Higher soil moisture increases microclimate temperature buffering in temperate broadleaf forests

Forest canopies can buffer the understory against temperature extremes, often creating cooler microclimates during warm summer days compared to temperatures outside the forest. The buffering of maximum temperatures in the understory results from a combination of canopy shading and air cooling through soil water evaporation and plant transpiration. Therefore, buffering capacity of forests depends on canopy cover and soil moisture content, which are increasingly affected by more frequent and severe canopy disturbances and soil droughts. The extent to which this buffering will be maintained in future conditions is unclear due to the lack of understanding about the relationship between soil moisture and air temperature buffering in interaction with canopy cover and topographic settings. We explored how soil moisture variability affects temperature offsets between outside and inside the forest on a daily basis, using temperature and soil moisture data from 54 sites in temperate broadleaf forests in Central Europe over four climatically different summer seasons. Daily maximum temperatures in forest understories were on average 2 degrees C cooler than outside temperatures. The buffering of understory temperatures was more effective when soil moisture was higher, and the offsets were more sensitive to soil moisture on sites with drier soils and on sun-exposed slopes with high topographic heat load. Based on these results, the soil-water limitation to forest temperature buffering will become more prevalent under future warmer conditions and will likely lead to changes in understory communities. Thus, our results highlight the urgent need to include soil moisture in models and predictions of forest microclimate, understory biodiversity and tree regeneration, to provide a more precise estimate of the effects of climate change

Pathways from research to sustainable development: Insights from ten research projects in sustainability and resilience

Drawing on collective experience from ten collaborative research projects focused on the Global South, we identify three major challenges that impede the translation of research on sustainability and resilience into better-informed choices by individuals and policy-makers that in turn can support transformation to a sustainable future. The three challenges comprise: (i) converting knowledge produced during research projects into successful knowledge application; (ii) scaling up knowledge in time when research projects are short-term and potential impacts are long-term; and (iii) scaling up knowledge across space, from local research sites to larger-scale or even global impact. Some potential pathways for funding agencies to overcome these challenges include providing targeted prolonged funding for dissemination and outreach, and facilitating collaboration and coordination across different sites, research teams, and partner organizations. By systematically documenting these challenges, we hope to pave the way for further innovations in the research cycle

M3 model as a prototype tool for predicting performance of innovative plant teams Deliverable D3.2 (D23)

This deliverable report describes the applicability of the Minimalist Mixture Model – M3 – as a prototype tool for predicting performance of innovative plant teams. M3 has been developed as part of the work conducted in DIVERSify within WP3. M3, and its most recent further developments, are briefly described. The emphasis is on the general structure of the model, and the data needed for its calibration, running and validation. In addition, this report presents two applications of M3 to predict the performance of existing and novel plant teams. In the first application, M3 is used to determine the performance of plant teams differing from the existing ones for specific parameters, thus helping in the identification of key traits for superior performance, and hence potential innovative plant teams that should be prioritized in breeding. In the second application, M3 is used to determine the stability of a plant team performance in the face of variable climatic conditions, including those projected for the future

Coordinated evaporative demand and precipitation maximize rainfed maize and soybean crop yields in the USA

To understand how climate change affects crop yields, we need to identify the climatic indices that best predict yields. Grain yields are most often predicted using precipitation and temperature in statistical models, assuming linear dependences. However, soil water availability is more influential for plant growth than precipitation and temperature, and there is ecophysiological evidence of intermediate yield maximizing conditions. Using rainfed maize and soybean yields for 1970-2010 across the USA, we tested whether the aridity index, that is, the ratio of precipitation and potential evapotranspiration seasonal totals and a proxy of soil water availability, better predicts yield than growing season precipitation total, average temperature and their interaction. We also tested for non-monotonic responses allowing for intermediate yield-maximizing conditions. The aridity index alone explained 77% and 72% of maize and soybean yield variability, compared with 78% and 73% explained by temperature, precipitation and their interaction. Yield responses were non-monotonic, with yields maximized at intermediate precipitation and temperature as well as at intermediate aridity index of 0.79 for maize and 0.98 for soybean. The yield maximizing precipitation also increased with growing season average temperature, faster in maize than soybean. The intermediate yield maximizing conditions show that rainfed maize and soybean yields could both increase and decrease depending on whether climatic conditions come closer to or deviate from the yield maximizing conditions in the future. In most counties, during 1970-2010, the precipitation and aridity index were lower and temperature higher compared with those maximizing yields, suggesting that climate change will reduce yields

Optimal plant water use strategies explain soil moisture variability

Plant responses to water stress influence water and carbon cycles and can lead to feedbacks on climate yet characterizing these responses at ecosystem levels remains uncertain. Quantifying ecosystem-level water use strategies is complex due to challenges of upscaling plant traits and disentangling confounding environmental factors, ultimately limiting our ability to understand and anticipate global change in ecosystem dynamics and ecohydrological fluxes. We reduce the dimensionality of this problem and quantify plant water use strategies by combining plant traits with soil and climate variables into parameter groups that synthesize key eco-physiological tradeoffs. Using a parsimonious soil water balance framework, we explore variations in plant water uptake capacity, water stress responses, and water use performance via these non-dimensional parameter groups. The group characterizing the synchronization of plant water transport and atmospheric water demand emerges as the primary axis of variation in water use strategies and interacts with the group representing plant hydraulic risk tolerance, especially in arid conditions when plant water transport is limiting. Next, we show that specific plant water use strategies maximize plant water uptake (leading to carbon gain benefits) weighted by risks of water stress (leading to higher costs of water use). A model-data comparison demonstrates that these ecohydrologically optimal parameter groups capture observed soil moisture variability in 40 ecosystems and beyond aridity, rainfall frequency is an important environmental control for plant water use strategies. The emerging parsimonious link between ecohydrological performance and non-dimensional parameters provides a tractable representation of plant water use strategies, relevant to parameterize global models while accounting for ecological and evolutionary constraints on the water cycle

Water management for irrigation, crop yield and social attitudes: a socio-agricultural agent-based model to explore a collective action problem

When rainfall does not meet crop water requirements, supplemental irrigation is needed to maintain productivity. On-farm ponds can prevent excessive groundwater exploitation - to the benefit of the whole community - but they reduce the cultivated area and require investments by each farmer. Thus, choosing the source of water for irrigation (groundwatervson-farm pond) is a problem of collective action. An agent-based model is developed to simulate a smallholder farming system; the farmers' long-/short-view orientation determines the choice of the water source. We identify the most beneficial water source for economic gain and its stability, and how it can change across communities and under future climate scenarios. By using on-farm ponds, long-view-oriented farmers provide collective advantages but have individual advantages only under extreme climates; a tragedy of the commons is always possible. Changes in farmers' attitudes (and hence sources of water) based on previous experiences can worsen the economic outcome

Combined heat and drought suppress rainfed maize and soybean yields and modify irrigation benefits in the USA

Heat and water stress can drastically reduce crop yields, particularly when they co-occur, but their combined effects and the mitigating potential of irrigation have not been simultaneously assessed at the regional scale. We quantified the combined effects of temperature and precipitation on county-level maize and soybean yields from irrigated and rainfed cropping in the USA in 1970-2010, and estimated the yield changes due to expected future changes in temperature and precipitation. We hypothesized that yield reductions would be induced jointly by water and heat stress during the growing season, caused by low total precipitation (P-GS) and high mean temperatures (T-GS) over the whole growing season, or by many consecutive dry days (CDDGS) and high mean temperature during such dry spells (T-CDD) within the season. Whole growing season (T-GS, P-GS) and intra-seasonal climatic indices (T-CDD, CDDGS) had comparable explanatory power. Rainfed maize and soybean yielded least under warm and dry conditions over the season, and with longer dry spells and higher dry spell temperature. Yields were lost faster by warming under dry conditions, and by lengthening dry spells under warm conditions. For whole season climatic indices, maize yield loss per degree increase in temperature was larger in wet compared with dry conditions, and the benefit of increased precipitation greater under cooler conditions. The reverse was true for soybean. An increase of 2 degrees C in T-GS and no change in precipitation gave a predicted mean yield reduction across counties of 15.2% for maize and 27.6% for soybean. Irrigation alleviated both water and heat stresses, in maize even reverting the response to changes in temperature, but dependencies on temperature and precipitation remained. We provide carefully parameterized statistical models including interaction terms between temperature and precipitation to improve predictions of climate change effects on crop yield and context-dependent benefits of irrigation

Application of Crop Growth Models to Assist Breeding for Intercropping: Opportunities and Challenges

Intercropping of two or more species on the same piece of land can enhance biodiversity and resource use efficiency in agriculture. Traditionally, intercropping systems have been developed and improved by empirical methods within a specific local context. To support the development of promising intercropping systems, the individual species that are part of an intercrop can be subjected to breeding. Breeding for intercropping aims at resource foraging traits of the admixed species to maximize niche complementarity, niche facilitation, and intercrop performance. The breeding process can be facilitated by modeling tools that simulate the outcome of the combination of different species' (or genotypes') traits for growth and yield development, reducing the need of extensive field testing. Here, we revisit the challenges associated with breeding for intercropping, and give an outlook on applying crop growth models to assist breeding for intercropping. We conclude that crop growth models can assist breeding for intercropping, provided that (i) they incorporate the relevant plant features and mechanisms driving interspecific plant-plant interactions; (ii) they are based on model parameters that are closely linked to the traits that breeders would select for; and (iii) model calibration and validation is done with field data measured in intercrops. Minimalist crop growth models are more likely to incorporate the above elements than comprehensive but parameter-intensive crop growth models. Their lower complexity and reduced parameter requirement facilitate the exploration of mechanisms at play and fulfil the model requirements for calibration of the appropriate crop growth models

Functional trait space in cereals and legumes grown in pure and mixed cultures is influenced more by cultivar identity than crop mixing

More efficient resource use, especially nitrogen (N) in agricultural fields could considerably reduce the losses and spillover effects on the environment. Cereal-legume mixtures can lead to more efficient uptake of growth-limiting resources, and increase and stabilize yields, due to the variation in functional traits that facilitate partitioning of niche space. Here we identify crop mixtures with functional traits that facilitate optimal N resource use in two selected cereal-legume mixtures by using the multi-dimensional trait space concept. Combinations of pea-barley and faba bean-wheat crops were grown in the field as pure cultures and mixtures in Central Sweden, during two years with contrasting weather. The ecological niche space was defined via the n-dimensional hypervolumes represented by N pool, tiller/branch number, shoot biomass, and grain yield functional traits. Regressions and correlations allowed quantifying the relations between functional traits and plant N pools. Differences in trait space were not a result of crop mixing per se, as similar hypervolumes were found in the pure culture and mixture-grown crops. Instead, the trait space differences depended on the cultivar identities admixed. Furthermore, cereals increased their efficiency for N uptake and therefore benefitted more than the legumes in the mixtures, in terms of accumulated N and grain yields. Tiller and shoot biomass production in cereals was positively correlated to N pool accumulation during the season. Resource acquisition through increased N uptake in the mixture was associated with a reduced overlap in niche-space in the mixtures, and initial seed N pools significantly contributed to within-season N accumulation, shoot and tiller production

Using hybrid modelling to predict basal area and evaluate effects of climate change on growth of Norway spruce and Scots nine stands

When modelling forest growth, capturing the effects of climate change is needed for reliable longterm predictions and management choices. This remains a challenge because commonly used mensurational forest growth and yield models, relying on inventory data, cannot account for climate change effects. We developed hybrid physiological/mensurational basal area growth and yield models, which combine physiological response to climatic conditions and empirical relations. We included climate and site effects by replacing time with light sums of photosynthetically active radiation and modifying the latter with monthly soil water, vapour pressure deficit, temperature, and frost days. When parameterised with permanent sample plot data for Scots pine and Norway spruce across Sweden, the hybrid models could reproduce observations well, although with no increase in precision compared with time-based mensurational models. When considering different climate scenarios, a significant impact on productivity from climate change emerged. For example, a 2 degrees C warming enhanced Scots pine production by up to 14% in regions where temperatures were originally cooler and soil water deficit was low (i.e. northwest Sweden), but depressed it, up to 9%, elsewhere. Hence, climate-sensitive models that take local variations into account are necessary for accurate predictions and sustainable forest management