Tritrophic defenses as a central pivot of low-emission, pest-suppressive farming systems.

The ongoing COVID-19 pandemic has spotlighted the intricate connections between human and planetary health. Given that pesticide-centered crop protection degrades ecological resilience and (in-)directly harms human health, the adoption of ecologically sound, biodiversity-driven alternatives is imperative. In this Synthesis paper, we illuminate how ecological forces can be manipulated to bolster 'tritrophic defenses' against crop pests, pathogens, and weeds. Three distinct, yet mutually compatible approaches (habitat-mediated, breeding-dependent, and epigenetic tactics) can be deployed at different organizational levels, that is, from an individual seed to entire farming landscapes. Biodiversity can be harnessed for crop protection through ecological infrastructures, diversification tactics, and reconstituted soil health. Crop diversification is ideally guided by interorganismal interplay and plant-soil feedbacks, entailing resistant cultivars, rotation schemes, or multicrop arrangements. Rewarding opportunities also exist to prime plants for enhanced immunity or indirect defenses. As tritrophic defenses spawn multiple societal cobenefits, they could become core features of healthy, climate-resilient, and low-carbon food systems.

Model biases in rice phenology under warmer climates.

Climate-induced crop yields model projections are constrained by the accuracy of the phenology simulation in crop models. Here, we use phenology observations from 775 trials with 19 rice cultivars in 5 Asian countries to compare the performance of four rice phenology models (growing-degree-day (GDD), exponential, beta and bilinear models) when applied to warmer climates. For a given cultivar, the difference in growing season temperature (GST) varied between 2.2 and 8.2 °C in different trials, which allowed us to calibrate the models for lower GST and validate under higher GST, with three calibration experiments. The results show that in warmer climates the bilinear and beta phenology models resulted in gradually increasing bias for phenology predication and double yield bias per percent increase in phenology simulation bias, while the GDD and exponential models maintained a comparatively constant bias. The phenology biases were primarily attributed to varying phenological patterns to temperature in models, rather than on the size of the calibration dataset. Additionally, results suggest that model simulations based on multiple cultivars provide better predictability than using one cultivar. Therefore, to accurately capture climate change impacts on rice phenology, we recommend simulations based on multiple cultivars using the GDD and exponential phenology models.

Crops and climate change: progress, trends, and challenges in simulating impacts and informing adaptation.

Assessments of the relationships between crop productivity and climate change rely upon a combination of modelling and measurement. As part of this review, this relationship is discussed in the context of crop and climate simulation. Methods for linking these two types of models are reviewed, with a primary focus on large-area crop modelling techniques. Recent progress in simulating the impacts of climate change on crops is presented, and the application of these methods to the exploration of adaptation options is discussed. Specific advances include ensemble simulations and improved understanding of biophysical processes. Finally, the challenges associated with impacts and adaptation research are discussed. It is argued that the generation of knowledge for policy and adaptation should be based not only on syntheses of published studies, but also on a more synergistic and holistic research framework that includes: (i) reliable quantification of uncertainty; (ii) techniques for combining diverse modelling approaches and observations that focus on fundamental processes; and (iii) judicious choice and calibration of models, including simulation at appropriate levels of complexity that accounts for the principal drivers of crop productivity, which may well include both biophysical and socio-economic factors. It is argued that such a framework will lead to reliable methods for linking simulation to real-world adaptation options, thus making practical use of the huge global effort to understand and predict climate change.