AGRONOMIC AND ENVIRONMENTAL BENEFITS OF REINTRODUCING HERB-AND LEGUME-RICH MULTISPECIES LEYS INTO ARABLE ROTATIONS:A REVIEW

Agricultural intensification and the subsequent decline of mixed farming systems has led to an increase in continuous cropping with only a few fallow or break years,undermining global soil health.Arable-ley rotations incorporating temporary pastures(leys) lasting 1–4 years may alleviate soil degradation by building soil fertility and improving soil structure.However,the majority of previous research on arable-ley rotations has utilized either grass or grassclover leys within ungrazed systems.Multispecies leys,containing a mix of grasses,legumes,and herbs,are rapidly gaining popularity due to their promotion in agri-environment schemes and potential to deliver greater ecosystem services than conventional grass or grass-clover leys.Livestock grazing in arable-ley rotations may increase the economic resilience of these systems,despite limited research of the effects of multispecies leys on ruminant health and greenhouse gas emissions.This review aims to evaluate previous research on multispecies leys,highlighting areas for future research and the potential benefits and disbenefits on soil quality and livestock productivity.The botanical composition of multispecies leys is crucial,as legumes,deep rooted perennial plants(e.g.,Onobrychis viciifolia and Cichorium intybus) and herbs(e.g.,Plantago lanceolata) can increase soil carbon,improve soil structure,reduce nitrogen fertilizer requirements,and promote the recovery of soil fauna(e.g.,earthworms) in degraded arable soils while delivering additional environmental benefits(e.g.,biological nitrification inhibition and enteric methane reduction).Multispecies leys have the potential to deliver biologically driven regenerative agriculture,but more long-term research is needed to underpin evidence-based policy and farmer guidance.

Panicle-Cloud:An Open and Al-Powered Cloud Computing Platform for Quantifying Rice Panicles from Drone-Collected Imagery to Enable the Classification of Yield Production in Rice

Rice(Oryza sativa)is an essential stable food for many rice consumption nations in the world and,thus,the importance to improve its yield production under global climate changes.To evaluate different rice varieties'yield performance,key yield-related traits such as panicle number per unit area(PNpM^(2))are key indicators,which have attracted much attention by many plant research groups.Nevertheless,it is still challenging to conduct large-scale screening of rice panicles to quantify the PNpM^(2)trait due to complex field conditions,a large variation of rice cultivars,and their panicle morphological features.Here,we present Panicle-Cloud,an open and artificial intelligence(AI)-powered cloud computing platform that is capable of quantifying rice panicles from drone-collected imagery.To facilitate the development of Al-powered detection models,we first established an open diverse rice panicle detection dataset that was annotated by a group of rice specialists;then,we integrated several state-of-the-art deep learning models(including a preferred model called Panicle-AI)into the Panicle-Cloud platform,so that nonexpert users could select a pretrained model to detect rice panicles from their own aerial images.We trialed the Al models with images collected at different attitudes and growth stages,through which the right timing and preferred image resolutions for phenotyping rice panicles in the field were identified.Then,we applied the platform in a 2-season rice breeding trial to valid its biological relevance and classified yield production using the platform-derived PNpM^(2)trait from hundreds of rice varieties.Through correlation analysis between computational analysis and manual scoring,we found that the platform could quantify the PNpM^(2)trait reliably,based on which yield production was classified with high accuracy.Hence,we trust that our work demonstrates a valuable advance in phenotyping the PNpM^(2)trait in rice,which provides a useful toolkit to enable rice breeders to screen and select desired rice varieties under field conditions.