Sustainable cocoa production underpins rural livelihoods across the tropics and offers significant potential for both climate change adaptation and mitigation. Although the biodiversity value of cocoa agroforestry systems is well documented, their functional capacity to buffer drought stress and enhance climate resilience remains insufficiently understood. In particular, scalable methods for monitoring plant physiological responses to water stress across structurally heterogeneous agroforestry landscapes are urgently needed. In this study, we assess the potential of multispectral drone imagery to detect drought-related physiological dynamics in cocoa agroforestry systems, with a specific emphasis on the role of shade tree leaf phenology.We integrated high-resolution multispectral drone imagery with in situ physiological measurements across ten smallholder cocoa plantations of comparable age in the northern cocoa belt of Ghana. Thirteen shade tree species, representing distinct functional groups based on leaf phenology, were selected. For eight individuals per species, we quantified structural traits (diameter at breast height, tree height, and canopy area) and phenological status, and measured leaf-level transpiration and stomatal conductance using a LI-600 porometer. Multispectral imagery acquired during the late wet, mid-wet, and peak dry seasons between 2021 and 2023 was used to derive the Green Normalized Difference Vegetation Index (GNDVI), a spectral proxy sensitive to chlorophyll content and photosynthetic activity. We observed pronounced seasonal and functional-group-specific differences in canopy reflectance, with significant interactions between season and shade tree phenology. GNDVI was strongly correlated with key physiological traits, particularly stomatal conductance, and exhibited consistent responses to seasonal climatic variation. These results demonstrate that drought-induced physiological stress, expressed as reductions in stomatal conductance, can be reliably predicted from spectral traits derived from high-resolution multispectral drone imagery, highlighting its potential as a scalable tool for assessing drought resilience in cocoa agroforestry systems.

Cocoa agroforestry systems are recognized for their potential to reconcile agricultural production with climate change mitigation, yet the mechanisms governing carbon storage across environmental gradients remain poorly understood. Here, we examined above- and belowground carbon stocks of cocoa and shade trees as well as soil organic carbon (SOC) in smallholder cocoa agroforestry systems across dry, intermediate (mid), and wet climatic zones in Ghana. Despite pronounced differences in shade tree density, size, and species richness, shade-tree aboveground carbon stocks (108–147 Mg C ha−1) as well as total farm-level carbon stocks were comparable across the climatic gradient (from dry to wet), with no negative effect on cocoa yield, signifying trade-offs in the structural characteristics of shade trees driven by management. Cocoa tree carbon and SOC stocks varied across the climatic zones but individually contributed less to ecosystem carbon stocks. Cocoa tree carbon stocks were highest in the mid climatic zone (41 Mg C ha−1) compared to 22 Mg C ha−1 for the dry and 27 Mg C ha−1 for the wet zone, reflecting the older cocoa tree stands and larger stem diameters of the mid zone. SOC was lower in the dry zone than in the mid and wet zones, by 23 and 27

Crop models are used within the GGCMI-AgMIP framework to assess climate impacts on crop yields, but their ability to reproduce observed yield responses to regional climatic variability and extremes remains insufficiently evaluated. To help fill this evaluation gap, we use the North China Plain (NCP), China, as a regional case study and combine prefecture-level winter wheat yield and climate records with GGCMI Phase 2 simulations. The analysis focuses on three growing-season climatic drivers: mean temperature, precipitation, and extreme heat days (EHD), defined as growing-season days with daily maximum temperature exceeding 34 °C. Rather than evaluating yield levels alone, we compare effective yield–climate response types for these climatic drivers, using observed responses as a benchmark for diagnosing model behaviour. We use targeted JULES-crop experiments to test whether a heat-stress phenology modification can reduce the diagnosed bias in simulated extreme-heat responses. Observations show a widespread significant negative linear yield response to EHD in the central NCP, whereas responses to growing-season mean temperature and precipitation are more spatially heterogeneous. Most GGCMI models fail to reproduce this observed response type to extreme heat, suggesting a systematic weakness in the representation of heat-stress impacts. The original JULES-crop configuration fails to capture significant negative linear yield-EHD relationships at the observed heat-sensitive sites. Incorporating heat-stress effects into phenological development and senescence improves this functional response in JULES-crop, increasing the number of sites with a consistent significant negative linear yield-EHD relationship from 0 to 7 out of 16 observed significant sites. Although this targeted modification does not eliminate all model-observation discrepancies, it demonstrates that phenological sensitivity to extreme heat is a plausible process-level source of model bias. This study extends GGCMI Phase 2 evaluation from broad-scale yield-level comparison towards regional, response-based diagnosis, and identifies heat-stress phenology as a practical pathway for improving winter wheat simulations under intensifying heat extremes.

Continuous long-term simulations of an ensemble of nine crop models covering the 1961-2080 period was employed to assess the expected impacts of climate change on the crop yield and water use for distinct crop rotations (CRs) in Europe. In this study, the likelihood of changes in two differently managed CRs (conventional and alternative) involving four important field crops (winter wheat, spring barley, silage maize, and winter oilseed rape) was assessed. The conventional agricultural practice (CR1) included only mineral fertilization with the removal of crop residues after harvest. The alternative agricultural practice (CR2) included cover crops and the application of mineral and organic fertilizers, with crop residues retained in the field. The simulations covered six sites in five European countries (M & uuml;hldorf and M & uuml;ncheberg in Germany, Ukkel in Belgium, & Oslash;dum in Denmark, Milhostov in Slovakia and Lednice in Czechia) based on two distinct soil profiles (universal soil and site-specific soils). The universal soil was the same across all the sites, while the site-specific soils were typical of each region. Eight transient climate change scenarios (4 general circulation models (GCMs) under representative concentration pathways (RCPs) 2.6 and 8.5) were used to capture the possible evolution of future climatic conditions. Compared with those during the 1962-1990 period, the ensemble projections for the 2051-2080 period indicated average increases in the annual yields of all crops of 0.7 t/ha (RCP 2.6) and 0.8 t/ha (PCP 8.5) under both CRs and soil types. Under most climate change scenarios, the crop model ensemble projections of the winter wheat and winter oilseed rape yield increases agreed for CR2 but not for CR1. For spring barley, the simulated increase was more sporadic, with no significant difference between CR1 and CR2. In regard to silage maize, the changes in the simulated yields depended on site-specific climatic conditions. If the same varieties were planted in the future, yield reductions would be expected, except at the & Oslash;dum site, where the silage maize growth conditions would remain satisfactory, regardless of the CR and soil type. The results indicated greater cover crop biomass production, which could affect the long-term soil water balance and groundwater replen- ishment. The crop model ensemble further indicated a greater spatial variability in the yield can be expected, which is likely caused by the expected increase in the air temperature and not by the expected increase, or even decrease, in the total precipitation and increases in the actual evapotranspiration under climate change at all sites. This trend was greater under CR2 and could affect the long-term soil water balance and soil regime in the case of rainfed agriculture.

Analysing root traits to identify below ground acquisition mechanisms and relating them to above ground traits, such as leaf phenology, can improve the understanding and design of resource use efficiency in drought resilient agroforestry systems. Shade trees play a key role in regulating above and below ground resource use dynamics in agroforestry systems. Specific shade tree functional traits such as leaf phenological development, crown architecture and leaf traits such as specific leaf area and nitrogen content have been related to shade tree impact on productivity, ecosystems service provision and drought resilience of agroforestry systems. Understanding the influence of many different shade tree species on resource use and the productivity outcome resulting from their interaction with cocoa plants have, so far, mainly focused on aboveground traits. Yet, there is an urgent need to put adequate emphasis to the equally important belowground processes, root systems, and root-rhizosphere interactions. Root trait research is significantly limited in tropical communities, constraining understanding of belowground processes and interactions within complex systems such as agroforestry. There is lack of understanding of strategies in belowground resource acquisition among functional groups of shade tree species. In this study, two key roots traits, i.e. fine root length density and fine root diameter of 13 common shade trees species belonging to 6 functional (leaf phenology) groups and cocoa were evaluated under farmer field conditions. Fine root samples were acquired for 4 replicates of each shade tree species through extensive root coring up to 60 cm depth and at three horizontal shade tree impact zones (inner, mid and outer). Scanned sorted shade tree and cocoa plant root images were analysed with WINRHIZO. All cocoa plants irrespective of their associated shade tree functional group exhibited resource acquisitive (non-conservative) fine root traits, i.e. with higher root length density and smaller diameter. Similarly, shade trees in the ‘brevi deciduous during dry season’ functional group exhibited the notable paradox of leaf flushing during dry season characterized by higher, leaf area-related, water uptake in the dry season exhibited non-conservative root traits. Evergreen and complete deciduous functional groups showed a conservative root trait showing lower fine root length density and larger diameter. Shade trees with conservative root traits are therefore considered complementary to the cocoa plant acquisitive traits, thereby enhancing belowground resource use efficiency and drought resilience in cocoa agroforestry systems.

Agroforestry has the potential to enhance climate change adaptation. While benefits from agroforestry systems consisting of cash crops and shade trees are usually attributed to the (shade) trees, the trees can also have negative impacts due to resource competition with crops. Our hypothesis is that leaf phenology and height of shade trees determine their seasonal effect on crops. We test this hypothesis by categorizing shade tree species into functional groups based on leaf phenology, shade tree canopy height and shade tree light (wet and dry season) interception as well as the effects. To this end, leaf phenology and the effects on microclimate (temperature, air humidity, intercepted photoactive radiation (PAR)), soil water, stomatal conductance and cocoa yield were monitored monthly during wet and dry seasons over a two-year period on smallholder cocoa plantations in the northern cocoa belt of Ghana. Seven leaf phenological groups were identified. In the wet season, highest buffering effect of microclimate was recorded under the trees brevi-deciduous before dry season. During dry season, high PAR and lowest reduction in soil moisture were observed under the trees in the group of completely deciduous during dry season. The evergreen groups also showed less reduction in soil water than the brevi-deciduous groups. In the wet season, shade tree effects on cocoa tree yields in their sub canopy compared to the respective control of outer canopy with full sun ranged from positive (+10 %) to negative (-15 %) for the deciduous groups, while yield reductions for the evergreen groups ranged from -20 % to -33 %. While there were negative yield impacts for all phenological groups in the dry season, the trees in completely deciduous during dry season group recorded least penalties (-12 %) and the trees with evergreen upper canopy the highest (-35 %). The function of shade trees in enhancing climate resilience is therefore strongly dependent on their leaf phenological characteristics. Our study demonstrates how the key trait leaf phenology can be applied to successful design of climate-resilient agroforestry systems.

Sustainable water management and enhanced irrigation efficiency in the growing macadamia sector in subtropical regions such as South Africa are essential amid severe periodic water scarcity exacerbated by climate change. This requires precise modelling of tree water demand under varying conditions. However, current methods often lack accuracy or require extensive data inputs. In this study, we adopted and evaluated a simple, mechanistic, low data-input transpiration model for macadamia trees. To this end, we conducted a comprehensive experimental study in the sub-humid Levubu region, South Africa, collecting tree sap velocity data from two macadamia cultivars, along with microclimate and soil water data over two seasons. First, the model was calibrated under non-limiting water conditions using data on tree-intercepted radiation, vapor pressure deficit (VPD), and canopy conductance to simulate potential tree transpiration (Td), representing the upper limit of macadamia water use. Secondly, we further developed the model to simulate Td for water deficit conditions by rescaling simulated potential Td based on the observed fraction of transpirable soil water (FTSW). The performance of the calibrated model was validated against observed Td from (spared) independent datasets for both cultivars. Observed macadamia Td showed pronounced seasonal variability (ranging from 0.6 mm d- 1 in winter to 1.3 mm d- 1 in summer), largely influenced by varying VPD and FTSW. The model captured the strong response of stomatal closure to increasing VPD, reflecting the conservative water use of macadamia trees. Model performance was satisfactory for both cultivars, and under both non-limiting and water deficit conditions, with lower relative error measures in the latter. This indicates that the improved model under water deficit is well-suited for accurately estimating macadamia Td under heterogeneous environmental conditions, making it a valuable tool for optimizing irrigation practices and conserving water resources in macadamia orchards.

This paper describes the dataset that was used to test the reliability of eight crop models in simulating growth and yield of canola in response to sowing dates, nitrogen inputs and climate variability across five countries. The dataset includes four spring cultivars and three winter cultivars across six sites, which represents a diverse range of canola production areas around the world. Model calibration and validation were conducted in the framework of the Agricultural Model Intercomparison and Improvement Project for canola (AgMIP-Canola). Field experimental datasets include site characterization, soil profile characterization, initial soil conditions (soil water and mineral nitrogen contents), in-season and end-season crop measurements (phenology, LAI, biomass, and nitrogen content in leaves, stems and pods, some with seed oil content), and daily weather data. Simulation datasets include the simulation results generated by ten individual model frameworks (eight crop models, APSIM and DSSAT respectively by two groups) for the experimental periods, and scenario simulations using 30 years historical weather data (1981 – 2010) together with a full multi-factorial combinations of temperature (-3, 0, +3, +6, +9 oC), rainfall (-25%, -10%, 0, +10%, +25%), CO2 concentrations (360, 450, 540, 630, 720 ppm) and nitrogen input rates (0, +25%, +50%, +100%, +150%).

The organic matter stored in soils is a major carbon pool with fundamental importance for the global atmospheric carbon balance. Its decomposition contributes not only to the emission of greenhouse gases into the atmosphere but also to the release of minerals that serve as nutrients for plants growing in these soils. SOC is an indicator of soil fertility, reflecting the influences of agricultural practices on this property. Using crop models, the amount of soil organic carbon (SOC) can be simulated under the assumption of different climate scenarios and different agricultural practices. The conventional agricultural practice in Czechia includes short crop rotations of mainly cereals and oil-seed rape, mineral fertilisation and removal of crop residues for technical and energy use. However, the conventional approach is often associated with soil degradation and constant depletion of soil carbon stocks. Based on the standard crop rotation method, we compared the conventional practice (CR1) to an alternative practice (CR2), in which more effort is made towards stabilising soil carbon stocks by including cover crops in the rotation, organic fertilizers and leaving crop residues in the field. We used an ensemble of crop models (APSIM, DAISY, DSSAT, HERMES, and MONICA) to assess the carbon loss from two typical agricultural soils (Chernozem and Cambisol) at three locations in Czechia under current and future climate conditions (RCP 8.5, as represented by five global climate models). The ensemble simulations revealed that using CR2 could lead to an average increase in the SOC content by 15.427 kg/ha for Chernozem and 12.624 kg/ha for Cambisol until 2080. With the use of CR1 the SOC values on average decreased by 34.462 kg/ha for Chernozem and 24.096 kg/ha for Cambisol until 2080. The 1990 value was taken as the SOC reference level. Furthermore, both the increase (CR2) and decrease (CR1) amounts SOC stabilised after 2050. As such, even at the cost of high levels of nitrogen fertilisation and the associated risk of nitrogen leaching (CR2), the additional carbon that can be stored in soils is limited. The differences due to the different climate models are negligible in the case of CR1, while in the case of CR2, the different climate scenarios (baseline vs. future) yielded different SOC equilibrium levels, with a lower level (by 4.400 kg/ha on average) under the RCP 8.5 scenario for both soils. The results showed that carbon can be sequestered by increasing organic inputs. The crop models predicted that CR2 could lead to a higher SOC content, which occurs at the cost of high manure application levels and increased risk of nitrogen leaching.

Crop models are often used to project future crop yield under climate and global change and typically show a broad range of outcomes. To understand differences in modeled responses, we analyzed modeled crop yield response types using impact response surfaces along four drivers of crop yield: carbon dioxide (C), temperature (T), water (W), and nitrogen (N). Crop yield response types help to understand differences in simulated responses per driver and their combinations rather than aggregated changes in yields as the result of simultaneous changes in various drivers. We find that models' sensitivities to the individual drivers are substantially different and often more different across models than across regions. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response types across models and their configurations suggests that models need to undergo further scrutiny. We suggest establishing standards in model evaluation based on emergent functionality not only against historical yield observations but also against dedicated experiments across different drivers to analyze emergent functional patterns of crop models. Crop models are widely used to compute crop yields under future climate change. Yields are determined by many interacting processes. Simulated future crop yields often show a broad uncertainty range. We investigate the sensitivity of nine different crop models to individual model inputs (carbon dioxide, temperature, water, nitrogen) in a very large simulation data set and find that there are substantial differences. We conclude that crop model evaluation needs to include analyses of functional properties to avoid that very diverse model responses to drivers are not tracked if interacting processes cancel out in the historical evaluation period but not in future scenarios, leading to large differences between models. Crop models show strong differences in input sensitivities Standardized modeling experiments reveal differences in emergent functional relationships New standards in model evaluation are needed

AbstractIn this chapter, we explore how, in the face of increasing climatic risks and resource limitations, improved agro-technologies can support sustainable intensification (SI) in small-scale farming systems in Limpopo province, South Africa. Limpopo exhibits high agro-ecological diversity and, at the same time, is one of the regions with the highest degree of poverty and food insecurity in South Africa. In this setting, we analyze the effects of different technology changes on both food security dimensions (i.e., supply, stability, and access) and quality of ecosystem service provision. This is conducted by applying a mixed-method approach combining small-scale farmer survey data, on-farm agronomic sampling, crop growth simulations, and socioeconomic modeling. Results for a few simple technology changes show that both food security and ecosystem service provision can be considerably improved when combining specific technologies in a proper way. Furthermore, such new “technology packages” tailored to local conditions are economically beneficial at farm level as compared to the status quo. One example is the combination of judicious fertilizer application with deficit or full irrigation in small-scale maize-based farming systems. Provided comparable conditions, the results could be also beneficial for decision-makers in other southern African countries.

Macadamia is a high value tree nut crop experiencing a global rise in demand. South Africa is the worldwide largest producer and macadamia orchards are rapidly expanding in the country. However, yields are highly variable across years and have been declining in recent years. Therefore, to sustainably increase the productivity and resilience of macadamia orchards to climate change, the impact of environmental factors on the trees' vegetative and reproductive cycles needs to be better understood. To this end, the extent to which orchard characteristics, climatic and soil factors, as well as irrigation amounts drive macadamia yields in Levubu (Limpopo Province, South Africa) along an altitudinal gradient (600-950 m a.s.l.), was quantified by using mixed-effects models. Climatic variables were selected separately for the different macadamia phenological stages, in which they potentially affected yields. Furthermore, the effects of different yield determining factors were compared between irrigated and rainfed orchards. A pronounced interannual variability of macadamia yields was found (from 1.2 to 4.0 tons dry nut-in-shell ha-1), although a triennial bearing pattern was observed. Higher yields were found at elevations >700 m a.s. l. and in micro-sprinkler irrigated orchards. Orchard characteristics and environmental variables jointly explained 49% of yield variability. Cultivar, presence of irrigation, tree age and planting density were found to affect yield, while no significant effect was found for soil variables (i.e. soil texture, bulk density, pH and C:N ratio). High temperatures and low global radiation during the nut development stages, alongside poor rainfall amounts in the dry season, were the climatic factors more severely affecting yields. In particular, low irradiance was the main yield limiting factor in irrigated orchards, while extremely high temperatures and poor rainy seasons in rainfed orchards. Increased irrigation amounts, although beneficial, were not fully compensating the impact of climatic factors on productivity. Our findings suggest that irrigation alone cannot counteract adverse climatic effects on macadamia yields. To sustainably increase macadamia productivity and resilience to climate change, abiotic stress impacts will have to be reduced through a combination of genetic improvement and better orchard management practices.

Crop models are often employed to project crop yields under changing conditions such as global warming and associated management change for adaptation. Multi-model ensembles are promoted to enhance the robustness of projections, but questions remain on what causes often large differences between projections of individual models. Global Gridded Crop Models (GGCMs) are especially exposed to this question when applied for assessing climate change impacts, adaptation, environmental impacts of agricultural production, because their results are used in downstream analyses, such as in integrated assessment or economic modeling for projecting future land-use change. Even though global gridded crop models are often based on detailed field-scale models or have implemented similar modeling principles in other ecosystem models, global-scale models are subject to substantial uncertainties from both model structure and parametrization as well as from calibration and input data quality. AgMIP’s Global Gridded Crop Model Intercomparison (GGCMI) has thus set out to intercompare GGCMs in order to evaluate model performance, describe model uncertainties, identify inconsistencies within the ensemble and underlying reasons, and to ultimately improve models and modeling capacities. In phase 2 of the GGCMI activities, 12 modeling groups followed a modeling protocol that asked for up to 1404 31-year global simulations at 0.5 arc-degree spatial resolution to assess models’ sensitivities to changes in carbon dioxide (C; 4 different levels) temperature (T; 7 different offset levels), water supply (W; 9 levels), and nitrogen (N; 3 levels), the so-called CTWN experiment (Franke et al. 2020; http://dx.doi.org/10.5194/gmd-13-2315-2020). We here present analyses of model response types using impact response surfaces along the C, T, W, and N dimensions, respectively and collectively. Doing so, we can understand differences in simulated responses per driver rather than aggregated changes in yields. We find that models’ sensitivities to the individual driver dimensions are substantially different and often more different across models than across regions. A cluster analysis finds regional and model-specific patterns. There is some agreement across models with respect to the spatial patterns of response types but strong differences in the distribution of response type clusters across models suggests that models need to undergo further scrutiny. We suggest establishing standards in model process evaluation not only against historical dynamics but also against dedicated experiments across the CTWN dimensions.

This is an open access article under the terms of the Creat ive Commo ns Attri butio nNonCo mmerc ialNoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is noncommercial and no modifications or adaptations are made. © 2021 The Authors. Journal of Agronomy and Crop Science published by Wiley-VCH GmbH 1Tropical Plant Production and Agricultural Systems Modelling (TROPAGS), GeorgAugustUniversität Göttingen, Göttingen, Germany 2Center of Biodiversity and Sustainable Land Use (CBL), GeorgAugustUniversität Göttingen, Göttingen, Germany 3AGVOLUTION GmbH, Göttingen, Germany 4Institut de Recherche pour le Développement (IRD), Université de Montpellier, UMR DIADE, Montpellier, France 5International Crop Research Institute for the SemiArid Tropics (ICRISAT), Dar es Salaam, Tanzania

Smallholder farming systems in southern Africa are characterized by low-input management and integrated livestock and crop production. Low yields and dry-season feed shortages are common. To meet growing food demands, sustainable intensification (SI) of these systems is an important policy goal. While mixed crop–livestock farming may offer greater productivity, it implies trade-offs between feed supply, soil nutrient replenishment, soil carbon accumulation, and other ecosystem functions (ESFs) and ecosystem services (ESSs). Such settings require a detailed system understanding to assess the performance of prevalent management practices and identify potential SI strategies. Models can evaluate different management scenarios on extensive spatiotemporal scales and help identify suitable management strategies. Here, we linked the process-based models APSIM (Agricultural Production Systems sIMulator) for cropland and aDGVM2 (Adaptive Dynamic Global Vegetation Model) for rangeland to investigate the effects of (i) current management practices (minimum input crop–livestock agriculture), (ii) an SI scenario for crop production (with dry-season cropland grazing), and (iii) a scenario with separated rangeland and cropland management (livestock exclusion from cropland) in two representative villages of the Limpopo Province, South Africa, for the period from 2000 to 2010. We focused on the following ESFs and ESSs provided by cropland and rangeland: yield and feed provision, soil carbon storage, cropland leaf area index (LAI), and soil water. Village surveys informed the models of farming practices, livelihood conditions, and environmental circumstances. We found that modest SI measures (small fertilizer quantities, weeding, and crop rotation) led to moderate yield increases of between a factor of 1.2 and 1.6 and reduced soil carbon loss, but they sometimes caused increased growing-season water limitation effects. Thus, SI effects strongly varied between years. Dry-season crop residue grazing reduced feed deficits by approximately a factor of 2 compared with the rangeland-only scenario, but it could not fully compensate for the deficits during the dry-to-wet season transition. We expect that targeted deficit irrigation or measures to improve water retention and the soil water holding capacity may enhance SI efforts. Off-field residue feeding during the dry-to-wet season transition could further reduce feed deficits and decrease rangeland grazing pressure during the early growing season. We argue that integrative modeling frameworks are needed to evaluate landscape-level interactions between ecosystem components, evaluate the climate resilience of landscape-level ecosystem services, and identify effective mitigation and adaptation strategies.

Increases in temperature and more erratic rainfall patterns due to climate change threaten the already fragile livelihood of smallholder coffee farmers. Shaded coffee in agroforestry systems appear to be a good alternative to protect coffee from extreme temperatures while providing additional ecosystem services, such as extra food and soil protection. However, excessive shade might reduce coffee yields. This study analyzed the effect of shade cover (shade type represented by cropping system (Coffee-Open (CO), Coffee-Banana (CB), Coffee-shade tree (CT))), and shade intensity (represented by leaf area index of the shade cover) along an altitude gradient on: (i) microclimate, (ii) soil water content and (iii) coffee reproductive and vegetative growth. Data was collected during two coffee fruit development cycles (2015 and 2016) in smallholder coffee farms (n = 27) on the west slopes of Mt. Elgon Uganda. Shade cover buffered coffee trees from microclimate extremes (maximum temperature (-3 C) and temperature amplitude -3 & nbsp;C). Fruit set decreased with shade cover increases. Leaf set was shown to be the most important variable for vegetative and reproductive growth along several production cycles, and fruit drop was determined mainly by fruit set. Intermediated shade cover (LAI ~ 1 m(2) m(-2)), as occurred in coffee intercropped with bananas, showed an optimal balance between microclimate regulations, fruit set, and fruit drop, and provided staple food and an extra source of income.

Sustainable intensification (SI) of low input farming systems is promoted as a strategy to improve smallholder farmer food security in southern Africa. Using the Limpopo province South Africa as a case study (four villages across a climate gradient), we combined survey data (140 households) and quantitative agronomic observations to understand climate-induced limitations for SI of maize-based smallholder systems. Insights were used to benchmark the agroecosystem model Agricultural Production System sIMulator, which was setup to ex ante evaluate technology packages (TPs) over 21-seasons (1998–2019): TP0 status quo (no input, broadcast sowing), TP1 fertiliser (micro dosing), TP2 planting density (recommended), TP3 weeding (all removed), TP4 irrigation, TP5 planting date (early, recommended), and TP6 all combined (TPs 1–5). An additional TP7 (forecasting) investigated varying planting density and fertiliser in line with weather forecasts. Input intensity levels were low and villages expressed similar challenges to climate risk adaptation, with strategies mostly limited to adjusted planting dates and densities, with less than 2% of farmers having access to water for irrigation. Simulations showed that combining all management interventions would be expected to lead to the highest mean maize grain yields (3200 kg ha−1 across villages) and the lowest harvest failure risk compared to individual interventions. Likewise, simulations suggested that irrigation alone would not result in yield gains and simple agronomic adjustments in line with weather forecasts indicated that farmers could expect to turn rainfall variability into an opportunity well worth taking advantage of. Our study emphasises the need for a cropping systems approach that addresses multiple crop stresses simultaneously.

Crop rotation, fertilization and residue management affect the water balance and crop production and can lead to different sensitivities to climate change. To assess the impacts of climate change on crop rotations (CRs), the crop model ensemble (APSIM,AQUACROP, CROPSYST, DAISY, DSSAT, HERMES, MONICA) was used. The yields and water balance of two CRs with the same set of crops (winter wheat, silage maize, spring barley and winter rape) in a continuous transient run from 1961 to 2080 were simulated. CR1 was without cover crops and without manure application. Straw after the harvest was exported from the fields. CR2 included cover crops, manure application and crop residue retention left on field. Simulations were performed using two soil types (Chernozem, Cambisol) within three sites in the Czech Republic, which represent temperature and precipitation gradients for crops in Central Europe. For the description of future climatic conditions, seven climate scenarios were used. Six of them had increasing CO & nbsp;concentrations according RCP 8.5, one had no CO2 increase in the future. The output of an ensemble expected higher productivity by 0.82 t/ha/year and 2.04 t/ha/year for yields and aboveground biomass in the future (2051-2080). However, if the direct effect of a CO2 increase is not considered, the average yields for lowlands will be lower. Compared to CR1, CR2 showed higher average yields of 1.26 t/ha/year for current climatic conditions and 1.41 t/ha/year for future climatic conditions. For the majority of climate change scenarios, the crop model ensemble agrees on the projected yield increase in C3 crops in the future for CR2 but not for CR1. Higher agreement for future yield increases was found for Chernozem, while for Cambisol, lower yields under dry climate scenarios are expected. For silage maize, changes in simulated yields depend on locality. If the same hybrid will be used in the future, then yield reductions should be expected within lower altitudes. The results indicate the potential for higher biomass production from cover crops, but CR2 is associated with almost 120 mm higher evapotranspiration compared to that of CR1 over a 5-year cycle for lowland stations in the future, which in the case of the rainfed agriculture could affect the long-term soil water balance. This could affect groundwater replenishment, especially for locations with fine textured soils, although the findings of this study highlight the potential for the soil water-holding capacity to buffer against the adverse weather conditions.

The rapid expansion of macadamia production areas in South Africa is linked to increased irrigation water use. Improving water use efficiency is paramount for sustainable production, yet, knowledge of the specific macadamia water requirements and water use behaviour is limited. To close this gap, we set up field experiments to: (i) determine variations of macadamia sap flux density (transpiration) as a result of changes in microclimate and soil water across seasons, (ii) quantify water use across seasons for trees of different cultivars and ages, and (iii) identify the most important drivers of macadamia tree transpiration. Field measurements of macadamia sap flux density, soil water content and microclimatic parameters were recorded over two years from 20 trees of different cultivars (‘Beaumont’ and ‘HAES849’) and age classes (intermediate vs. full-bearing).Results showed that macadamia trees exhibit a conservative (isohydric) water use behaviour, however seasonal deviations from this water-saving strategy were observed for full-bearing trees. Furthermore, trees of the ‘Beaumont’ cultivar had higher transpiration rates than ‘HAES849’ trees in conditions of limited water availability. Hence, tree age and cultivar appeared to be important determinants of macadamia daily water use. Macadamia water use was generally lower than previously reported values, with full-bearing ‘Beaumont’ and ‘HAES849’ trees transpiring between 0.6-0.8 and 0.6-0.9 mm per day, respectively, during dry and rainy seasons. Multiple regression analysis showed that microclimatic parameters were the main drivers of macadamia transpiration. A vapor pressure deficit threshold of 1.5-2 kPa was observed beyond which transpiration was limited due to strict tree stomatal control, regardless of soil water content. The application of increased irrigation amounts under such conditions, would not promote tree transpiration but rather lead to over-irrigation. Thus, there is scope to increase macadamia tree water use efficiency by reducing irrigation, especially during periods of high evaporative demand.

The European Union is highly dependent on soybean imports from overseas to meet its protein demands. Individual Member States have been quick to declare self-sufficiency targets for plant-based proteins, but detailed strategies are still lacking. Rising global temperatures have painted an image of a bright future for soybean production in Europe, but emerging climatic risks such as drought have so far not been included in any of those outlooks. Here, we present simulations of future soybean production and the most prominent risk factors across Europe using an ensemble of climate and soybean growth models. Projections suggest a substantial increase in potential soybean production area and productivity in Central Europe, while southern European production would become increasingly dependent on supplementary irrigation. Average productivity would rise by 8.3% (RCP 4.5) to 8.7% (RCP 8.5) as a result of improved growing conditions (plant physiology benefiting from rising temperature and CO2 levels) and farmers adapting to them by using cultivars with longer phenological cycles. Suitable production area would rise by 31.4% (RCP 4.5) to 37.7% (RCP 8.5) by the mid-century, contributing considerably more than productivity increase to the production potential for closing the protein gap in Europe. While wet conditions at harvest and incidental cold spells are the current key challenges for extending soybean production, the models and climate data analysis anticipate that drought and heat will become the dominant limitations in the future. Breeding for heat-tolerant and water-efficient genotypes is needed to further improve soybean adaptation to changing climatic conditions.