Reducing herbicide use and leaching in agronomically performant maize-based cropping systems: An 8-year study

Irrigated maize-based Cropping Systems (CS) are questioned because of the high risk of herbicide transfer to water. An 8-year systemic experiment was conducted to i) compute a multi-performance comparison between a Conventional Maize Monoculture (MM_Conv) and four CS that aimed to reduce irrigation and herbicide leaching: MM_LI, a low-input MM using cover crop and Integrated Weed Management (IWM) techniques; MM_Still, a Strip-tillage MM using cover crop; MM_CT, a Conservation Tillage MM with cover crop; Maize-MSW, an IWM Maize rotated with Soybean and Wheat and ii) determine the main drivers and evaluate the influence of CS on herbicide leaching in maize. Drainage water was collected through 1-m depth lysimeter plates and analysed for 6 herbicide molecules and 1 degradation metabolite. MM_LI yielded 10.7 t ha^-1 close to MM_Conv (11.5 t ha^-1) despite a lower herbicide use (-57%) and irrigation (-21%). MM_LI and Maize-MSW had less drainage events compared to MM_Conv. MM_CT and MM_Still both yielded less (respectively 7.6 t ha^-1 and 6.2 t ha^-1) while their herbicide use increased (both +24%). Mean annual herbicide losses were 0.5 ± 1.0 g ha^-1 for MM_LI, 0.7 ± 1.2 g ha^-1 for Maize-MSW, 1.3 ± 2.1 g ha^-1 for MM_Still, 2.0 ± 4.8 g ha^-1 MM_Conv and 3.0 ± 9.6 g ha^-1 for MM_CT. Herbicide leaching remained variable but was consistently and mostly influenced by drainage volume. According to the CS, only 1.5 to 6.0 drainage events were responsible for 90% of the herbicide losses. High leaching peaks were identified for mesotrione and glyphosate and may indicate that preferential flows occurred, especially under MM_CT. Quantity applied had limited influence on herbicide leaching. To reduce the herbicide leaching risk, CS must concomitantly manage water quality and quantity through a combination of agroecological practices, as in MM_LI, a CS able to reach other technical objectives. Present study recommends assessing CS through a diversity of performance indicators.