Cover crop (CC) residue decomposition influences the provisioning of agroecosystem services. While several laboratory and field studies have investigated processes and mechanisms of CC residue decomposition at specific point or plot scales, regional assessment of factors controlling decomposition rates (i.e., $k$-values) in no-till corn (Zea mays L.) systems are currently lacking. Here, we conducted the first multi-state on-farm litter bag studies over 105 site-years in the mid-Atlantic and Southeastern US states to determine the independent and combined effect of factors intrinsic to the field (soil and weather) and extrinsic or management factors (CC quantity and quality) on $k$-values. In the coastal plain regions, the $k$-values decreased as the underlying soils became sandier. Among weather variables, mean daily air relative humidity (RH) and number of rainy days showed stronger control on $k$-values than cumulative rainfall. This suggests faster decomposition of CC residues in humid environments and in site-years with frequent rain-events. Among extrinsic factors, the $k$-values decreased with higher CC biomass, C:N, residue holo-cellulose concentrations, and lignin:N, but increased with higher residue carbohydrate concentrations. The combination of CC residue quality (C:N and holo-cellulose) and weather (RH and rainy days) variables accounted in total for 69% of the variability in $k$-values with CC residue quality having a greater control over $k$-values than does weather in the mid-Atlantic and Southeastern US states. Therefore, our study emphasizes the necessity to update current process-based decomposition models to explicitly consider both CC residue quality (C:N, holo-cellulose) and weather factors (RH, rainy days), when predicting CC residue decomposition in no-till cropping systems.

覆盖作物 (CC) 残留物分解影响农业生态系统服务的提供。虽然一些实验室和实地研究调查了特定点或地块尺度上 CC 残留物分解的过程和机制，但对控制分解速率的因素（即，$克$-values) 在免耕玉米 ( Zea mays L.) 系统中目前缺乏。在这里，我们在大西洋中部和美国东南部各州进行了超过 105 个站点年的第一次多州农场垃圾袋研究，以确定该领域内在因素（土壤和天气）和外在或管理因素（CC 数量和质量）$克$-值。在沿海平原地区，$克$- 值随着底层土壤变得更沙而下降。在天气变量中，日均空气相对湿度（RH）和雨天数对$克$-值比累积降雨量。这表明在潮湿环境和频繁降雨事件的现场年份中，CC 残留物的分解速度更快。在外在因素中，$克$- 值随着 CC 生物量、C:N、残留全纤维素浓度和木质素:N 的升高而降低，但随着残留碳水化合物浓度的升高而增加。CC 残留质量（C:N 和全纤维素）和天气（RH 和雨天）变量的组合总共占了 69% 的变异性。$克$- 具有 CC 残留质量的值可以更好地控制 $克$- 比大西洋中部和美国东南部各州的天气值。因此，我们的研究强调有必要更新当前基于过程的分解模型，在预测免耕中 CC 残留物分解时，明确考虑 CC 残留物质量（C:N、全纤维素）和天气因素（RH、雨天）种植系统。