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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.

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., Key Points Total emissions from 1 April to 30 September 2015 were 2.4 ± 0.7 and 2.1 ± 0.2 kg N2O‐N/ha at the no‐ and conventional‐till sites, respectivelyN2O fluxes had similar responses to temperature and precipitation at both sitesHigh spatial variability at both sites (CVs > 140%) limited the ability of the chamber systems to characterize field‐scale N2O emissions

Dairy concentrated animal feeding operations (CAFO) are required to implement nutrient management plans enforced by environmental protection agencies to minimizing the risk of water resource degradation and to report gaseous emissions when they exceed certain threshold values. Although tools exist to aid in completing such tasks, few of the tools integrate the impact of on-farm manure treatment unit operations such as anaerobic digestion, solids separation, and nutrient recovery. Furthermore, existing tools do not estimate the nutrient value of recovered products and effluent leaving the dairy system or the nutrient fate after effluent is applied to crop fields. Dairy-CropSyst is a decision support tool for researchers and CAFO managers aimed at evaluating the effects of different manure treatment unit operations on gaseous emission and nutrient fate in dairy systems. The model tracks nutrients through the dairy system, including inorganic and organic forms of carbon, nitrogen, and phosphorus. This is accomplished by integrating established transformation and emission equations, performance parameters of manure treatments from industrial data and literature, and using a cropping system model for the land application evaluation. Comparison of simulated values with observed emissions indicated that model performance was reasonable. The overall correlation coefficient of observed and predicted ammonia and methane emission from dairy manure management was 0.72 and 0.58 respectively. Simulated and observed N2O emission were found in close agreement before and after the irrigation event, however, the model over estimated N2O emission after irrigation events (Coefficient of Residual Mass = −0.87). The use of Dairy-CropSyst has the potential to assist the dairy industry in decision making on manure management treatment strategies and as a tool for reporting GHG and ammonia emissions.

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.

Food systems are increasingly challenged to meet growing demand for specialty crops due to the effects of climate change and increased competition for resources. We apply a novel integrated methodology that includes climate, crop, economic, and life cycle assessment (LCA) models to US potato and tomato supply chains. We assess the effectiveness of changing management strategies to address climate change and the opportunity to relocate away from regions with increased water scarcity. 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 GHG emissions can be mitigated by waste reduction and process modification. Our integrated methodology can be applied to other crops and geographies, and the results could inform decision-making at multiple steps along supply chains.

Irrigation efficiency plays an important role in agricultural productivity; it affects farm-scale water demand, and the partitioning of irrigation losses into evaporative and non-evaporative components. This partitioning determines return flow generation and thus affects water availability. Over the last two decades, hydrologic and agricultural research communities have significantly improved our understanding of the impacts of climate change on water availability and food productivity. However, the impacts of climate change on the efficiency of irrigation systems, particularly on the partitioning between evaporative and non-evaporative losses, have received little attention. In this study, we incorporated a process-based irrigation module into a coupled hydrologic/agricultural modeling framework (VIC-CropSyst). To understand how climate change may impact irrigation losses, we applied VIC-CropSyst over the Yakima River basin, an important agricultural region in Washington State, U.S. We compared the historical period of 1980–2010 to an ensemble of ten projections of climate for two future periods: 2030–2060 and 2060–2090. Results averaged over the watershed showed that a 9% increase in evaporative losses will be compensated by a reduction of non-evaporative losses. Therefore, overall changes in future efficiency are negligible (−0.4%) while the Evaporative Loss Ratio (ELR) (defined as the ratio of evaporative to non-evaporative irrigation losses) is enhanced by 10%. This higher ELR is associated with a reduction in return flows, thus negatively impacting downstream water availability. Results also indicate that the impact of climate change on irrigation losses depend on irrigation type and climate scenarios.

In this paper we propose to extend methods for agricultural impact assessment to study the adaptations that agricultural producers are likely to consider in response to climate change – i.e., the use of different combinations of crop or livestock species and associated changes in management. Analysis of these kinds of adaptations, referred to here as “between-system adaptations” – requires estimates of the counterfactual productivity and cost of production for prospective systems that are not observable in the locations where they could be used. We propose two methods that we call simulation matching and propensity score matching. We apply and compare the results of these methods in a study of wheat-based systems in the U.S. Pacific Northwest. We find substantial differences between the two methods, but these differences do not appear to be systematic or associated with characteristics of the systems. We conclude that the method used for estimating the productivity of the new system introduces an element of uncertainty into adaptation analysis, in addition to the other data, model and scenario uncertainties. Further research is warranted to evaluate alternative methods for analysis of between-system adaptations and their associated uncertainties.

Future changes in crop evapotranspiration (ETc) are of interest to water management stakeholders. However, long-term projections are complex and merit further investigation due to uncertainties in climate data, differential responses of crops to climate and elevated atmospheric CO2, and adaptive agricultural management. We conducted factor-control simulation experiments using the process-based CropSyst model and investigated the contribution of each of these factors. Five major irrigated crops in the Columbia Basin Project area of the USA Pacific Northwest were selected as a case study and fifteen general circulation models (GCM) under two representative concentration pathways (RCP) were used as the climate forcing. Results indicated a wide range in ETc change, depending on the time frame, crop type, planting dates, and CO2 assumptions. Under the 2090s RCP8.5 scenario, ETc changes were crop-specific: +14.3% (alfalfa), +8.1% (potato), −5.1% (dry bean), −8.1% (corn), and −12.5% (spring wheat). Future elevated CO2 concentrations decreased ETc for all crops while earlier planting increased ETc for all crops except spring wheat. Changes in reference ET (ETo) only partially explains changes in ETc because crop responses are an important modulating factor; therefore, caution must be exercised in interpreting ETo changes as a proxy for ETc changes.

Continuous flow through chamber systems are recognized for their superiority in obtaining high resolution seasonal greenhouse gas (GHG) emissions data. In the current study, we combine Li-Cor 8100A CO2 flux system with the infrared N2O analyzer (Teledyne API, San Diego, CA) and a laser spectroscopic analyzer (Los Gatos Research Inc, Mountain View, CA) to design a robust setup for reliable concurrent measurements of N2O and CO2 emissions from automatic chambers and to obtain flux gradient N2O data from agricultural land. Field testing revealed considerable interference of moisture and temperature with the infrared N2O analyzer data, which showed a negative drift of up to 0.5 ppm at increased humidity (R2 = 0.76). Addition of desiccant column on line in an attempt to improve infrared analyzer N2O readings due to removal of moisture affected the N2O and CO2 baselines, likely due to flow modulation by the Li-Cor 8100A air pump. High precision output from the laser spectroscopic analyzer and stability of its baseline at variable temperature and moisture were preferable for combined automatic chamber and flux tower measurements. The total N2O produced at the site fertilized with 100 kg N ha−1 was 1.8 ± 0.7 kg N2O-N ha−1, and the total CO2 emissions were 4350 ± 1173 kg CO2-C ha−1 during the measurement period in the fall and spring. The high frequency of sampling and low labor intensity of the N2O and CO2 measurement setup allows for comparison of GHG emissions from short term events and across seasons, and establishes the basis for better understanding of emissions sources in agricultural systems.

A regional assessment of baseline (1980–2010) and future (2015–2085) yields of dryland wheat-based cropping systems in the US Inland Pacific Northwest (IPNW) was conducted. The computer simulation-based assessment was done using CropSyst, a cropping systems simulation model, and projected daily weather data downscaled to a 4 × 4 km grid using 12 general circulation models (GCMs) for two atmospheric CO2 representative concentration pathways (RCP 4.5 and RCP 8.5). The study region was divided into 3 agro-ecological zones (AEZs): continuous cropping (CC), continuous cropping-fallow transition (CCF), and crop-fallow (CF), with the following typical rotations assigned to the zones: winter wheat (WW) – summer fallow (SF) (CF zone), WW – spring wheat (SW) – SF (CCF zone), and WW – SW – spring pea (CC zone). By the 2070s (2065–2085), precipitation in the IPNW is projected to increase by about 8 and 12% compared to the baseline period under RCP 4.5 and 8.5, respectively. Mean temperature during the WW growing season will increase about 1.5 and 2.3 °C under RCP 4.5 and 8.5, respectively, but will not change noticeably during the SW growing season due to the adaptive early planting used in this study. Concurrently, atmospheric CO2 concentration will increase from today's average of ~ 400 ppm to 532 ppm to 801 ppm by 2085 depending on future emissions of greenhouse gases. Soil water-crop growth interactions, which show large variation across the region, will modulate crop responses to these changing conditions, with our results showing an overall increase in yield across the IPNW. By the 2070s, the mean ratio of future to baseline WW yield will range from 1.29 to 1.35 under RCP 4.5 and from 1.41 to 1.64 under RCP 8.5 depending on the AEZ. The mean yield ratio for SW across AEZs will range from 1.38 to 1.53 under RCP 4.5 and 1.54 to 1.91 under RCP 8.5. Given substantial climatic heterogeneity in the region, these gains will not be distributed equally across the region or within AEZs, and overall they will not be shared equally by all growers.

Cropland is an important land cover influencing global carbon and water cycles. Variability of agricultural carbon and water fluxes depends on crop species, management practices, soil characteristics, and climatic conditions. In the context of climate change, it is critical to quantify the long-term effects of these environmental drivers and farming activities on carbon and water dynamics. Twenty site-years of carbon and water fluxes covering a large precipitation gradient and a variety of crop species and management practices were measured in the inland Pacific Northwest using the eddy covariance method. The rain-fed fields were net carbon sinks while the irrigated site was close to carbon neutral during the winter wheat crop years. Sites growing spring crops were either carbon sinks, sources, or neutral, varying with crops, rainfall zones, and tillage practices. Fluxes were more sensitive to variability in precipitation than temperature: annual carbon and water fluxes increased with the increasing precipitation while only respiration increased with temperature in the high-rainfall area. Compared to a nearby rain-fed site, irrigation improved winter wheat production but resulted in large losses of carbon and water to the atmosphere. Compared to conventional tillage, no-till had significantly lower respiration but resulted in slightly lower yields and water use efficiency over four years. Under future climate change, it is expected that more carbon fixation by crops and evapotranspiration would occur in a warmer and wetter environment.

Organic farmers often use complex management practices to foster a positive impact on the environment. Many tools exist to aid in estimating environmental services but few are able to properly handle the complexities of organic agriculture. We developed an online tool called OFoot to estimate the carbon footprint of organic farms located in the Pacific Northwest and to help evaluate the potential for environmental benefits. OFoot utilizes a cradle-to-gate carbon calculator and a biophysical, process-based, cropping and field management model. We present the software architecture of the tool, model descriptions, and a case study which simulates several scenarios of organic potato production. The scenario simulating potato production with organic fertilizers and a leguminous winter cover crop sequestered soil carbon. The other scenarios, either lacking fertilizer or cover crops, lost soil carbon. The usefulness of the tool as an aid to management decisions is demonstrated.

This article examines the use of process-based agronomic, livestock, and farming systems models with economic models for analysis of climate impact and adaptation. First we describe the int...

The long-term sustainability of wheat-based dryland cropping systems in the Inland Pacific Northwest (IPNW) of the United States depends on how these systems adapt to climate change. Climate models project warming with slight increases in winter precipitation but drier summers for the IPNW. These conditions combined with elevated atmospheric CO2, which promote crop growth and improve transpiration-use efficiency, may be beneficial for cropping systems in the IPNW and may provide regional opportunities for agricultural diversification and intensification. Crop modeling simulation under future climatic conditions showed increased wheat productivity for the IPNW for most of the century. Water use by winter wheat was projected to decrease significantly in higher and intermediate precipitation zones and increase slightly in drier locations, but with winter crops utilizing significantly more water overall than spring crops. Crop diversification with inclusion of winter crops other than wheat is a possibility depending on agronomic and economic considerations, while substitution of winter for spring crops appeared feasible only in high precipitation areas. Increased weed pressure, higher pest populations, expanded ranges of biotic stressors, and agronomic, plant breeding, economic, technology, and other factors will influence what production systems eventually prevail under future climatic conditions in the region.

The traditional catch-can technique for measuring water depth application under sprinkler irrigation systems has the limitation of being unable to monitor the continuous variation of wind drift and evaporation losses (WDEL) under changing weather conditions. Such information is essential to better manage the agricultural water by improving the global water application uniformity under moving irrigation machines. Three parallel, long, impermeable water collection strips were constructed underneath a stationary linear move irrigation system to address this issue. The sprinkler discharge efficiency (SDE ≈ 1- WDEL) was monitored over 5-min intervals (SDE5min) during Apr-Aug of 2014-on a bare experimental plot. Experiments were conducted on a discrete basis for about 1040 h total in order to collect more than 11,600 SDE5min data points. It was found that the SDE5min is very dynamic and can experience abrupt changes up to 16.5% as a result of sudden changes in wind speed and direction. The maximum and minimum observed SDE5min during the study period were 97.5 and 73.6%, respectively. The difference between maximum and minimum SDE5min during a day/night cycle was controlled by solar radiation and maximum wind speed. The average hourly SDE (SDEhr) for data collected at predawn, morning, afternoon, and evening were 89.5, 87.7, 86.9 and 88.8%, respectively. Overall, the daily SDE was mainly controlled by air temperature and varied between 81.8 and 91.8%, indicating that ∼8–18% of the daily applied water was lost on average. A multiple linear regression model was developed to help predict the SDEhr as a function of weather parameters. The model indicated wind speed, temperature and relative humidity as the best explanatory variables and predicted the SDEhr with an absolute error just over 4%. Because the difference between maximum and minimum SDEhr over a full experimental day could be as high as 22%, this magnitude of error was considered to be acceptable for wind drift and evaporation loss estimation.

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?

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].

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.

Site-specific irrigation management for perennial crops such as grape requires water use assessments at high spatiotemporal resolution. In this study, small unmanned-aerial-system (UAS)-based imaging was used with a modified mapping evapotranspiration at high resolution with internalized calibration (METRIC) energy balance model to map water use (UASM-ET approach) of a commercial, surface, and direct-root-zone (DRZ) drip-irrigated vineyard. Four irrigation treatments, 100%, 80%, 60%, and 40%, of commercial rate (CR) were also applied, with the CR estimated using soil moisture data and a non-stressed average crop coefficient of 0.5. Fourteen campaigns were conducted in the 2018 and 2019 seasons to collect multispectral (ground sampling distance (GSD): 7 cm/pixel) and thermal imaging (GSD: 13 cm/pixel) data. Six of those campaigns were near Landsat 7/8 satellite overpass of the field site. Weather inputs were obtained from a nearby WSU-AgWeatherNet station (1 km). First, UASM-ET estimates were compared to those derived from soil water balance (SWB) and conventional Landsat-METRIC (LM) approaches. Overall, UASM-ET (2.70 ± 1.03 mm day−1 [mean ± std. dev.]) was higher than SWB-ET (1.80 ± 0.98 mm day−1). However, both estimates had a significant linear correlation (r = 0.64–0.81, p < 0.01). For the days of satellite overpass, UASM-ET was statistically similar to LM-ET, with mean absolute normalized ET departures (ETd,MAN) of 4.30% and a mean r of 0.83 (p < 0.01). The study also extracted spatial canopy transpiration (UASM-T) maps by segmenting the soil background from the UASM-ET, which had strong correlation with the estimates derived by the standard basal crop coefficient approach (Td,MAN = 14%, r = 0.95, p < 0.01). The UASM-T maps were then used to quantify water use differences in the DRZ-irrigated grapevines. Canopy transpiration (T) was statistically significant among the irrigation treatments and was highest for grapevines irrigated at 100% or 80% of the CR, followed by 60% and 40% of the CR (p < 0.01). Reference T fraction (TrF) curves established from the UASM-T maps showed a notable effect of irrigation treatment rates. The total water use of grapevines estimated using interpolated TrF curves was highest for treatments of 100% (425 and 320 mm for the 2018 and 2019 seasons, respectively), followed by 80% (420 and 317 mm), 60% (391 and 318 mm), and 40% (370 and 304 mm) of the CR. Such estimates were within 5% to 11% of the SWB-based water use calculations. The UASM-T-estimated water use was not the same as the actual amount of water applied in the two seasons, probably because DRZ-irrigated vines might have developed deeper or lateral roots to fulfill water requirements outside the irrigated soil volume. Overall, results highlight the usefulness of high-resolution imagery toward site-specific water use management of grapevines.

A typical Dry Grind Corn Ethanol Mill (DGCEM) with a capacity of 230 ML of ethanol per year is used as the baseline for the evaluation of biorefinery concepts for sustainable aviation fuels (SFAs). The main goal is to identify SAF cost reduction opportunities as well as environmental benefits by integrating with existing DGCEM infrastructure. Five SAF production technologies are studied: Virent's BioForming (VB), Alcohol to Jet (ATJ), Direct Sugar to Hydrocarbon (DSHC), Fast Pyrolysis (FP) and Gasification & Fischer-Tropsch (GFT). We built SAF unit cases with capital cost equal to the studied DGCEM ($115 M). Larger SAF units are unlikely to synergize well with existing DGCEMs. Twelve co-location and repurposing scenarios are evaluated where SAF technologies utilize intermediate products, auxiliary facilities, or unit operations from DGCEM. For each of the scenarios, the minimum fuel selling price (MFSP) and greenhouse gas (GHG) emissions are estimated. Our aim is to identify which SAF technologies can be most efficiently integrated with a corn ethanol mill. Eleven scenarios result in lowered MFSPs in the range of 3–67% reduction, from their corresponding greenfield design cases. The highest reduction is observed when ATJ is produced in a repurposed facility. In the case of GHG we were able to identify one scenario with lower GHG emissions compared with greenfield units. SAF in thirteen scenarios have GHG emission ranging from 13 to 93% of fossil fuel. One of the repurposed scenarios of ATJ is the concept with the best overall performance parameter.

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.

Six alternatives for the conversion of an average Colombian palm oil mill (30 t h−1 of fresh fruit bunches (FFB) into biorefineries were evaluated. The alternatives studied were: (C1) Production of biogas from the Palm Oil Mill Effluents (POME), (C2) Composting of empty fruit bunches (EFB) and fiber, (C3) Biomass combustion for high pressure steam combined heat and power, (C4) Pellets production, (C5) Biochar production and, (C6) Biochar and bio-oil production. The available biomass could result in up to 125 kWh of electricity, 207 kg of compost, 125 kg of pellet, 44 kg of biochar and 63 kg of bio-oil per metric ton of FFB. The global warming potential (GWP), eutrophication potential (EP), net energy ratio (NER), capital expenditures (CAPEX), operational costs (OPEX), net present value (NPV) and internal rate of return (IRR) were calculated for all the alternatives. GHG reductions of more than 33% could be achieved. Anaerobic digestion and composting contributed to 30% reduction of the EP. The CAPEX for all of the biorefinery alternatives studied varies between 0.7 $ t−1 and 2.8 $ t−1 of FFB. The OPEX varies between 1.6 $ t−1 and 7.3 $ t−1 of FFB. The NPV for viable scenarios ranged between 2.5 million and 13.9 million US dollars. The IRR calculated varied between 3% and 56% and the payback periods were between 3 and 8 years. The total extra incomes reached values up to 15.2 $ t−1 of FFB. Overall the pellets production biorefinery was the preferred alternative.

Despite its potential to mitigate many environmental impacts of agriculture, organic farming does contribute to greenhouse gas (GHG) emissions. A full accounting and understanding of the GHG emissions associated with specific activities, materials, and energy used in organic operations are needed to support decision-making for GHG mitigation. A small-scale organic vegetable farm in Washington State, USA, was used as a case study to determine the carbon footprint (CF) and GHG hotspots. A partial life cycle assessment was conducted to identify primary and secondary GHG fluxes associated with activities and materials used in production of potatoes, cauliflower, dry bush beans, winter squash, summer squash, chard, peppers, and onions grown in a crop rotation. The CF associated with each crop ranged from a low of 1.68 t CO2-eq ha− 1 yr− 1 for chard to a high of 2.67 t CO2-eq ha− 1 yr− 1 for cauliflower. Cauliflower had the highest CF per ha followed by potatoes and pepper. Across the farm as a whole, the major CF hotspots were fuel use for both on-farm and off-farm operations (38%), fertilization (18%), soil emission (12%), and irrigation (11%). Simulation of a switch to biodiesel instead of petroleum gasoline and diesel resulted in a 32% reduction in the total farm CF. By identifying the CF hotspots of a whole farm and individual crops, particular inputs and activities can be targeted for modification in order to effectively reduce the farm's CF.

Confined animal feeding operations (CAFOs) contribute to greenhouse gas emission, but the magnitude of these emissions as a function of operation size, infrastructure, and manure management are difficult to assess. Modeling is a viable option to estimate gaseous emission and nutrient flows from CAFOs. These models use a decomposition rate constant for carbon mineralization. However, this constant is usually determined assuming a homogenous mix of manure, ignoring the effects of emerging manure treatments. The aim of this study was to measure and compare the decomposition rate constants of dairy manure in single and three-pool decomposition models, and to develop an empirical model based on chemical composition of manure for prediction of a decomposition rate constant. Decomposition rate constants of manure before and after an anaerobic digester (AD), following coarse fiber separation, and fine solids removal were determined under anaerobic conditions for single and three-pool decomposition models. The decomposition rates of treated manure effluents differed significantly from untreated manure for both single and three-pool decomposition models. In the single-pool decomposition model, AD effluent containing only suspended solids had a relatively high decomposition rate of 0.060 d(-1), while liquid with coarse fiber and fine solids removed had the lowest rate of 0.013 d(-1). In the three-pool decomposition model, fast and slow decomposition rate constants (0.25 d(-1) and 0.016 d(-1) respectively) of untreated AD influent were also significantly different from treated manure fractions. A regression model to predict the decomposition rate of treated dairy manure fitted well (R(2) = 0.83) to observed data.

A potato crop multi-model assessment was conducted to quantify variation among models and evaluate responses to climate change. Nine modeling groups simulated agronomic and climatic responses at low- (Chinoli, Bolivia and Gisozi, Burundi) and high- (Jyndevad, Denmark and Washington, United States) input management sites. Two calibration stages were explored, partial (P1), where experimental dry matter data were not provided, and full (P2). The median model ensemble response outperformed any single model in terms of replicating observed yield across all locations. Uncertainty in simulated yield decreased from 38% to 20% between P1 and P2. Model uncertainty increased with inter-annual variability, and predictions for all agronomic variables were significantly different from one model to another (p < 0.001). Uncertainty averaged 15% higher for low- versus high- input sites, with larger differences observed for evapotranspiration (ET), nitrogen uptake, and water use efficiency as compared to dry matter. A minimum of five partial, or three full, calibrated models was required for an ensemble approach to keep variability below that of common field variation. Model variation was not influenced by change in carbon dioxide (C), but increased as much as 41 and 23% for yield and ET respectively as temperature (T) or rainfall (W) moved away from historical levels. Increases in T accounted for the highest amount of uncertainty, suggesting that methods and parameters for T sensitivity represent a considerable unknown among models. Using median model ensemble values, yield increased on average 6% per 100-ppm C, declined 4.6% per °C, and declined 2% for every 10% decrease in rainfall (for non-irrigated sites). Differences in predictions due to model representation of light utilization were significant (p < 0.01). These are the first reported results quantifying uncertainty for tuber/root crops and suggest modeling assessments of climate change impact on potato may be improved using an ensemble approach.

The palm oil agroindustry not only produces the most consumed vegetable oil in the world, but also a significant quantity of residual biomass. This waste represents real opportunity to create a variety of products. In the context of sustainable oil production, the use of biomass to generate value-added products can be addressed through the evolution of existing palm oil mills (POMs) into biorefineries. In this manuscript, the authors present a literature review of potential uses for biomass generated in palm plantations and at the POM, including the main properties, quantities, and current practices. After this, a review of novel, less traditional is made. Finally, strategies for the synthesis and analysis of POM biorefinery concepts are discussed. This review highlights the need for development of high-value products from POM waste and the urgency to incubate these emerging technologies for gradual transition into biorefineries. Based on short term economic performance, biomass pelletization and anaerobic digestion of POME are the most promising technologies. Furthermore, the production of biochar has great potential when the environmental performance is taken into account. More work is needed to evaluate the long term economic, social, and environmental impact of other new technologies both now and in the future.

Indirect methods are widely used for determining air exchange rates (AER) in naturally ventilated barns because they are relatively easier and cheaper than direct methods, which measure actual airflow in and out of the barns. The main goal of this study was to evaluate a common indirect method (CO2 mass balance) against a direct method, and identify factors influencing this indirect method. The mean AER based on 24-h averaging, irrespective of method, ranged from 13 to 39 h−1 during the study-periods. The CO2-balance method tended to overestimate barn AER. The cows' CO2 production rate, in the current study, was estimated at 0.178 m3 h−1 hpu−1 based on 24-h averaging. The CO2-balance method with 24-h data averaging yielded more reliable barn AER than with shorter averaging times (i.e., 1, 2, and 12 h). The 1-h averaging, however, was chosen to analyze the effects of other pertinent factors on the CO2-balance method to capture diurnal variations of AER. Both wind speed and wind direction had significant effects on barn AER as well as the difference between the CO2-balance method and direct method. Barn AER, in general, increased with wind speed. The CO2-balance method was unreliable during milking times, and when indoor–outdoor CO2 concentration and temperature differences were less than 70 ppm and 0 °C, respectively.

Phenology, the first step of calibration of models.Robustness of APSIM-Wheat and CERES-Wheat in simulating spring wheat phenology, LAI, biomass and yield.Calibration is necessary to minimize uncertainty.Models as decision support tools in agriculture under climate variability. Crop growth in process based crop models is controlled by different parameters. Model calibration is necessary for application to new cultivars and environment. We applied a manual method to calibrate APSIM-Wheat and CERES-Wheat for the flowering day, maturity day, leaf area index, biomass and grain yield of five spring wheat cultivars under rainfed conditions in Pakistan. Five wheat cultivars of diverse origin namely Tatara, NARC-2009, Sehar-2006, SKD-1 and F-Sarhad were planted on 19th November, at Islamabad during the years 2007-2011. The experiments were laid out in Randomized Complete Block Design (RCBD) replicated four times with individual plot size of 5m?3m. APSIM-Wheat and CERES-Wheat were calibrated for all five wheat cultivars using genetic coefficients estimated based upon measured data during 2008-09 cropping year and validated with independent data sets (experimental data of 2009-10 and 2010-11 cropping seasons) which were not used for models calibration. Both models were able to accurately simulate anthesis and maturity days, maximum leaf area index, biomass and grain yield, with normalized root mean square error (RMSE) less than 10%, D-index greater than 0.80 and model efficiency above 80% in most cases. The temporal changes in maximum LAI accumulation for all cultivars indicate that both measured and simulated values match each other. Evaluation with the measured data showed that performance of both models was realistic as indicated by the accurate simulation of crop phenology, LAI, biomass and grain yield against measured data. Climate variability results depicted that an increase in temperature from 0?C to 5?C resulted in a 60% average decline in the yield of wheat cultivars while increased CO2 increased yield similar to the combined effect of increased temperature and CO2. We concluded that to bring accuracy in the simulation outcomes of models, new cultivars should be calibrated to minimize uncertainty to allow judicious recommendations in response to climate variability.

Agricultural soils have the potential to be an important carbon (C) sink with proper management. The main goal of this study was to characterize C dynamics and net C exchange over two full crop years at two sites in the inland Pacific Northwest (iPNW). The iPNW is a highly productive dryland wheat growing region. The two measurement sites represent the low- and high-end of the regional precipitation gradient (250 and 550 mm year−1, respectively). The low rainfall site is in a winter wheat-fallow rotation, while the high rainfall site is under continuous cropping with a rotation that includes winter wheat and pulse crops. The net ecosystem exchange of CO2 (NEE) was monitored using the eddy covariance method. The winter wheat cropping years were strong CO2 sinks, with net uptake of 517 ± 26 and 524 ± 29 g C m−2 year−1 at the high- and low-rainfall sites, respectively. The fallow and pulse-crop years were close to neutral with respect to NEE, with a net uptake of 20 ± 38 g C m−2 year−1 for garbanzo beans at the high rainfall site, and a net loss of 3 ± 19 g C m−2 year−1 for the fallow season at the low rainfall site. Combining NEE with other carbon exchange terms, most importantly carbon import and export via seeding and harvest, gives the net ecosystem carbon balance (NECB). We found that even when harvest export was taken into account, both sites acted as net carbon sinks over the two year period: the NECB at the continuous cropping site was 202 ± 60 g C m−2, and the crop-fallow site had a NECB of 444 ± 34 g C m−2. These results present useful insights to field-scale C dynamics on finely resolved timescales. To understand the potential of soils as a long term C sink however, longer-term monitoring over multiple complete crop rotation cycles in combination with other field measurements or process-based models will be necessary.

The CropSyst model was parameterized and verified for garlic with data from six field experiments carried out in southern Spain from 2008 to 2011. The model simulated well crop dry matter production, N uptake, and yield from field plots with different N fertilization treatments. A garlic–wheat rotation was then simulated for a 30-year period to evaluate the role of wheat after garlic as a catch crop that utilizes and reduces the loss of residual soil N left at the time of garlic harvest. The scenarios studied were the combination of seven rates of mineral N fertilization in garlic (0, 50, 100, 150, 200, 250 and 300 kgN/ha) and three rates in wheat (30, 70 and 110 kgN/ha). Fertilization rates of 150 kgN/ha in garlic and 70 kgN/ha in wheat were found to provide the best tradeoff between N loss reductions (N leaching and N gas emissions) while attaining near-maximum yields. Increasing fertilization to 200 and 110 kgN/ha for garlic and wheat, respectively, rates not atypical in the region, had a minimum effect on yields but increased average N losses over the rotation by 19 kgN/ha/2-year (29%). Further reductions of N losses by decreasing N fertilization below the best tradeoff point are possible at the expense of crop yield.

Water scarcity is one of the most important problems of agroecosystems in Mediterranean and semiarid areas, especially for species such as vineyards that largely depend on irrigation. Actual evapotranspiration (ET) is a variable that represents water consumption of a crop, integrating climate and biophysical variables. Actual evapotranspiration models based on remote sensing data from visible bands of Sentinel-2, including Penman–Monteith–Stewart (RS-PMS) and Penman–Monteith–Leuning (RS-PML), were evaluated at different temporal scales in a Cabernet Sauvignon vineyard (Vitis vinifera L.) located in central Chile, and their performance compared with independent ET measurements from an eddy covariance system (EC) and outputs from models based on thermal infrared data from Landsat 7 and Landsat 8, such as Mapping EvapoTranspiration with high Resolution and Internalized Calibration (METRIC) and Priestley–Taylor Two-Source Model (TSEB-PT). The RS-PMS model showed the best goodness of fit for all temporal scales evaluated, especially at instantaneous and daily ET, with root mean squared error (RMSE) of 28.9 Wm−2 and 0.52 mm day−1, respectively, and Willmott agreement index (d1) values of 0.77 at instantaneous scale and 0.7 at daily scale. Additionally, both approaches of RS-PM model were evaluated incorporating a soil evaporation estimation method, one considering the soil water content (fSWC) and the other hand, using the ratio of accumulated precipitation and equivalent evaporation (fZhang), achieving the best fit at instantaneous scale for RS-PMS fSWC method with relative root mean squared error (%RMSE) of 15.2% in comparison to 58.8% of fZhang. Finally, the relevance of the RS-PMS model was highlighted in the assessment and monitoring of vineyard drip irrigation in terms of crop coefficient (Kc) estimation, which is one of the methods commonly used in irrigation planning, yielding a comparable Kc to the one obtained by the EC tower with a bias around 9%

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.

This research investigated the use of two relatively cost-effective devices for determining NH3 concentrations in naturally ventilated (NV) dairy barns including an Ogawa passive sampler (Ogawa) and a passive flux sampler (PFS). These samplers were deployed adjacent to sampling ports of a photoacoustic infrared multigas spectroscope (INNOVA), in a NV dairy barn. A 3-day deployment period was deemed suitable for both passive samplers. The correlations between concentrations determined with the passive samplers and the INNOVA were statistically significant (r = 0.93 for Ogawa and 0.88 for PFS). Compared with reference measurements, Ogawa overestimated NH3 concentrations in the barn by ∼ 14%, while PFS underestimated NH3 concentrations by ∼ 41%. Barn NH3 emission factors per animal unit (20.6-21.2 g d(-1) AU(-1)) based on the two passive samplers, after calibration, were similar to those obtained with the reference method and were within the range of values reported in literature.

Rainwater harvesting in small dams has good potential to provide supplementary agricultural irrigation during critical crop growth stages. Field experiments were conducted to evaluate cropping patterns: fallow–wheat (FW), mash–wheat (MaW), sorghum–wheat (SW), maize (grain)–wheat (MW), maize (grain)–gram (MG) and mung–canola (MuC); under two conditions: irrigated (Command area of Pira Fatehial small dam) and rainfed (Un-command area) on sandy loam soil during three years (2009 to 2011). Treatments were arranged in a randomized complete block design, three factor factorial (cropping patterns, irrigated/non-irrigated conditions and years) using four replications. The highest grain yield of wheat (5.6 Mg ha−1) was obtained from the MaW cropping pattern as compared to those from MW, FW and SW cropping patterns. Summer crops (sorghum fodder, maize grain and mash) performed excellent under irrigated conditions. Water use efficiency of wheat (14.3 kg ha−1mm−1) following mash under both the conditions exhibited higher values than when compared with those from SW, FW and MW cropping patterns. However, nutrient use efficiency in SW cropping pattern surpassed the others. Performance of all crops remained better under the irrigated condition during both all seasons and years, as rainfall deficiency was compensated by supplementary irrigations. Hence, this study concluded that farmers having water resources for supplemental irrigation should adopt the MW cropping pattern, based on grain yield, efficient utilization of available supplemental water and better utilization of nutrients. Similarly, based on improved nutrient utilization and monitory outputs, the MaW sequence should be followed in rain-fed areas for better resource management. Copyright © 2015 John Wiley & Sons, Ltd.

The response of wheat crops to elevated CO2 (eCO2 ) was measured and modelled with the Australian Grains Free-Air CO2 Enrichment experiment, located at Horsham, Australia. Treatments included CO2 by water, N and temperature. The location represents a semi-arid environment with a seasonal VPD of around 0.5 kPa. Over 3 years, the observed mean biomass at anthesis and grain yield ranged from 4200 to 10 200 kg ha-1 and 1600 to 3900 kg ha-1 , respectively, over various sowing times and irrigation regimes. The mean observed response to daytime eCO2 (from 365 to 550 μmol mol-1 CO2 ) was relatively consistent for biomass at stem elongation and at anthesis and LAI at anthesis and grain yield with 21%, 23%, 21% and 26%, respectively. Seasonal water use was decreased from 320 to 301 mm (P = 0.10) by eCO2 , increasing water use efficiency for biomass and yield, 36% and 31%, respectively. The performance of six models (APSIM-Wheat, APSIM-Nwheat, CAT-Wheat, CROPSYST, OLEARY-CONNOR and SALUS) in simulating crop responses to eCO2 was similar and within or close to the experimental error for accumulated biomass, yield and water use response, despite some variations in early growth and LAI. The primary mechanism of biomass accumulation via radiation use efficiency (RUE) or transpiration efficiency (TE) was not critical to define the overall response to eCO2 . However, under irrigation, the effect of late sowing on response to eCO2 to biomass accumulation at DC65 was substantial in the observed data (~40%), but the simulated response was smaller, ranging from 17% to 28%. Simulated response from all six models under no water or nitrogen stress showed similar response to eCO2 under irrigation, but the differences compared to the dryland treatment were small. Further experimental work on the interactive effects of eCO2 , water and temperature is required to resolve these model discrepancies.

Climate-friendly best management practices for mitigating and adapting to climate change (cfBMPs) include changes in crop rotation, soil management and resource use. Determined largely by precipitation gradients, specific agroecological systems in the inland Pacific Northwestern U.S. (iPNW) feature different practices across the region. Historically, these farming systems have been economically productive, but at the cost of high soil erosion rates and organic matter depletion, making them win-lose situations. Agronomic, sociological, political and economic drivers all influence cropping system innovations. Integrated, holistic conservation systems also need to be identified to address climate change by integrating cfBMPs that provide win-win benefits for farmer and environment. We conclude that systems featuring short-term improvements in farm economics, market diversification, resource efficiency and soil health will be most readily adopted by farmers, thereby simultaneously addressing longer term challenges including climate change. Specific “win-win scenarios” are designed for different iPNW production zones delineated by water availability. The cfBMPs include reduced tillage and residue management, organic carbon (C) recycling, precision nitrogen (N) management and crop rotation diversification and intensification. Current plant breeding technologies have provided new cultivars of canola and pea that can diversify system agronomics and markets. These agronomic improvements require associated shifts in prescriptive, precision N and weed management. The integrated cfBMP systems we describe have the potential for reducing system-wide greenhouse gas (GHG) emissions by increasing soil C storage, N use efficiency (NUE) and by production of biofuels. Novel systems, even if they are economically competitive, can come with increased financial risk to producers, necessitating government support (e.g., subsidized crop insurance) to promote adoption. Other conservation- and climate change-targeted farm policies can also improve adoption. Ultimately, farmers must meet their economic and legacy goals to assure longer-term adoption of mature cfBMP for iPNW production systems.

This paper presents the design and use of Representative Agricultural Pathways (RAPs) in regional integrated assessment of climate impacts. In the first part of the paper, we describe the role of pathways and scenarios in regional integrated assessment as well as the three RAPs developed for a study of dryland wheat-based systems in the U.S. Pacific Northwest. We use this example to illustrate the challenges associated with the development and implementation of RAPs, including the engagement of research team and stakeholders, the dimensionality problem in integrated assessment, incorporation of economic data, and quantification of uncertainties. In the second part, we illustrate the use of RAPs in the study of climate impacts on dryland wheat-based systems. Results show that the direct impacts of future climate projections through crop yields provide the largest source of uncertainty in the climate impact and vulnerability analysis, but the indirect impacts of climate change through price projections embedded in RAPs also play an important role in the analysis. We conclude that in addition to being an essential element in designing an integrated assessment at the regional level, the RAPs development process can facilitate stakeholder engagement and improve communication of climate impact assessments.

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., E.W. acknowledges support from the CSIRO project ‘Enhanced modelling of genotype by environment interactions’ and the project ‘Advancing crop yield while reducing the use of water and nitrogen’ jointly funded by CSIRO and the Chinese Academy of Sciences (CAS). Z.Z. received a scholarship from the China Scholarship Council through the CSIRO and the Chinese Ministry of Education PhD Research Program. P.M., A.M. and D.R. acknowledge support from the FACCE JPI MACSUR project (031A103B) through the metaprogram Adaptation of Agriculture and Forests to Climate Change (AAFCC) of the French National Institute for Agricultural Research (INRA). A.M. received the support of the EU in the framework of the Marie-Curie FP7 COFUND People Programme, through the award of an AgreenSkills fellowship under grant agreement No. PCOFUND-GA-2010-267196. S.A. and D.C. acknowledge support provided by the International Food Policy Research Institute (IFPRI), CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), the CGIAR Research Program on Wheat and the Wheat Initiative. C.S. was funded through USDA National Institute for Food and Agriculture award 32011-68002-30191. C.M. received financial support from the KULUNDA project (01LL0905 L) and the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (BMBF). F.E. received support from the FACCE MACSUR project (031A103B) funded through the German Federal Ministry of Education and Research (2812ERA115) and E.E.R. was funded through the German Federal Ministry of Economic Cooperation and Development (Project: PARI). M.J. and J.E.O. were funded through the FACCE MACSUR project by the Danish Strategic Research Council. K.C.K. and C.N. were funded by the FACCE MACSUR project through the German Federal Ministry of Food and Agriculture (BMEL). F.T., T.P. and R.P.R. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM); F.T. was also funded through the National Natural Science Foundation of China (No. 41071030). C.B. was funded through the Helmholtz project ‘REKLIM-Regional Climate Change: Causes and Effects’ Topic 9: ‘Climate Change and Air Quality’. M.P.R. and PD.A. received funding from the CGIAR Research Program on Climate Change, Agriculture, and Food Security (CCAFS). G.O'L. was funded through the Australian Grains Research and Development Corporation and the Department of Economic Development, Jobs, Transport and Resources Victoria, Australia. R.C.I. was funded by Texas AgriLife Research, Texas A&M University. B.B. was funded by USDA-NIFA Grant No: 2015-68007-23133.

Accurate carbon and water flux simulations for croplands are greatly dependent on high quality representation of management practices and meteorological conditions, which are key drivers of the surface-atmosphere exchange processes. Fourteen site-years of carbon and water fluxes were simulated using the CropSyst model over four agricultural sites in the inland Pacific Northwest (iPNW) US from October 1, 2011 to September 30, 2015. Model performance for field-scale net ecosystem exchange of CO2 (NEE) and evapotranspiration (ET) was evaluated by comparing simulations with long-term eddy covariance measurements. The model captured the temporal variations of NEE and ET reasonably well with an overall r of 0.78 and 0.80, and a low RMSE of 1.82 g C m−2 d−1 and 0.84 mm d−1 for NEE and ET, respectively. The model slightly underestimated NEE and ET by 0.51 g C m−2 d−1 and 0.09 mm d−1, respectively. ET simulations showed better agreement with eddy covariance measurements than NEE. The model performed much better for the sites with detailed initial conditions (e.g., SOC content) and management practice information (e.g., tillage type). The CropSyst results showed that the winter wheat fields could be annual net carbon sinks or close to neutral with the net ecosystem carbon balance (NECB) ranging from 92 to −17 g C m−2, while the spring crop fields were net carbon sources or neutral with an annual NECB of −327 to −3 g C m−2. Simulations for the paired tillage sites showed that the no-till site resulted in lower CO2 emissions for the crop rotations of winter wheat-spring garbanzo, but had higher carbon loss into the atmosphere for spring canola compared to the conventional tillage site. Water budgets did not differ significantly between the two tillage systems. Winter wheat in the high-rainfall area had higher crop yields and water use efficiency but emitted larger amounts of CO2 into the atmosphere than in the low-rainfall area. Based on model evaluations in this study, CropSyst appears promising as a tool to simulate field-scale carbon and water budgets and assess the effects of different management practices and local meteorological conditions for the wheat-based cropping systems in this region.

Motivated by global and regional challenges in food production and a broader consideration of ecosystem services, there has been a substantial increase in the demand for integrated agricultural systems models and spatially-distributed applications that can be used for regional and global assessments and as decision support tools. This demand marks a shift from earlier emphasis in single-crop point simulations and poses a significant challenge as cropping systems models need to improve and increase their capabilities to address multiple scales in a cohesive scientific manner and using updated computing platforms. In this article we discuss how the cropping systems model CropSyst has evolved to meet these new demands and provide some concepts for the future. Climate change and environmental concerns are driving the development of crop models.The cropping systems model CropSyst has evolved in response to new research needs.Our vision is to provide Internet-based open access to a library of CropSyst modules.Global communities of crop, climate, and economic modelers are emerging.