Climate analogs can catalyze cross-regional dialogs for US specialty crop adaptation.

Communication theory suggests that interactive dialog rather than information transmission is necessary for climate change action, especially for complex systems like agriculture. Climate analogs-locations whose current climate is similar to a target location's future climate-have garnered recent interest as transmitting more relatable information; however, they have unexplored potential in facilitating meaningful dialogs, and whether the way the analogs are developed could make a difference. We developed climate context-specific analogs based on agriculturally-relevant climate metrics for US specialty crop production, and explored their potential for facilitating dialogs on climate adaptation options. Over 80% of US specialty crop counties had acceptable US analogs for the mid-twenty-first century, especially in the West and Northeast which had greater similarities in the crops produced across target-analog pairs. Western counties generally had analogs to the south, and those in other regions had them to the west. A pilot dialog of target-analog pairs showed promise in eliciting actionable adaptation insights, indicating potential value in incorporating analog-driven dialogs more broadly in climate change communication.

Assessing temperature-based adaptation limits to climate change of temperate perennial fruit crops.

Temperate perennial fruit and nut trees play varying roles in world food diversity-providing edible oils and micronutrient, energy, and protein dense foods. In addition, perennials reuse significant amounts of biomass each year providing a unique resilience. But they also have a unique sensitivity to seasonal temperatures, requiring a period of dormancy for successful growing season production. This paper takes a global view of five temperate tree fruit crops-apples, cherries, almonds, olives, and grapes-and assesses the effects of future temperature changes on thermal suitability. It uses climate data from five earth system models for two CMIP6 climate scenarios and temperature-related indices of stress to indicate potential future areas where crops cannot be grown and highlight potential new suitable regions. The loss of currently suitable areas and new additions in new locations varies by scenario. In the southern hemisphere (SH), end-century (2081-2100) suitable areas under the SSP 5-8.5 scenario decline by more than 40% compared to a recent historical period (1991-2010). In the northern hemisphere (NH) suitability increases by 20% to almost 60%. With SSP1-2.6, however, the changes are much smaller with SH area declining by about 25% and NH increasing by about 10%. The results suggest substantial restructuring of global production for these crops. Essentially, climate change shifts temperature-suitable locations toward higher latitudes. In the SH, most of the historically suitable areas were already at the southern end of the landmass limiting opportunities for adaptation. If breeding efforts can bring chilling requirements for the major cultivars closer to that currently seen in some cultivars, suitable areas at the end of the century are greater, but higher summer temperatures offset the extent. The high value of fruit crops provides adaptation opportunities such as cultivar selection, canopy cooling using sprinklers, shade netting, and precision irrigation.

Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment.

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.

A trait-based model ensemble approach to design rice plant types for future climate.

Crop models are powerful tools to support breeding because of their capability to explore genotype × environment×management interactions that can help design promising plant types under climate change. However, relationships between plant traits and model parameters are often model specific and not necessarily direct, depending on how models formulate plant morphological and physiological features. This hinders model application in plant breeding. We developed a novel trait-based multi-model ensemble approach to improve the design of rice plant types for future climate projections. We conducted multi-model simulations targeting enhanced productivity, and aggregated results into model-ensemble sets of phenotypic traits as defined by breeders rather than by model parameters. This allowed to overcome the limitations due to ambiguities in trait-parameter mapping from single modelling approaches. Breeders' knowledge and perspective were integrated to provide clear mapping from designed plant types to breeding traits. Nine crop models from the AgMIP-Rice Project and sensitivity analysis techniques were used to explore trait responses under different climate and management scenarios at four sites. The method demonstrated the potential of yield improvement that ranged from 15.8% to 41.5% compared to the current cultivars under mid-century climate projections. These results highlight the primary role of phenological traits to improve crop adaptation to climate change, as well as traits involved with canopy development and structure. The variability of plant types derived with different models supported model ensembles to handle related uncertainty. Nevertheless, the models agreed in capturing the effect of the heterogeneity in climate conditions across sites on key traits, highlighting the need for context-specific breeding programmes to improve crop adaptation to climate change. Although further improvement is needed for crop models to fully support breeding programmes, a trait-based ensemble approach represents a major step towards the integration of crop modelling and breeding to address climate change challenges and develop adaptation options.

Impacts of irrigation efficiency on water-dependent sectors are heavily controlled by region-specific institutions and infrastructures.

Farmers' investment in more efficient irrigation systems represents a primary adaptation strategy when confronting climate change. However, the regional benefits of these investments and their influence on the conflicting demands among different water dependent stakeholders for intensely irrigated regions remains an open question. Using the Pacific Northwest of the United States as an illustrative region of focus, we show that higher irrigation efficiency has diverse effects across stakeholders that are contingent on many local climatic, institutional and infrastructural factors such as the availability of water storage, the location of hydropower generators, and water rights. These complexities limit simple abstractions of irrigation efficiency as broader policy challenge and are central to its inclusion within the class of "wicked problems". Additionally, we argue that the widely used rebound effect concept, which implicitly discourages irrigation efficiency supporting policies, should not be assumed to fully capture the nuances of the complex suite of regional impacts that emerge from irrigation efficiency investments. Consequently, the evaluation of irrigation efficiency investments requires a broader framing across a diversity of perspectives. policies and actions that are pluralistic, context-specific, and closely engage various groups of stakeholders in the policymaking process.

Corrigendum to "protocol for life cycle assessment modeling of US fruit and vegetable supply chains- cases of processed potato and tomato products" [Data in Brief 34 (2021) 1-24/ 106639].

[This corrects the article DOI: 10.1016/j.dib.2020.106639.].

Impact of climate change on greenhouse gas emissions and water balance in a dryland-cropping region with variable precipitation.

Wheat covers a significant fraction of the US Pacific Northwest (PNW) dryland agriculture. Past studies have suggested that management practices can differentially affect productivity and emission of greenhouse gases (GHGs) across the different agro-ecological Zones (AEZs) in PNW. In this study we used CropSyst, a biophysically-based cropping systems model that simulates crop processes and water and nitrogen cycles, with the purpose of evaluating relevant scenarios and contributing analyses to inform adaptation and mitigation strategies aimed at reducing and managing the risks of climate change. We compared the baseline historical period of 1980-2010 with three future periods: 2015-2045 (2030s), 2035-2065 (2050s), and 2055-2085 (2070s). The uncertainty of the future climate was captured using 12 general circulation models (GCMs) forced with two representative carbon dioxide concentration pathways (RCP 4.5 and 8.5). The study region was divided into three AEZs: crop-fallow (CF), continuous cropping to fallow transition (CCF), and continuous cropping (CC). The results indicated that areas with higher precipitation, N fertilization, and mineralization produced more N2O emissions during both baseline and future periods. The average annual N2O emission during the baseline period was between 1.8 and 4.1 kg ha-1 depending on AEZ. The overall N2O emission showed decreasing future trends from 2030s to 2070s which resulted from a higher proportion of N used by crops. From 2015 to 2085 under RCP 4.5, the average N2O emission was between 1.8 and 4.4 kg ha-1 year-1. They are slightly higher under RCP 8.5 since it is a warmer scenario. The soil organic carbon (SOC) content decreased during the baseline period while SOC did not reach equilibrium with the cropping systems considered in the study. SOC decreased during the future periods as well, with rate of change ranging from -146 to -352 kg ha-1year-1 depending on AEZ and RCP. Warming increased SOC oxidation in future scenarios, but after an initial increase of SOC losses during the 2030s period, the rate of SOC losses decreased in the 2050s, and more so in the 2070s as SOC and carbon input reached equilibrium with losses. Higher carbon input resulted from higher biomass production under elevated CO2 scenarios. The total GHG emissions were 1.95, 3.16 and 4.84 Mg CO2-equivalent ha-1year-1 under RCP 4.5, and 1.99, 3.43 and 5.49 Mg CO2-equivalent ha-1year-1 under RCP 8.5 during 2070s in CF, CCF and CC respectively, with N2O accounting for about 81% of total GHG emissions.

Protocol for life cycle assessment modeling of US fruit and vegetable supply chains- cases of processed potato and tomato products.

This article elaborates on the life cycle assessment (LCA) protocol designed for formulating the life cycle inventories (LCIs) of fruit and vegetable (F&V) supply chains. As a set of case studies, it presents the LCI data of the processed vegetable products, (a) potato: chips, frozen-fries, and dehydrated flakes, and (b) tomato-pasta sauce. The data can support to undertake life cycle impact assessment (LCIA) of food commodities in a "cradle to grave" approach. An integrated F&V supply chain LCA model is constructed, which combined three components of the supply chain: farming system, post-harvest system (processing until the consumption) and bio-waste handling system. We have used numbers of crop models to calculate the crop yields, crop nutrient uptake, and irrigation water requirements, which are largely influenced by the local agro-climatic parameters of the selected crop reporting districts (CRDs) of the United States. For the farming system, LCI information, as shown in the data are averaged from the respective CRDs. LCI data for the post-harvest stages are based on available information from the relevant processing plants and the engineering estimates. The article also briefly presents the assumptions made for evaluating future crop production scenarios. Future scenarios integrate the impact of climate change on the future productivity and evaluate the effect of adaptation measures and technological advancement on the crop yield. The provided data are important to understand the characteristics of the food supply chain, and their relationships with the life cycle environmental impacts. The data can also support to formulate potential environmental mitigation and adaptation measures in the food supply chain mainly to cope with the adverse impact of climate change.

Supply chains for processed potato and tomato products in the United States will have enhanced resilience with planting adaptation strategies.

Food systems are increasingly challenged to meet growing demand for specialty crops due to the effects of climate change and increased competition for resources. Here, we apply an integrated methodology that includes climate, crop, economic and life cycle assessment models to US potato and tomato supply chains. We find that supply chains for two popular processed products in the United States, French fries and pasta sauce, will be remarkably resilient, through planting adaptation strategies that avoid higher temperatures. Land and water footprints will decline over time due to higher yields, and greenhouse gas emissions can be mitigated by waste reduction and process modification. Our integrated methodology can be applied to other crops, health-based consumer scenarios (fresh versus processed) and geographies, thereby informing decision-making throughout supply chains. Employing such methods will be essential as food systems are forced to adapt and transform to become carbon neutral due to the imperatives of climate change.

Can Crop Models Identify Critical Gaps in Genetics, Environment, and Management Interactions?

Increasing food demand under climate change constraints may challenge and strain agricultural systems. The use of crop models to assess genotypes performance across diverse target environments and management practices, i.e., the genetic × environment × management interaction (GEMI), can help understand suitability of genotype and agronomic practices, and possibly accelerate turnaround in plant breeding programs. However, the readiness of models to support these tasks can be debated. In this article, we point out modeling and data limitations and argue the need for evaluation and improvement of relevant process algorithms as well as model convergence. Under conditions suitable for plant growth, without meteorological extremes or soil limitation to root exploration, models can simulate resource capture, growth, and yield with relative ease. As stresses accumulate, the plant species- and genotype-specific attributes and their interactions with the soil and atmospheric environment generate a large range of responses, including conditions where resources become so limiting as to make yields very low. The space in between high and low yields is where most rainfed production occurs, and where the current model and user skill at representing GEMI varies. We also review studies comparing the performance of a large number of crop models and the lessons learned. The overall message is that improvement of models appears as a necessary condition for progress, and perhaps relevancy. Model ensembles help mitigate data input, model, and user-driven uncertainty for some but not all applications, sometimes at a very high cost. Successful model-based assessment of GEMI not only requires better crop models and knowledgeable users, but also a realistic representation of the environmental conditions of the landscape where crops are grown, which is not trivial given the 3D nature of water and nutrient transport. Models remain the best quantitative repository of our knowledge on crop functioning; they contain a narrative of plant, soil, and atmospheric functioning in computer language and train the mind to couple processes. But in our quest to tame GEMI, will they lead the way or just ride along history?

Comparison of air temperature measured in a vineyard canopy and at a standard weather station.

The complex environment within a crop canopy leads to a high variability of the air temperature within the canopy, and, therefore, air temperature measured at a weather station (WS) does not represent the internal energy within a crop. The objectives of this study were to quantify the difference between the air temperature measured at a standard WS and the air temperature within a six-year-old vineyard (cv. Chardonnay) and to determine the degree of uncertainty associated with the assumption that there is no difference between the two temperatures when air temperature is used as input in grapevine models. Thermistors and thermocouples were installed within the vine canopy at heights of 0.5 m and 1.2 m above the soil surface and immediately adjacent to the berry clusters. In the middle of the clusters sensors were installed to determine the temperature of the air surrounding the clusters facing east and west. The data were recorded within the canopy from December 2015 to June 2017 as well as at the standard WS that was installed close to the vineyard (410 m). Significant differences were found between the air temperatures measured at the WS and those within the vineyard during the summer when the average daily minimum air temperature within the canopy was 1.2°C less than at the WS and the average daily maximum air temperature in the canopy was 2.0°C higher than at the WS. The mean maximum air temperature measured in the clusters facing east was 1.5°C higher and west 4.0°C higher than the temperature measured at the WS. Therefore, models that assume that air temperature measured at a weather station is similar to air temperature measured in the vineyard canopy could have greater uncertainty than models that consider the temperature within the canopy.

N2O Emissions From Two Agroecosystems: High Spatial Variability and Long Pulses Observed Using Static Chambers and the Flux-Gradient Technique.

With the addition of nitrogen (N), agricultural soils are the main anthropogenic source of N2O, but high spatial and temporal variabilities make N2O emissions difficult to characterize at the field scale. This study used flux-gradient measurements to continuously monitor N2O emissions at two agricultural fields under different management regimes in the inland Pacific Northwest of Washington State, USA. Automated 16-chamber arrays were also deployed at each site; chamber monitoring results aided the interpretation of the flux gradient results. The cumulative emissions over the six-month (1 April-30 September) monitoring period were 2.4 ± 0.7 and 2.1 ± 2 kg N2O-N/ha at the no-till and conventional till sites, respectively. At both sites, maximum N2O emissions occurred following the first rainfall event after N fertilization, and both sites had monthlong emission pulses. The no-till site had a larger N2O emission factor than the Intergovernmental Panel on Climate Change Tier 1 emission factor of 1% of the N input, while the conventional-till site's emission factor was close to 1% of the N input. However, these emission factors are likely conservative. We estimate that the global warming potential of the N2O emissions at these sites is larger than that of the no-till conversion carbon uptake. We recommend the use of chambers to investigate spatiotemporal controls as a complementary method to micrometeorological monitoring, especially in systems with high variability. Continued monitoring coupled with the use of models is necessary to investigate how changing management and environmental conditions will affect N2O emissions.

Novel multimodel ensemble approach to evaluate the sole effect of elevated CO2 on winter wheat productivity.

Elevated carbon-dioxide concentration [eCO2] is a key climate change factor affecting plant growth and yield. Conventionally, crop modeling work has evaluated the effect of climatic parameters on crop growth, without considering CO2. It is conjectured that a novel multimodal ensemble approach may improve the accuracy of modelled responses to eCO2. To demonstrate the applicability of a multimodel ensemble of crop models to simulation of eCO2, APSIM, CropSyst, DSSAT, EPIC and STICS were calibrated to observed data for crop phenology, biomass and yield. Significant variability in simulated biomass production was shown among the models particularly at dryland sites (44%) compared to the irrigated site (22%). Increased yield was observed for all models with the highest average yield at dryland site by EPIC (49%) and lowest under irrigated conditions (17%) by APSIM and CropSyst. For the ensemble, maximum yield was 45% for the dryland site and a minimum 22% at the irrigated site. We concluded from our study that process-based crop models have variability in the simulation of crop response to [eCO2] with greater difference under water-stressed conditions. We recommend the use of ensembles to improve accuracy in modeled responses to [eCO2].

Combining Simple Phenotyping and Photothermal Algorithm for the Prediction of Soybean Phenology: Application to a Range of Common Cultivars Grown in Europe.

Developing new cropping strategies (very early sowing, crop expansion at higher latitudes, double cropping) to improve soybean production in Europe under climate change needs a good prediction of phenology under different temperature and photoperiod conditions. For that purpose, a simple phenology algorithm (SPA) was developed and parameterized for 10 contrasting soybean cultivars (maturity group 000 to II). Two experiments were carried out at INRA Toulouse (France) for parameterization: 1) Phenological monitoring of plants in pots on an outdoor platform with 6 planting dates. 2) Response of seed germination to temperature in controlled conditions. Multi-location field trials including 5 sites, 4 years, 2 sowing dates, and 10 cultivars were used to evaluate the SPA phenology predictions. Mean cardinal temperatures (minimum, optimum, and maximum) for germination were ca. 2, 30, and 40°C, respectively with significant differences among cultivars. The photoperiod sensitivity coefficient varied among cultivars when fixing Popt and Pcrt, optimal and critical photoperiods respectively, by maturity group. The parameterized algorithm showed an RMSE of less than 6 days for the prediction of crop cycle duration (i.e. cotyledons stage to physiological maturity) in the field trials including 75 data points. Flowering (R1 stage), and beginning of grain filling (R5 stage) dates were satisfactorily predicted with RMSEs of 8.2 and 9.4 days respectively. Because SPA can be also parameterized using data from field experiments, it can be useful as a plant selection tool across environments. The algorithm can be readily applied to species other than soybean, and its incorporation into cropping systems models would enhance the assessment of the performance of crop cultivars under climate change scenarios.

Global wheat production with 1.5 and 2.0°C above pre-industrial warming.

Efforts to limit global warming to below 2°C in relation to the pre-industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre-industrial period) on global wheat production and local yield variability. A multi-crop and multi-climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by -2.3% to 7.0% under the 1.5°C scenario and -2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980-2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter-annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer-India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade.

Multimodel ensembles improve predictions of crop-environment-management interactions.

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

Author Correction: The uncertainty of crop yield projections is reduced by improved temperature response functions.

Nature Plants 3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.

Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions.

This corrects the article DOI: 10.1038/nplants.2017.102.

Erratum: The uncertainty of crop yield projections is reduced by improved temperature response functions.

This corrects the article DOI: 10.1038/nplants.2017.102.

The uncertainty of crop yield projections is reduced by improved temperature response functions.

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.