| Conservation agriculture (CA) is a crop management paradigm based on minimal soil disturbance, crop rotation and year-round cover of soil surfaces by crop residues, and is globally promoted as a system that can increase productivity while improving soil quality. Adoption of CA by smallholder farmers in the mid-hills of Nepal is constrained by potential time lags in materialization of yield benefits, excessive rains during the maize growing season, and dry winter season conditions that incentivize farmers to grow low-residue, short-season rapeseed rather than a high-residue, full-season winter crop. We found that monsoon maize yields are depressed under CA management, but secondary dry-season crops benefited from CA management provided planting dates were optimized. Furthermore, CA management significantly increased soil aggregation bulk density, time-to-pond and surface cracking, but decreased infiltration rate within the first two years after adoption. Subsequent crop simulation modeling using the Decision Support System for Agricultural Transfer (DSSAT) showed that a maize-wheat double crop is possible given early maize planting and/or shorter duration maize varieties. In hot and dry locations, optimal wheat planting occurs in early September but may be limited by heat stress, whereas cooler and wetter locations have optimal planting between October 15th and November 1st. Conservation agriculture productivity showed the greatest gains over conventional management (CP) for late wheat planting dates and at locations with temperature averages of 14°C or less. Also, delaying wheat planting after maize harvest boosted crop yields by allowing for soil water recharge and mineralization of plant residues. In a second set of crops simulations, we show that soil texture and the amounts of residues retained on soil surfaces significantly impact relative productivity of CA compared CP, and that given optimal residue retention rates, CA systems are a less risky production option for smallholder farmers regardless of soil type. Our last study used a meta-analysis of the CA literature and machine learning techniques to identify the drivers and predict the outcome of CA globally. We found that the duration of CA management and weather parameters are the primary drivers of CA productivity, and that CA production is most productive in equatorial zones of the world. |
