Can Intercropping Increase Climate Resilience of Smallholder Dryland Cropping Systems? Insights from Experimentation and Modelling

With the potential threat of more frequent climate extremes putting semi-arid crop production in jeopardy, there is a need to establish more resilient cropping systems. Intercropping is often practised by farmers in such regions, but to what extent and how can it be a viable option for future conditions? As field testing complex adaptation strategies in well-controlled environments is often difficult, we opted for a different approach: combining experimentation and modelling. A field trial was run in semi-arid India over a two-year period (2015 & 2016) in the dry season. These trials tested a split-plot designed experiment with four replicates, assessing the performance of sole versus intercropped stands, with two densities (30 cm & 60 cm between row spacing), and three drip irrigation treatments (severe stress, partial stress, and well-watered). Under low rainfall conditions, results showed that total grain yields were in-line with the irrigation treatments applied. Intercropping pearl millet led to a significantly lower total grain yield in comparison to the sole equivalent. Pearl millet achieved 1.1 t/ha when intercropped and 2.5 t/ha when grown as a sole crop. Cowpea yielded 0.8 t/ha when intercropped, and 0.8 t/ha as a sole crop. From this study, we can conclude that even when temperatures exceed 43 C crops produce reasonable yields when irrigated. In terms of pearl millet production, sole as opposed to intercrop cultivation could be more suitable. Such experiments during the dry season are arguably an opportunity for testing cropping strategies under extreme but real-world conditions. Subsequently, we used the above-described detailed data in conjunction with the agroecosystem model APSIM. Linking such experimental data with models is important to evaluate models that can be applied to explain and quantify the performance of such systems. Model performance was satisfactory and eproduced the effects of density, irrigation and year on the variables chosen. Plant height proved to be crucial for model evaluation. Simulation experiments were conducted to further evaluate plant densities, as well as genetic traits. Our combined approach is capable of improving intercropping strategies, through understanding the processes that determine interactions between specific environments and management practices

Multimodel Ensembles of Wheat Growth: More Models are Better than One

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models

Priority questions in multidisciplinary drought research

Addressing timely and relevant questions across a multitude of spatio-temporal scales, state-of-the-art interdisciplinary drought research will likely increase in importance under projected climate change. Given the complexity of the various direct and indirect causes and consequences of a drier world, scientific tasks need to be coordinated efficiently. Drought-related research endeavors ranging from individual projects to global initiatives therefore require prioritization. Here, we present 60 priority questions for optimizing future drought research. This topical catalogue reflects the experience of 65 scholars from 21 countries and almost 20 fields of research in both natural sciences and the humanities. The set of drought-related questions primarily covers drought monitoring, impacts, forecasting, climatology, adaptation, as well as planning and policy. The questions highlight the increasingly important role of remote sensing techniques in drought monitoring, importance of drought forecasting and understanding the relationships between drought parameters and drought impacts, but also challenges of drought adaptation and preparedness policies

Modeling the multi-functionality of African savanna landscapes under global change

Various recent publications have indicated that accelerated global change and its negative impacts on terrestrial ecosystems in Southern Africa urgently demand quantitative assessment and modelling of a range of ecosystem services on which rural communities depend. Information is needed on how these Ecosystem Services (ES) can be enhanced through sustainable land management interventions and enabling policies. Yet, it has also been claimed that, to date, the required system analyses, data and tools to quantify important interactions between biophysical and socio-economic components, their resilience and ability to contribute to livelihood needs do not exist. We disagree, but acknowledge that building an appropriate integrative modelling framework for assessing the multi-functionality of savanna landscapes is challenging. Yet, in this Letter-to-the-Editor, we show that a number of suitable modelling components and required data already exist and can be mobilized and integrated with emerging data and tools to provide answers to problem-driven questions posed by stakeholders on land management and policy issues.German Federal Ministry of Education and Researchhttps://onlinelibrary.wiley.com/journal/1099145xhj2022Zoology and Entomolog

Cocoa agroforestry is less resilient to sub-optimal and extreme climate than cocoa in full sun

Published online: 28 Sept 2017Cocoa agroforestry is perceived as potential adaptation strategy to sub-optimal or adverse environmental conditions such as drought. We tested this strategy over wet, dry and extremely dry periods comparing cocoa in full sun with agroforestry systems: shaded by (i) a leguminous tree species, Albizia ferruginea and (ii) Antiaris toxicaria, the most common shade tree species in the region. We monitored micro-climate, sap flux density, throughfall, and soil water content from November 2014 to March 2016 at the forest-savannah transition zone of Ghana with climate and drought events during the study period serving as proxy for projected future climatic conditions in marginal cocoa cultivation areas of West Africa. Combined transpiration of cocoa and shade trees was significantly higher than cocoa in full sun during wet and dry periods. During wet period, transpiration rate of cocoa plants shaded by A. ferruginea was significantly lower than cocoa under A. toxicaria and full sun. During the extreme drought of 2015/16, all cocoa plants under A. ferruginea died. Cocoa plants under A. toxicaria suffered 77% mortality and massive stress with significantly reduced sap flux density of 115 g cm−2 day−1, whereas cocoa in full sun maintained higher sap flux density of 170 g cm−2 day−1. Moreover, cocoa sap flux recovery after the extreme drought was significantly higher in full sun (163 g cm−2 day−1) than under A. toxicaria (37 g cm−2 day−1). Soil water content in full sun was higher than in shaded systems suggesting that cocoa mortality in the shaded systems was linked to strong competition for soil water. The present results have major implications for cocoa cultivation under climate change. Promoting shade cocoa agroforestry as drought resilient system especially under climate change needs to be carefully reconsidered as shade tree species such as the recommended leguminous A. ferruginea constitute major risk to cocoa functioning under extended severe drought

Multimodel Ensembles of Wheat Growth: Many Models are Better than One

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop model scan give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 2438 for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models

Evidence for increasing global wheat yield potential

Wheat is the most widely grown food crop, with 761 Mt produced globally in 2020. To meet the expected grain demand by mid-century, wheat breeding strategies must continue to improve upon yield-advancing physiological traits, regardless of climate change impacts. Here, the best performing doubled haploid (DH) crosses with an increased canopy photosynthesis from wheat field experiments in the literature were extrapolated to the global scale with a multi-model ensemble of process-based wheat crop models to estimate global wheat production. The DH field experiments were also used to determine a quantitative relationship between wheat production and solar radiation to estimate genetic yield potential. The multi-model ensemble projected a global annual wheat production of 1050 ± 145 Mt due to the improved canopy photosynthesis, a 37% increase, without expanding cropping area. Achieving this genetic yield potential would meet the lower estimate of the projected grain demand in 2050, albeit with considerable challenges