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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Assessing the effect of different treatments on decomposition rate of dairy manure.

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.

Reliable low-cost devices for monitoring ammonia concentrations and emissions in naturally ventilated dairy barns.

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.

Multimodel ensembles of wheat growth: many models are better than one.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

Self-consistent liquid-to-gas mass transfer calculations.

This work develops an alternative gas transfer calculation method to the two methods currently used in anaerobic digestion modelling. The current calculation methods are problematic because one is computationally stiff, while the other introduces an artificial overpressure. The new approach began by noting that the gas partial pressures are the same as the partial flows at the liquid/gas interface, and then used the self-consistency requirement to develop gas pressure equations which were used by a search algorithm. The new approach took about three iterations to achieve a flow precision better than 2x10(-7) mol h(-1) l(-1), and was self-consistent and stable even when working with eight gases.

A biogas meter with adjustable resolution and minimal back-pressure.

A new biogas meter was developed to satisfy the need for an adjustable resolution meter that has minimal back-pressure and wide flow rate capability. The new meter had three main components; a timed bellows pump that delivered fixed volumes, a pressure sensor, and a data logger. The meter was built from off-the-shelf components and was thus easy to build and cost effective. The meter also proved to be accurate, precise, sensitive, and simple to calibrate.