs and expressed in MJ ha 1. The mechanical energy was computed on the basis of total fuel consumption (liter ha 1 )in different field operations. Based on the energy equivalents of the inputs and outputs (Table 6), energy use efficiency and energy productivity were calculated using Eqs. (6) and (7). Energy use efficiency = Total energy Output (MJ ha 1 )/Total energy Input (MJ ha 1 ) (6) Energy productivity (kg MJ 1 ) = Grain output (kg ha 1 )/Total energy input (MJ ha 1 ) (7) Global warming potential (GWP) analysis The greenhouse gases (GHGs) emissions were calculated by using CCAFS-MOT i.e. Mitigation Options Tool developed by Climate Change, Agriculture and Food Security (CCAFS) in collaboration with the university of Aberdeen (Feliciano et al., 2015) which estimates the performance of production system from GHG emission perspective both in terms of land-use efficiency and efficiency per unit of product. The model calculates background and fertilizer- induced emissions based on multivariate empirical model of (Bouwman et al., 2002) for nitrous oxide (N 2 O) and nitric oxide (NO) emissions, and the model of FAO/IFA (2001) for ammonia (NH 3 ) emission. Emissions from crop residues returned to the field were calculated using IPCC N 2 O Tier 1 emission factors. Similarly, emissions from the production and transportation of fertilizer were based on Ecoinvent database (Ecoinvent Center, 2007). Changes in soil carbon (C) due to tillage and residue management are based on IPCC methodology as in Ogle et al. (2005) and Smith et al. (1997). Similarly, emissions of CO 2 from soil resulting from urea application are estimated using IPCC methodology (IPCC, 2006). To estimate the total GHG emissions from the production systems i.e. global warming potential (GWP) (Eq. (8)), all GHGs are converted into CO 2 - equivalents (CO 2 e) using the global warming potential (over 100 years) of 34 and 298 for CH 4 and N 2 O, respectively (IPCC, 2013) (Eq. (8)). Yield-scaled GWP of each crop was determined by dividing the total GWP by grain yield. GWP (kg CO 2 -eq ha 1 )=CO 2 (kg ha 1 )+N 2 O (kg ha 1 ) CH 4 (kg ha 1 ) 34) (8) 127

7 Table 6 Energy equivalents (MJ unit 1 ) used for energy input and output calculations. Particulars Units Unit energy equivalent (MJ Unit 1 ) References Input Human labour Man-hour 1.96 Gathala et al. (2016) Diesel Litre Gathala et al. (2016) Nitrogen (N) kg Gathala et al. (2016) Phosphorus (P 2 O 5 ) kg Gathala et al. (2016) Potassium (K 2 O) kg Gathala et al. (2016) Herbicides, insecticides and pesticides kg Gathala et al. (2016) Irrigation water ha-cm Gathala et al. (2016) Zinc sulphate (ZnSO 4 ) kg 8.40 Argiro et al. (2006) Iron sulphate (FeSO 4 ) kg Argiro et al. (2006) Rice/Wheat seed kg Ozkan et al. (2004) Output Rice and Wheat grain kg Ozkan et al. (2004) Rice and Wheat Straw kg Ozkan et al. (2004) Statistical analysis The data recorded for different crop parameters were analysed using analysis of variance (ANOVA) technique (Gomez and Gomez, 1984) for randomized block design using SAS 9.1 software (SAS Institute, 2001). Where ANOVA was significant, the treatment means were compared using Tukey s honestly significant difference (HSD at 5% level of significance). 3. Results 3.1. Weather The amount of rainfall received during rice and wheat production season in , and was similar to the long-term average (700 mm) although their distribution was different amongst the monsoon months (June September) (Fig. 1). Rice season in 2014, 2015 and 2016 received 485 (256 mm in September), 420 (255 mm in July) and 533 (284 mm in August) mm of rainfall, respectively. The wheat season in first year received a rainfall of 247 mm whereas in second and third year it was only 56 and 96 mm. In first year, about 40% of total wheat season rainfall occurred during 2nd march which affected the crop to some extent. Daily maximum and minimum temperature and total rainfall was similar in all the years which were also similar to the long-term average of the study area (Fig. 1) Crop productivity Rice yield was not different under different scenarios in the first year (2014), but in second year, the higher yield (7.14 t ha 1 ) was recorded with CSA-H and found at par with IFP and CSA-M (Table 7). However, in third year, IFP recorded the higher (7.09 t ha 1 ) and at par yield with other scenarios compared to IFP-AM and CSA-L (Table 7). On 3 years mean basis rice grain yield ranged from 6.73 to 6.90 t ha 1 under different scenarios. Wheat grain yield was influenced significantly with layering of various crop management practices in all the years (Table 7). Climate smart agriculture practices (CSAPs-) produced similar and higher wheat Table 7 Effect of management practices portfolios on grain yield, cost of cultivation and net returns under different scenarios during year , and Scenarios a Grain yield (t ha 1 ) Cost of cultivation (USD ha 1 ) Net return (USD ha 1 ) Rice Wheat System c Rice Wheat System Rice Wheat System FP 6.59 Ab 4.70 C C 549 A 327 A 876 A 795 D 959 C 1753 D IFP 6.59 A 4.65 C C 549 A 328 A 877 A 794 D 947 C 1740 D IFP-AM 6.52 A 5.31 B B 486 B 326 A 812 B 842 CD 1117 B 1961 C CSA-L 6.55 A 5.44 AB AB 466 C 314 B 779 C 869 BC 1163 AB 2033 BC CSA-M 6.60 A 5.52 AB AB 418 D 313 B 731 D 927 AB 1183 A 2110 AB CSA-H 6.64 A 5.66 A A 402 E 311 B 713 E 951 A 1222 A 2173 A FP 6.73 B 5.19 C D 570 A 386 B 956 A 803 D 1023 B 1825 E IFP 6.88 AB 5.30 C BC 570 A 386 B 956 A 832 CD 1055 B 1887 DE IFP-AM 6.70 B 5.44 BC BC 486 B 398 A 885 B 879 C 1081 B 1960 D CSA-L 6.71 B 5.70 AB AB 477 C 348 C 825 C 891 BC 1200 A 2091 C CSA-M 6.84 AB 5.80 A A 441 D 340 D 781 D 953 B 1237 A 2189 B CSA-H 7.14 A 5.87 A A 426 E 334 E 760 E 1031 A 1262 A 2293 A FP 6.86 AB 5.30 B B 572 A 398 B 970 A 875 C 1045 C 1920 C IFP 7.09 A 5.49 B AB 572 A 397 B 969 A 924 BC 1098 C 2021 C IFP-AM 6.72 B 5.40 B B 490 B 410 A 900 B 929 BC 1060 C 1989 C CSA-L 6.69 B 5.77 A AB 486 B 359 D 845 C 925 BC 1212 B 2138 B CSA-M 6.76 AB 6.00 A A 468 C 364 C 831 D 960 AB 1269 A 2228 AB CSA-H 6.91 AB 6.01 A A 452 D 355 D 808 E 1007 A 1280 A 2287 A a Refer Table 1 for scenario description. b Means followed by a similar uppercase letters within a column are not significantly different at 0.05 level of probability using Tukey s HSD test. c System grain yield was expressed as rice-equivalent yield (t ha 1 ). 128

8 Table 8 Irrigation water use and water productivity under different scenarios during the year , and Scenarios a Irrigation (mm ha 1 ) Rainfall (mm) WP I (kg grain m 3 ) WP I+R (kg grain m 3 ) Rice Wheat System Rice Wheat System Rice Wheat System Rice Wheat System FP 2135 Ab 245 A 2381 A C 1.96 C 0.48 D 0.26 C 0.96 C 0.37 D IFP 2135 A 245 A 2387 A C 1.94 C 0.48 D 0.26 C 0.95 C 0.37 D IFP-AM 1776 B 212 AB 1989 B B 2.61 BC 0.60 C 0.30 B 1.17 B 0.44 C CSA-L 1644 C 182 B 1827 C B 3.08 AB 0.66 B 0.31 B 1.27 AB 0.47 B CSA-M 1507 D 158 B 1666 D A 3.48 A 0.73 A 0.33 A 1.36 A 0.51 A CSA-H 1507 D 158 B 1666 D A 3.57 A 0.74 A 0.34 A 1.40 A 0.51 A FP 1875 A 423 A 2299 A C 1.23 B 0.52 D 0.30 D 1.09 B 0.44 D IFP 1875 A 423 A 2299 A C 1.26 B 0.53 D 0.31 D 1.11 B 0.45 D IFP-AM 1598 B 423 A 2022 B B 1.29 B 0.60 C 0.33 C 1.14 B 0.49 C CSA-L 1495 C 359 B 1855 C B 1.59 A 0.67 B 0.35 B 1.38 A 0.53 B CSA-M 1355 D 359 B 1715 D A 1.62 A 0.74 A 0.39 A 1.40 A 0.58 A CSA-H 1355 D 359 B 1715 D A 1.64 A 0.76 A 0.40 A 1.42 A 0.60 A FP 1821 A 420 A 2241 A B 1.26 B 0.55 C 0.30 B 1.06 B 0.44 C IFP 1821 A 420 A 2241 A B 1.31 B 0.57 C 0.30 B 1.10 B 0.45 C IFP-AM 1632 B 420 A 2053 B AB 1.29 B 0.60 BC 0.31 AB 1.09 B 0.46 BC CSA-L 1565 BC 369 B 1934 BC A 1.57 A 0.66 AB 0.33 A 1.29 A 0.50 AB CSA-M 1498 C 369 B 1867 C A 1.63 A 0.70 A 0.33 A 1.34 A 0.52 A CSA-H 1498 C 369 B 1867 C A 1.63 A 0.71 A 0.34 A 1.34 A 0.53 A a Refer Table 1 for scenario description. b Means followed by a similar uppercase letters within a column are not significantly different at 0.05 level of probability using Tukey s HSD test. yields across the years compared to FP and IFPs (mean of IFP and IFP- AM). CSA-H, CSA-M and CSA-L recorded 16, 14 and 12% (3 yrs mean) higher yield compared to that of FP, respectively. Improved farmer s practices (mean of IFP and IFP-AM) and CSAPs (mean of CSA-L, CSA-M and CSA-H), recorded 4 and 14% (3 yrs mean) higher yield respectively, compared to farmers practice (FP) (Table 7). The unusual weather reduced conventional-till (FP) wheat yield by 0.84 t ha 1 under farmers practice compared to zero-till (Happy Seeder sown) wheat in CSAPs in first year, whereas, during normal year (second and third year) wheat yield was lowered by 0.6 t ha 1 in FP compared to CSAPs (Table 7). Higher system (rice equivalents) productivity was recorded with CSAPs (CSA-L, CSA-M and CSA-H) compared to FPs (FP, IFP and IFP- AM) in all the years except at par with IFP in (Table 7). System productivity was increased by 8, 6 and 4% (3 yrs mean) in CSA-H, CSA- M and CSA-L, respectively compared to farmers practice (Table 7). CSAPs and IFPs with varied intensity of adaptive measures increased the system productivity by 6 and 2% (3 yrs mean) respectively, compared to farmers practice (11.97 t ha 1 ) Economic profitability The costs of cultivation were mainly attributed to field preparations, fertilizer dose, irrigation and man-days used. Higher cost of cultivation was observed with the FPs compared to CSAPs during all the years (Table 7). Farmers practice and IFP recorded the highest (USD 934 ha 1 ; 3 yrs mean) and CSA-H recorded the lowest (USD 760 ha 1 ; 3 yrs mean) cost of cultivation during all the three years. In rice, wheat and RW system, net returns were higher in order of CSA-H > CSA- M > CSA-L > IFP-AM > IFP > FP based on 3 yrs mean (Table 7). On an average, CSAPs increased the net returns from rice, wheat and RW production by 15, 21 and 19% (3 yrs mean), respectively compared to farmers practice (USD 824, 1009 and 1833 ha 1, respectively). Compared to current farmers practice (FP), CSA-H increased the net return by 21, 24 and 23% (3 yrs mean) in rice, wheat and RW system, respectively (Table 7). Climate smart agriculture practices (CSAPs) and IFPs improved system profitability by 19 and 5% (3 yrs mean) respectively, compared to farmers practice Irrigation water use and productivity The amount of irrigation water in rice varied from 1507 to 2135, 1355 to 1875 and 1498 to 1821 mm ha 1 and that in wheat varied from 158 to 245, 359 to 423 and 369 to 420 mm ha 1 during first, second and third year, respectively (Table 8). Puddled transplanted rice consumed highest (1944 mm ha 1 ; 3 yrs mean) amount of water, whereas CSA-M and CSA-H consumed lowest (1454 mm ha 1 ; 3 yrs mean) amount of irrigation water in all the years and the same trend was followed in wheat crop also (Table 8). Compared to farmers practice (FP), CSA with layering of different practices (i.e. CSA-L, CSA-M and CSA-H) saved irrigation water by 14 29% in rice and 12 36% in wheat and 14 30% in RW system across the years. CSAPs and IFPs saved 22 and 6% irrigation water on 3 yrs mean basis respectively, compared to FP (2307 mm ha 1 ) in RW system. Higher grain yield and lower water requirement in the CSA scenarios with medium and high level of adaptive measures (CSA-M and CSA-H) led to significantly higher water productivity (WP I and WP I+R ) of rice, wheat and RW system in all the 3 years compared to farmers practices (FPs) (Tables 7 and 8). On 3 years range basis, CSA-H recorded and 29 82% higher WP I and and 26 46% higher WP I+R in rice and wheat, respectively compared to farmers practice. IFP-AM and CSA-L had somewhat intermediate water productivity of rice, wheat and RW system in all the years (Table 8). On 3 years mean basis, CSA-H improved WP I and WP I+R by 42% and 31%, respectively compared to FP. CSAPs increased WP I and WP I+R by 37 and 26% and IFPs by 9 and 5% (3 yrs mean) compared to farmers practice (0.42 and 0.52 kg grain m 1 ) Energy and its use efficiency and productivity Energy input in rice ranged from to MJ ha 1 whereas that in wheat ranged from to MJ ha 1. Energy input for rice, wheat and RW system was the highest in FP and IFP followed by IFP-AM > CSA-L > CSA-M > CSA-H in all the years and 3 years mean basis (45.67, and MJ ha 1 ). But energy-output from rice, wheat and RW system was the highest under CSAPs compared to FPs. The lowest input and the highest output of energy under CSA-H resulted into the highest energy use efficiency 129

9 Table 9 Effect of management scenarios on energy (input and output), energy use efficiency and energy productivity during , and Scenarios a Energy input 10 3 MJ ha 1 Energy output 10 3 MJ ha 1 Energy use efficiency (MJ ha 1 ) Energy productivity (kg MJ 1 ) Rice Wheat System Rice Wheat System Rice Wheat System Rice Wheat System FP Ab A A 216 B 141 C 357 C 4.78 D 6.17 D 5.24 E 0.15 C 0.21 E 0.17 D IFP A A A 216 B 140 C 356 C 4.76 D 6.11 D 5.22 E 0.15 C 0.20 E 0.17 D IFP-AM B B B 218 B 157 B 375 B 6.09 C 7.91 C 6.74 D 0.18 B 0.27 D 0.22 C CSA-L C C C 224 A 161 AB 385 A 6.56 B 8.57 B 7.27 C 0.19 B 0.29 C 0.23 B CSA-M D D D 225 A 162 AB 387 A 7.23 A 8.98 B 7.87 B 0.21 A 0.31 B 0.25 A CSA-H E D E 226 A 166 A 392 A 7.45 A 9.45 A 8.18 A 0.22 A 0.32 A 0.26 A FP A A A 221 C 158 C 379 D 5.20 D 6.29 E 5.60 E 0.16 D 0.21 D 0.18 E IFP A A A 225 BC 161 C 387 CD 5.30 D 6.44 E 5.72 E 0.16 D 0.21 D 0.18 E IFP-AM B B B 226 BC 166 BC 392 C 6.60 C 7.41 D 6.92 D 0.20 C 0.24 C 0.22 D CSA-L C C C 230 BC 174 AB 404 B 7.05 C 8.32 C 7.54 C 0.21 C 0.27 B 0.24 C CSA-M D D D 233 B 177 A 409 B 7.68 B 9.51 B 8.37 B 0.23 B 0.31 A 0.26 B CSA-H E E E 243 A 179 A 422 A 8.38 A A 9.02 A 0.25 A 0.33 A 0.28 A FP A A A 216 AB 160 D 376 C 4.43 C 5.99 F 4.97 E 0.14 C 0.20 E 0.16 D IFP A A A 223 A 165 C 389 ABC 4.57 C 6.20 E 5.14 E 0.14 C 0.21 E 0.17 D IFP-AM B B B 214 B 162 CD 376 C 5.25 B 6.78 D 5.81 D 0.16 B 0.23 D 0.19 C CSA-L BC C C 213 B 174 B 386 BC 5.43 B 7.76 C 6.26 C 0.17 B 0.26 C 0.21 B CSA-M CD D D 215 AB 180 A 395 AB 5.75 AB 8.55 B 6.72 B 0.18 AB 0.28 B 0.22 A CSA-H D E E 221 AB 181 A 401 A 6.11 A 9.04 A 7.11 A 0.19 A 0.30 A 0.23 A a Refer Table 1 for scenario description. b Means followed by a similar uppercase letters within a column are not significantly different at 0.05 level of probability using Tukey s HSD test. (EUE) and energy productivity (EP) in both the crops and on system basis in all the 3 years followed by CSA-M and CSA-L compared to other scenarios (Table 9). Both EUE and EP of rice, wheat and RW system under CSA-H was increased by 36 60% compared to that under farmers practice (FP). Energy input, output and therefore EUE and EP was somewhat intermediate under IFP-AM and CSA-L scenarios in all the years (Table 9). CSA-H recorded lower energy intake by 25 33% and improved EUE and EP ranged from 38 to 61 and 36 to 56% during all the years compared to FP in rice. Compared to farmers practice, EUE and EP were increased by and 50 57% in wheat across the years. CSA-H recorded 53% (3 yrs mean) higher EUE and EP compared to farmers practice (5.26 MJ ha 1 and 0.17 kg MJ 1 ). CSAPs recorded 7% (3 yrs mean) higher energy output, 43% higher EUE and 41% higher EP compared to FP in RW system. However IFPs improved the EUE and EP by 12% (3 yrs mean) compared to farmers practice Global warming potential (GWP) Global warming potential (GWP) as well as greenhouse gas intensity (GHGI) was highest in IFP followed by FP and the lowest was with CSA-H (Fig. 2). Improved farmers practice with high intensity of adaptive measures (IFP-AM) and CSA with low intensity of adaptive measures (CSA-L) recorded somewhat intermediate GWP and GHGI. On 3 years mean basis, CSA-H recorded lower GWP by 40% and GHGI by 44% compared to FP (7653 kg CO 2 eq ha 1 yr 1 and 0.64 kg kg 1 CO 2 eq ha 1 yr 1 )(Fig. 2). CSAPs (mean of CSA-H, CSA-M and CSA-L) lowered GWP and GHGI by 37 and 40% (3 yrs mean) compared to FP. 4. Discussion 4.1. Crop productivity and profitability The similar rice yield under TPR and DSR was reported by many researchers (Gathala et al., 2011, 2014; Kamboj et al., 2012; Jat et al., 2014) from the same region with best management practices, while some studies reported lower yield under DSR after 2 or 3 years under ZT condition (Kumar et al., 2018; Sharma et al., 2015). Our result reconfirms the benefit of DSR in subsequent wheat in RW system as also reported by Jat et al. (2014) and Kumar et al. (2018). Puddling the field during rice season results in compaction of the field resulting in poor rooting of subsequent wheat crops whereas growing rice without puddling (DSR) improves the soil physical properties (Gathala et al., 2011; Jat et al., 2017) and increase soil organic matter (Choudhary et al., 2018b; Jat et al., 2017; Sidhu et al., 2015) ensuring better germination and root development of subsequent wheat crop (Aryal et al., 2016). Better root development and deeper penetration, improves the uptake of water and nutrients (Jat et al., 2017). Poor performances of winter crops following puddled transplanted rice is also reported by other researchers in the region (Jat et al., 2009; Saharawat et al., 2010; Gathala et al., 2011) and have attributed this to deteriorated soil condition, compaction and increased bulk density. The yield difference between normal and bad years was much higher in FP and the lowest in CSA-H indicating that the scenario which bundled all possible CSA practices offered maximum adaptation to this climatic risk. Intensive tillage based scenarios showed water stagnation for long period due to untimely rainfall which ultimately turned into lower grain yield but such factors did not influence grain yield under ZT scenarios (Gathala et al., 2011; Aryal et al., 2016). Higher RW system productivity with climate smart agriculture practices might be due to improved management practices, precise land levelling (Humphreys et al., 2010), efficient cultivar, proper tillage and crop establishment (Gathala et al., 2014; Jat et al., 2014; Kumar et al., 2018), precise water management (Choudhary et al., 2018a) and higher nutrient availability (Jat et al., 2017). Higher net returns were associated with CSAPs followed by IFPs due to lower cost of cultivation incurred in tillage, crop establishment, irrigation cost and higher crop yields (Tables 7 and 8). Avoiding tillage, puddling and manual transplanting in rice and adoption of zero-till DSR reduced tillage and establishment cost by 79 85% (Gathala et al., 2011). Lesser water and labour requirement reduced the input costs to a great extent in DSR (Erenstein and Laxmi, 2008; Laik et al., 2014; Saharawat et al., 2010, 2012) compared to transplanted rice (TPR). Higher crop yields and lower production cost in CSAPs resulted in to higher profitability of RW system. Deployment of CA based indicators (tillage, crop establishment, residue, nutrient and water management) under rice-wheat systems in IGP resulted in higher net returns (Gathala 130

10 Fig. 2. Global warming potential (GWP) and greenhouse gases intensity (GHGI) of rice-wheat system under different scenarios (Treatment bars followed by a different letter within a group are significantly different at P < 0.05 according to Tukey s HSD test). et al., 2014; Jat et al., 2014; Sharma et al., 2015; Kumar et al., 2018) due to lower production costs and higher crop productivity compared with farmers practice Water productivity Direct seeded rice (DSR) under CSAPs (CSA-L, CSA-M and CSA-H) required 20 29% less water than TPR system under FP (Table 8) which was comparable to the saving reported by Nawaz et al. (2017) who stated 19% water saving with DSR over TPR in RW system. Some other researchers (Mahajana et al., 2012; Jat et al., 2009; Choudhary et al., 2018a; Kumar et al., 2018) reported 15 50% water saving in DSR compared to TPR across South Asia and opined that it can further be increased by using smart water management techniques. Mishra et al. (2013) had also revealed that short-term weather forecasts (5-days) could reduce average water application by 27%. In CSAPs scenarios, avoiding puddling (which requires around 3 4 irrigation) and keeping crop residues on the soil surface probably minimized development of cracks thereby reducing water loss (Gathala et al., 2014; Kamboj et al., 2012; Kumar et al., 2018). Residue retention in ZT wheat based system reduces the water requirement by conserving soil moisture, reducing evaporation loss and minimizing competitions between crop plants and weeds by suppressing weed population (Erenstein et al., 2008; Sidhu et al., 2015; Gathala et al., 2014; Kumar et al., 2018; Sharma et al., 2015). Higher water productivity of ZT wheat under CSAPs than in farmers practice (FP) was due to elimination of pre-sowing irrigation thereby requiring less water coupled with higher grain yield under ZT conditions (Kumar et al., 2018). Higher water productivity of DSR, ZT wheat and CA based RW rotation was also recorded by Gathala et al. (2014) from the same district and they attributed this to better grain yields with less water consumption. Similarly, Jat et al. (2009, 2011) have also reported higher water productivity with 50% less irrigation water under precise laser land levelling compared to traditional land levelling Energy productivity The higher energy input was associated with tillage, labor, irrigation and nitrogenous fertilizers in FPs compared to CSAPs. Srivastava (2003), Kumar et al. (2013) and Aravindakshan et al. (2015) reported that intensive tillage before planting required about one-third of the total operational energy that could be saved without adversely affecting the yield with zero tillage. Barut et al. (2011) and Ladha et al. (2015) also found that intensive tillage for crop establishment, higher amount of irrigation water, higher labour and fertilizer inputs are the major factors for higher energy usage under current production systems. Laik et al. (2014) and Gathala et al. (2016) confirmed that intensive tillage are the major factors contributing up to 40% higher energy usage than improve agronomic management practices in RW system. Higher use efficiency and productivity of energy in rice, wheat as well as RW system under CSA scenarios (CSA-L, CSA-M and CSA-H) was mainly due to lower energy input and higher energy output in these scenarios as compared to FP. Our results are in close conformity with that of Laik et al. (2014), Ladha et al. (2015), Parihar et al. (2017, 2018) and Kumar et al. (2018) who also reported lower energy input and higher output energy, energy use efficiency and energy productivity with improved CA- based management practices Mitigation potential The major sources of emission in our study were production, transportation and application of fertilizer, residue management and water management in rice. The estimated GHG emission from our study are in close conformity with the work reported from India (Bhatia et al., 2005; Datta et al., 2009; Malla et al., 2005) as well as global metaanalysis by Linquist et al. (2012). Rice CH 4 emission was different under different scenarios i.e. DSR vs TPR. This was because although frequency of irrigation was higher in TPR than in DSR, both water management system could be broadly categorized as Multiple drainage. Padre et al. (2016), through their field measurement in same area, reported that switching TPR to ZT-DSR reduced CH 4 emissions by 56% without significant increases in N 2 O emissions. Reduction of GWP under CSAPs was primarily due to the use of neem coated N fertilizer, lower nitrogen amount as a result of SSNM techniques and proper residue management. Reduction of GWP in the rage of 44 47% without any significant yield loss under ZT-based CSA practices compared to CT-based farmers practices are also reported by Bhattacharyya et al. (2012), Gupta et al. (2016) and Sapkota et al. (2015, 2017) from the IGP region. 5. Conclusion Our results showed that layering of management practices over to farmers practice improved RW system productivity and profitability with less irrigation water and environmental footprints. Layering of CSAPs in RW rotation improved system productivity and profitability by 6 and 19% respectively, and saved 22% of irrigation water and improved WP I and WP I+R by 37 and 26% (3 yrs mean) respectively, compared to farmers practice while reducing 40% global warming potential (GWP). Climate smart agriculture practices were found more adapted to climate risks rather than conventional practices. Layering of 131