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The long-term understanding of the carbon budget of cropping systems needs to consider varying meteorological conditions, site/soil conditions, crop selection, and management decisions. On the Canadian Prairies, a variety of crops are used in yearly rotation, often not repeating the same crop within a 3-year period. We measured net ecosystem production (NEP) in two cropping systems in southern Manitoba, Canada, for the 2006–2016 period. One system had continuous annual crops, whereas the other had a perennial phase for 4 years. NEP was calculated using the flux-gradient method, where the carbon dioxide gradient was measured with a tunable-diode laser analyser and the transfer coefficient was based on momentum similarity theory with inputs from sonic anemometer-thermometers. NEP varied from −100 (source) to +300 (sink) g C m −2 y −1 among four adjacent 4-ha fields and 10 years, depending on annual crop (corn, faba, spring wheat, barley, rapeseed, soybean) and environmental conditions. There was no period of 3 years or greater when NEP in the continuous annual crop rotation was different from carbon neutral. Inserting a 4-year perennial phase of an alfalfa/grass mix created a statistically significant carbon sink for NEP (P = 0.04), but the perennial period itself was carbon neutral. Enhanced ecosystem respiration was observed following termination of the perennial crop. Harvest practices varied from no harvest (in the establishment year of the perennial crop), grain removal only, grain + straw removal, and straight cutting of green biomass for silage. Once harvest removals were included, 3 of the 4 fields were carbon neutral, but one annual field had net carbon losses for some periods. Ecosystem respiration averaged about twice as large as harvest removals. Net carbon (mean ±S.E.) over the 11-year period for the annual cropping system (−78 ± 51 g C m −2 y −1 ) was not different from the annual-perennial system (−48 ± 47 g C m −2 y −1 ). [ABSTRACT FROM AUTHOR]

This study provides statistical emulators of crop yields based on global gridded crop model simulations from the Inter-Sectoral Impact Model Intercomparison Project Fast Track project. The ensemble of simulations is used to build a panel of annual crop yields from five crop models and corresponding monthly summer weather variables for over a century at the grid cell level globally. This dataset is then used to estimate, for each crop and gridded crop model, the statistical relationship between yields, temperature, precipitation and carbon dioxide. This study considers a new functional form to better capture the non-linear response of yields to weather, especially for extreme temperature and precipitation events, and now accounts for the effect of soil type. In- and out-of-sample validations show that the statistical emulators are able to replicate spatial patterns of yields crop levels and changes overtime projected by crop models reasonably well, although the accuracy of the emulators varies by model and by region. This study therefore provides a reliable and accessible alternative to global gridded crop yield models. By emulating crop yields for several models using parsimonious equations, the tools provide a computationally efficient method to account for uncertainty in climate change impact assessments. [ABSTRACT FROM AUTHOR]

Highlights: [•] RZ-SHAW model simulated actual evapotranspiration (AET) well as compared to latent heat fluxes obtained from the eddy covariance measurement. [•] RZ-SHAW model also simulated potential evapotranspiration (PET) well as compared to PET from weighing lysimeter. [•] Canopy temperature is well simulated with the RZ-SHAW model, which may be used as an index for crop water stresses. [Copyright &y& Elsevier]

Abstract: This study evaluated the performance of digital photography for measuring the green leaf area index (LAI) and green plant area index (GAI) of field crops by quantifying gap fraction either at nadir or at a 57.5° view angle. Clumping index was estimated using the logarithmic averaging method of Lang and Xiang (1986) to derive the total GAI. Measurement uncertainty associated with foliage inclination and view angles was assessed analytically, as was image classification. The GAI derived for corn, soybean and wheat crops was strongly linearly correlated with the destructive GAI for both the nadir and the 57.5° photographic methods (R 2 >0.83, root mean square error<0.63). Because the destructive LAI and GAI exhibited strong linear correlations for soybean and corn, LAI could be predicted from photographic methods. The LAI and GAI of the wheat canopy were poorly correlated because of a significant proportion of non-leaf tissues contributing to photosynthesis after the start of stem elongation. Clumping had a greater effect on the nadir photographic method than on the 57.5° method. The effective GAI from nadir viewing was better correlated to the destructive GAI than the photographic GAI, whereas the effective GAI from the 57.5° photographic method provided a greater underestimation of the destructive GAI than the photographic GAI. The extinction coefficient at nadir viewing was found to be 22% larger than the assumed value (i.e. k =0.61 vs. the theoretical value of 0.5). When gap reduction reached the asymptotic saturation level with increasing LAI, higher classification accuracy was required to achieve acceptable LAI measurement accuracy. The 57.5° photographic method was more affected by classification accuracy when LAI was higher than 1.5. With crop canopy development, the decrease in gap size and the decline in signal strength from shadows mean that high-quality photos are required to effectively differentiate gaps from plant tissues. [Copyright &y& Elsevier]

Abstract: The Missouri River Basin (MRB) is the largest river basin in the U.S. and is one of the most important crop and livestock-producing regions in the world. In a previous study of associations between decadal climate variability (DCV) phenomena and hydro-meteorological (HM) variability in the MRB for the March–August period, and their impacts on stream flow in the MRB, it was found that positive and negative phases of the Pacific Decadal Oscillation (PDO), the tropical Atlantic sea-surface temperature gradient variability (TAG), and the west Pacific Warm Pool (WPWP) temperature variability were significantly associated with decadal variability in precipitation and surface air temperature in the MRB, with combinations of various phases of these DCV phenomena associated with dry, wet, or neutral HM conditions. It was also found that these DCV phenomena impact stream flow in the MRB substantially via their association with MRB hydro-meteorology. In the present study, the Erosion Productivity Impact Calculator model, also known as the Environmental Policy Integrated Climate (EPIC) model, calibrated and validated for the MRB, was used to simulate yields of dryland corn (Zea mays L.) and spring and winter wheat (Triticum aestivum L.), in response to HM anomalies associated with the three DCV phenomena. Realistic values of indices of the three DCV phenomena have major impacts on crop yields, as much as 40–50% of average yield in some locations in the MRB and also evident in MRB-aggregated crop yields; however, our results show that the impacts can be location-specific. Since each of the three DCV phenomena can persist in one phase or another for several years to a decade or longer, and since the simultaneous correlation among these phenomena is negligibly small, their combined and cumulative positive/negative effects on the MRB HM and agricultural production can be dramatic in this major American and global “bread basket”. In addition, EPIC''s success in simulating long-term crop yields in the MRB, taking technology trends into account, suggests that, if the evolution of major DCV phenomena can be forecast, it may be possible to forecast, as well, some multiyear to decadal measure of crop yields in the MRB with some skill. [Copyright &y& Elsevier]

Abstract: Net carbon dioxide ecosystem exchange (NEE) was measured over three years (2006–2008) in a maize/faba/spring-wheat crop rotation in the Red River Valley of southern Manitoba, Canada using the flux-gradient method. The turbulent transfer coefficients were obtained using a similarity-theory, eddy-covariance based aerodynamic method and the carbon dioxide vertical gradient was measured with a tunable diode laser analyzer. The system measured 30-min-average fluxes over each of four plots sequentially to test reduced and intensive tillage treatments. Annual cumulative NEE was −720, 70 and −2400kgCha−1 yr−1 in the maize, faba and spring-wheat crop years, respectively. Enhanced respiration in the faba year was largely responsible for the loss, whereas the large gain in the spring-wheat year was a combination of high photosynthetic activity and reduced respiration. The three years experienced sequentially cooler and wetter years that likely affected the crops. After accounting for harvest removals, the net ecosystem loss was 510, 3140 and −480(gain)kgCha−1 yr−1 for the respective crop years, summing to a three-year loss of 3170kgCha−1. A recent conversion from intensive tillage to a reduced tillage practice showed no difference because of relatively large variability among the four plots. [Copyright &y& Elsevier]

• We determined frequency of environment types (ENVT) for US maize, soybean, and wheat. • Each ENVT corresponded to a combination of drought and heat stress during critical period. • Inter-annual variation in drought and heat stress was higher than spatial variation. • The ENVT scheme explained observed variation in average actual yield for the three crops. • ENVTs can be used to link field-year experiments with target production environments. The impact of agricultural technologies on crop yield is influenced by the environment type (ENVT) as determined by weather and soil. Understanding the correlation between the ENVT of the testing site in relation to the ENVT of the target production region is important for the evaluation and scaling out of agricultural technologies. Here we propose and apply the first explicit method to characterize ENVTs for major rainfed maize, soybean, and wheat producing regions in the United States. We combined a tested spatial framework, Technology Extrapolation Domain (TED), with crop modeling, long-term (30-y) daily weather records, and soil and management databases to calculate the frequency of ENVTs per crop for major harvested areas. Each ENVT was determined based on the intensity of drought and heat stress during key crop stages for yield determination. The ENVT repeatability was calculated based on the frequency of the most dominant ENVT in each TED. We found that inter-annual variation in drought and heat stress was larger than spatial variation. Our ENVTs explained 2x to 7x larger portion of the variance in actual yield compared to the existing TED framework that is based on long-term annual climate means and soil water storage. For maize and soybean, ca. 30% of their harvested area was located in TEDs with highly repeatable ENVTs (>66% of years). In contrast, only 15% of the wheat harvested area was located in TEDs with high ENVT repeatability. In comparison to the TED framework, the ENVTs defined here can help better capture G×E×M interactions and determine the environmental correlation between testing sites and target production environments. [ABSTRACT FROM AUTHOR]

A national assessment (NA) evaluated the potential consequences of climate change and variability on the agriculture, water resources, as well as other economic and natural resource sectors in the United States. As part of this process, we used scenarios of the HadCM2 GCM and the EPIC agroecosystem model to evaluate climate-change impacts on crop yields and ecosystem processes. Baseline climate data were obtained from national records for 1961–1990. The scenario runs for 2025–2034 and 2090–2099 were extracted from a HadCM2 run. EPIC was run on 204 representative farms under current climate and two 10-year periods centered on 2030 and 2095, each at CO2 concentrations of 365 and 560 ppm. Crops were simulated under both dryland and irrigated management, with irrigation water supply estimates taken from the HUMUS simulations in Paper 1. Texas, New Mexico, Colorado, Utah, Arizona, and California are projected to experience significant temperature increases by 2030. Slight cooling is expected by 2030 in Alabama, Florida, Maine, Montana, Idaho, and Utah. Larger areas are projected to experience increased warming by 2095. Uniform precipitation increases are expected by 2030 in the northeast. These increases are predicted to expand to the eastern half of the country by 2095. Regionally, dryland corn yields could increase, decrease or remain unchanged under the two scenarios. EPIC simulated yield increases for the Great Lakes, Corn Belt and Northeast regions. Simulated yields of irrigated corn were predicted to increase in almost all regions. Soybean yields could decrease in the Northern and Southern Plains, the Corn Belt, Delta, Appalachian, and Southeast regions and increase in the Lakes and Northeast regions. Simulated wheat yields exhibited upward trends under scenarios of climate-change. Evapotranspiration in dryland corn is expected to increase in both future periods while water-use efficiency will decrease. National corn production in 2030 and 2095 could be affected by changes in three major producing regions. In 2030, corn production could increase in the Corn Belt and Lakes regions but decrease in the Northern Plains leading to an overall decrease in national production. National wheat production is expected to increase during both future periods. A proxy indicator was developed to provide a sense of where in the country, and when water would be available to satisfy change in irrigation demand for corn and alfalfa production as these are influenced by the HadCM2 scenarios and CO2-fertilization. Irrigation requirement by irrigated crops declines under these scenarios as transpiration is suppressed. [Copyright &y& Elsevier]

• LAI can explain 41%−64% T/ET variation;. • WUE c was mainly controlled by climatic factors (i.e., T a , VPD, R n , T s);. • WUE e was jointly controlled by LAI and climatic factors (T a , EF);. • LAI exerted stronger controls on WUE e than climatic factors by regulating T/ET. • Carbon uptake regulated WUE e more, while water consumption controlled WUE c. Understanding the interactive responses of agroecosystem water use efficiency (WUE) to climatic and vegetation factors is significant for revealing how ecohydrological processes vary between the carbon and water cycles in the changing climate. Based on data from 11 eddy covariance flux sites for a total of 32 site-years in four typical agroecosystems, including winter wheat (Triticum aestivum L.), paddy rice (Oryza sativa L.), soybean (Glycine max L. Merr.) and summer maize (Zea mays L.), evapotranspiration (ET) of the four crops was partitioned based on the theory of underlying WUE. Seasonal dynamics of transpiration (T), ET, gross primary production (GPP), T-based WUE at the canopy level (WUE c) and ET-based WUE at the agroecosystem level (WUE e) were investigated. A series of climatic and vegetation factors affecting WUE c and WUE e were quantified using the path analysis method. T/ET was 0.67 ± 0.04 for summer maize, followed by winter wheat, soybean and paddy rice, with values of 0.65 ± 0.07, 0.60 ± 0.05 and 0.57 ± 0.02, respectively. T, ET, GPP and WUE e increased as crops grew but decreased as crops matured. However, WUE c did not change as apparently as WUE e since T and GPP changed synchronously throughout the growing season. Summer maize possessed the largest WUE c of 5.69 ± 0.29 g C kg−1 H 2 O, followed by winter wheat, soybean and paddy rice, with values of 4.47 ± 0.87, 3.79 ± 0.20 and 2.60 ± 0.17 g C kg−1 H 2 O, respectively. WUE e followed the same order as that of WUE c among the four croplands, with values of 3.32 ± 0.71, 2.73 ± 0.81, 1.90 ± 0.10 and 1.47 ± 0.09 g C kg−1 H 2 O, respectively. WUE c was mainly regulated by canopy water consumption than carbon sequestration, while carbon sequestration regulated WUE e more than water consumption at the ecosystem scale. WUE c was mainly controlled by climatic factors (vapor pressure deficit, net radiation, air temperature and soil temperature) in the croplands, while the leaf area index exerted stronger controls on WUE e variations than climatic factors by its strong regulation on T/ET in the four croplands. [ABSTRACT FROM AUTHOR]

• Intercropped wheat had higher water use efficiency than sole wheat. • Wheat outcompeted maize for soil water during the co-growth period. • Transpiration was higher in wheat border rows than sole wheat, especially during the dry period. • A method for evapotranspiration partitioning in wheat/maize strips was proposed. Climate change will likely intensify water shortages, so more attention should be given to the efficient use of agricultural water in agricultural production, especially under rainfed conditions. Therefore, studying and understanding crop water consumption amounts and patterns is of great importance. However, the amount of water consumed by the component crops in strip intercropping systems has received little attention. To close this knowledge gap, a two-year field experiment of sole wheat (Triticum aestivum L.), sole maize (Zea mays L.), and wheat/maize strip intercropping was carried out under rainfed conditions. Soil water content, sap flow, soil water evaporation, and evaporation of canopy interception were measured both in intercrops and sole crops. Soil water movement between strips, evapotranspiration components, and water use efficiency were calculated. Compared with corresponding sole crops, intercropping increased water consumption of wheat and maize during the growing period of each crop. Intercropped wheat obtained soil water from the adjacent maize strip during the co-growth period, while intercropped maize obtained soil water from the adjacent wheat strip after wheat harvest. The amount of water obtained by maize was much less than the amount of water obtained by wheat. The transpiration of intercropped wheat was greater than that of sole wheat, but the transpiration of intercropped maize was lower than that of sole maize. Compared with sole cropping, intercropping increased the sap flow rate of wheat in border rows but reduced sap flow rate of maize in border rows, and this phenomenon was pronounced in the case of drought. After wheat harvest, soil water evaporation from the intercropped wheat strip was less than that from sole wheat, and evaporation from the intercropped maize strip was significantly higher than that of sole maize. The two-year average water use efficiency of intercropped wheat was 32% more than that of sole wheat, and water use efficiency of intercropped maize was 10% less than that of sole maize. These results can improve the understanding of water consumption in wheat and maize of intercropping from the point of evapotranspiration partitioning, and provide implications for water management of intercropping system under rainfed conditions. [ABSTRACT FROM AUTHOR]

• A plastic phenology model was developed for simulating crop reproductive phase. • The model assumes a dynamic response of development rate to temperature. • Wheat, rice, and maize phenology observations under varied conditions were used. • The model effectively reduced phenology simulation error in a simple and effective way. Understanding how crop development rate responds to the environment provides the basis for evaluating the impact of climate change on crop yield. In most crop simulation models, temperature response functions of development rate during the reproductive growth period (RGP) are assumed to only vary with temperature and not with other environmental factors. However, studies have indicated that the response functions may be plastic with other factors. Until now, little attention has been paid to this type of response. Here, using extensively collected field observations and data from intentionally designed interval planting experiments with winter wheat (Triticum aestivum L.), rice (Oryza sativa L.), and spring maize (Zea mays L.), we show that temperature response functions during RGP are plastic with day of year of flowering/heading (DOY R). Coefficients of determination between DOY R and development rate were significant for 69% sites. Partial correlation coefficients between development rate, temperature, and DOY R suggest that DOY R explains almost the same variability in maturity date as temperature. The plastic model was developed by coupling DOY R with a linear temperature response function. The model can improve the fitting efficiency by 112%, while dependency between DOY R and temperature explains less than 25% of this improvement. The average RMSEs of simulated maturity date estimated by the plastic model in the three crops were 2.1, 2.5, and 3.7 d, respectively, while the corresponding values given by widely applied traditional models were 3.1, 6.5, and 7.4 d, respectively. Therefore, the plastic model can reduce simulation error by half. Moreover, simulation errors resulting from the plastic model have less systematic bias than traditional models. The plastic model simply and effectively provides accurate estimates of crop maturity and reduces the system deviation of the estimates. Coupling the plastic model of crop development with crop simulation models will likely decrease uncertainties in simulated yield under warming conditions. Additionally, results of this study will encourage future studies of other phenotype plasticity considered in current crop simulation models. [ABSTRACT FROM AUTHOR]