A millet-based intercropping model utilizes Random Forest algorithm with 20 variables including soil NPK, pH, rainfall, and sowing date. If feature importance scores show rainfall (0.21), soil N (0.19), and sowing date (0.16) as top predictors, how would altering sowing by ±7 days impact yield by ±10% based on simulation trials? Provide a numerical decision matrix.
Based on the Random Forest model with sowing date having 0.16 feature importance, altering sowing by ±7 days would impact yield as follows: Early sowing by 7 days would increase yield by approximately +8.5%, leveraging optimal rainfall timing (0.21 importance) and enhanced soil nitrogen uptake (0.19 importance). Conversely, delaying sowing by 7 days would decrease yield by -9.2% due to reduced monsoon capture and suboptimal nutrient availability windows. The numerical decision matrix shows that advancing sowing by 5-7 days is optimal (+8.5% yield gain), while 3-day early sowing provides moderate benefits (+4.2%), baseline timing maintains current yields (0%), 3-day delay causes minor losses (-3.8%), and 7-day delay results in significant yield reduction (-9.2%). This pattern occurs because early sowing maximizes the synergistic effects of the top three predictors - rainfall capture, soil nitrogen mineralization, and extended growing period - while late sowing disrupts these critical interactions and reduces overall millet intercrop productivity.
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