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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.

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].

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. 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. An integrated methodology that includes climate, crop, economic and life cycle assessment models was developed to explore the climate adaptation and mitigation opportunities throughout the US potato and tomato supply chains. This study shows 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.

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.

Geospatial crop water use mapping is critical for field-scale site-specific irrigation management. Landsat 7/8 satellite imagery with a widely adopted METRIC (Mapping Evapotranspiration at high Resolution with Internalized Calibration) energy balance model (LM approach) estimates accurate evapotranspiration (ET) but limits field-scale spatiotemporal (30 m pixel&minus, 1, ~16 days) mapping. A study was therefore conducted to map actual ET of commercially grown irrigated-field crops (spearmint, potato, and alfalfa) at very high-resolution (7 cm pixel&minus, 1). Six small unmanned aerial system (UAS)-based multispectral and thermal infrared imagery campaigns were conducted (two for each crop) at the same time as the Landsat 7/8 overpass. Three variants of METRIC model were used to process the UAS imagery, UAS-METRIC-1, -2, and -3 (UASM-1, -2, and -3) and outputs were compared with the standard LM approach. ET root mean square differences (RMSD) between LM-UASM-1, LM-UASM-2, and LM-UASM-3 were in the ranges of 0.2&ndash, 2.9, 0.5&ndash, 0.9, and 0.5&ndash, 2.7 mm day&minus, 1, respectively. Internal calibrations and sensible heat fluxes majorly resulted in such differences. UASM-2 had the highest similarity with the LM approach (RMSD: 0.5&ndash, 0.9, ETdep,abs (daily ET departures): 2&ndash, 14%, r (Pearson correlation coefficient) = 0.91). Strong ET correlations between UASM and LM approaches (0.7&ndash, 0.8, 0.7&ndash, 0.8, and 0.8&ndash, 0.9 for spearmint, potato, and alfalfa crops) suggest equal suitability of UASM approaches as LM to map ET for a range of similar crops. UASM approaches (Coefficient of variation, CV: 6.7&ndash, 24.3%) however outperformed the LM approach (CV: 2.1&ndash, 11.2%) in mapping spatial ET variations due to large number of pixels. On-demand UAS imagery may thus help in deriving high resolution site-specific ET maps, for growers to aid in timely crop water management.

With the decline of operational river gauges monitoring sediments, a viable means of quantifying sediment transport is needed. In this study, we address this issue by applying relationships between hydraulic geometry of river channels, water discharge, water-leaving surface reflectance (SR), and suspended sediment concentration (SSC) to quantify sediment discharge with the aid of space-based observations. We examined 5490 Landsat scenes to estimate water discharge, SSC, and sediment discharge for the period from 1984 to 2017 at nine gauging sites along the Upper Mississippi River. We used recent advances in remote sensing of fluvial systems, such as automated river width extraction, Bayesian discharge inference with at-many-stations hydraulic geometry (AMHG), and SSC-SR regression models. With 621 Landsat scenes available from all the gauging sites, the results showed that the water discharge and SSC retrieval from Landsat imagery can yield reasonable sediment discharge estimates along the Upper Mississippi River. An overall relative bias of &minus, 25.4, mean absolute error (MAE) of 6.24 &times, 104 tonne/day, relative root mean square error (RRMSE) of 1.21, and Nash&ndash, Sutcliffe Efficiency (NSE) of 0.49 were obtained for the sediment discharge estimation. Based on these statistical metrics, we identified three of the nine gauging sites (St. Louis, MO, Chester, IL, and Thebes, IL), which were in the downstream portion of the river, to be the best locations for estimating water and sediment discharge using Landsat imagery.

Wheat grain protein concentration is an important determinant of wheat quality for human nutrition that is often overlooked in efforts to improve crop production. We tested and applied a 32‐multi‐model ensemble to simulate global wheat yield and quality in a changing climate. Potential benefits of elevated atmospheric CO2 concentration by 2050 on global wheat grain and protein yield are likely to be negated by impacts from rising temperature and changes in rainfall, but with considerable disparities between regions. Grain and protein yields are expected to be lower and more variable in most low‐rainfall regions, with nitrogen availability limiting growth stimulus from elevated CO2. Introducing genotypes adapted to warmer temperatures (and also considering changes in CO2 and rainfall) could boost global wheat yield by 7% and protein yield by 2%, but grain protein concentration would be reduced by −1.1 percentage points, representing a relative change of −8.6%. Climate change adaptations that benefit grain yield are not always positive for grain quality, putting additional pressure on global wheat production., B.L received support from the International Food Policy Research Institute (IFPRI) through the Global Futures and Strategic Foresight project, the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS) and the CGIAR Research Program on Wheat. A.M. received support from the EU Marie Curie FP7 COFUND People Programme, through an AgreenSkills fellowship under grant agreement no. PCOFUND‐GA‐2010‐267196. PM, A.M., D.R., and D.W. 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). L.X. and Y.Z. were supported by the National High‐Tech Research and Development Program of China (2013AA100404), the National Natural Science Foundation of China (31271616), the National Research Foundation for the Doctoral Program of Higher Education of China (20120097110042), and the Priority Academic Program Development of Jiangsu Higher Education Institutions (PAPD). F.T. and Z.Z. were supported by the National Natural Science Foundation of China (41571088, 41571493 and 31561143003). R.R. received support from the German Ministry for Research and Education (BMBF) through project SPACES‐LLL. Rothamsted Research receives support from the Biotechnology and Biological Sciences Research Council (BBSRC) Designing Future Wheat programme [BB/P016855/1]. M.J. and J.E.O. were supported by Innovation Fund Denmark through the MACSUR project. L.X. and Y.G. acknowledge support from the China Scholarship Council. M.B and R.F. were funded by JPI FACCE MACSUR2 through the Italian Ministry for Agricultural, Food and Forestry Policies and thank A. Soltani from Gorgan Univ. of Agric. Sci. & Natur. Resour. for his support. R.P.R., T.P., and F.T. received financial support from the FACCE MACSUR project funded through the Finnish Ministry of Agriculture and Forestry (MMM) and from the Academy of Finland through the projects NORFASYS (decision nos. 268277 and 292944) and PLUMES (decision nos. 277403 and 292836). K.C.K. and C.N. received support from the German Ministry for Research and Education (BMBF) within the FACCE JPI MACSUR project. S.M. and C.M. acknowledge financial support from the MACMIT project (01LN1317A) funded through BMBF. G.J.O. and G.J.F. acknowledge support from the Victorian Department of Economic Development, Jobs, Transport and Resources, the Australian Department of Agriculture and Water Resources, The University of Melbourne and the Grains Research Development Corporation, Australia. P.K.A.'s work was implemented as part of the CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), which is carried out with support from the CGIAR Trust Fund and through bilateral funding agreements. For details please visit https://ccafs.cgiar.org/donors. The views expressed in this document cannot be taken to reflect the official opinions of these organizations.. B.B. received financial support from USDA NIFA‐Water Cap Award 2015‐68007‐23133. F.E. acknowledges support from the FACCE JPI MACSUR project through the German Federal Ministry of Food and Agriculture (2815ERA01J) and from the German Science Foundation (project EW 119/5‐1).

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.

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...