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Accurate evaluation of crop performance and yield prediction at a sub-field scale is essential for achieving high yields while minimizing environmental impacts. Two important approaches for improving agronomic management and predicting future crop yields are the spatial stability of historic crop yields and in-season remote sensing imagery. However, the relative accuracies of these approaches have not been well characterized. In this study, we aim to first, assess the accuracies of yield stability and in-season remote sensing for predicting yield patterns at a sub-field resolution across multiple fields, second, investigate the optimal satellite image date for yield prediction, and third, relate bi-weekly changes in GCVI through the season to yield levels. We hypothesize that historical yield stability zones provide high accuracies in identifying yield patterns compared to within-season remote sensing images. To conduct this evaluation, we utilized biweekly Planet images with visible and near-infrared bands from June through September (2018–2020), along with observed historical yield maps from 115 maize fields located in Indiana, Iowa, Michigan, and Minnesota, USA. We compared the yield stability zones (YSZ) with the in-season remote sensing data, specifically focusing on the green chlorophyll vegetative index (GCVI). Our analysis revealed that yield stability maps provided more accurate estimates of yield within both high stable (HS) and low stable (LS) yield zones within fields compared to any single-image in-season remote sensing model. For the in-season remote sensing predictions, we used linear models for a single image date, as well as multi-linear and random forest models incorporating multiple image dates. Results indicated that the optimal image date for yield prediction varied between and within fields, highlighting the instability of this approach. However, the multi-image models, incorporating multiple image dates, showed improved prediction accuracy, achieving R2 values of 0.66 and 0.86 by September 1st for the multi-linear and random forest models, respectively. Our analysis revealed that most low or high GCVI values of a pixel were consistent across the season (77%), with the greatest instability observed at the beginning and end of the growing season. Interestingly, the historical yield stability zones provided better predictions of yield compared to the bi-weekly dynamics of GCVI. The historically high-yielding areas started with low GCVI early in the season but caught up, while the low-yielding areas with high initial GCVI faltered. In conclusion, the historical yield stability zones in the US Midwest demonstrated robust predictive capacity for in-field heterogeneity in stable zones. Multi-image models showed promise for assessing unstable zones during the season, but it is crucial to link these two approaches to fully capture both stable and unstable zones of crop yield. This study provides opportunities to achieve better precision management and yield prediction by integrating historical crop yields and remote sensing techniques. [ABSTRACT FROM AUTHOR]

We evaluated the optical and thermal reflectance of maize (Zea mays L.) interseeded with cover crops using remotely sensed canopy temperature and multispectral imagery. In 2017 and 2018 annual ryegrass (Lolium multiflorum Lam.), crimson clover (Trifolium incarnatum L.), oilseed radish (Raphanus sativus L.), and a mixture of annual ryegrass and crimson clover were interseeded in maize at V3 and V6 at three different cover crop seeding rates in small research plots at two experimental farm sites within the network of Michigan State University. The same cover crop species were interseeded in maize at V3 and V6 at a single seeding rate in on‐farm replicated strip trials and also a full‐scale field trial at five locations in Michigan. Canopy temperature and multispectral reflectance were remotely measured 10–12 times throughout each season at all sites using fixed wing aircraft at 1‐m spatial resolution. Optical and thermal reflectance were also measured remotely using an unmanned aerial vehicle (UAV) with 2‐cm spatial resolution three times during the growing season at the small plot sites. Normalized difference vegetation index (NDVI) and normalized difference red‐edge (NDRE) were calculated for each of the experimental sites. No significant differences were detected between the interseeded treatments and control with regards to the optical and thermal reflectance and maize grain yield. Variability at field scale was due to inherent differences and not caused by the interseeding treatments. Core Ideas: Optical and thermal reflectance did not show any differences in cover crops treatments.Interseeded cover crops did not affect maize growth and yield.At field scale, the inherent spatial variability had greater impact than interseeded cover crop treatments. [ABSTRACT FROM AUTHOR]

Smallholder farmers in sub‐Saharan Africa (SSA) currently grow rainfed maize with limited inputs including fertilizer. Climate change may exacerbate current production constraints. Crop models can help quantify the potential impact of climate change on maize yields, but a comprehensive multimodel assessment of simulation accuracy and uncertainty in these low‐input systems is currently lacking. We evaluated the impact of varying [CO2], temperature and rainfall conditions on maize yield, for different nitrogen (N) inputs (0, 80, 160 kg N/ha) for five environments in SSA, including cool subhumid Ethiopia, cool semi‐arid Rwanda, hot subhumid Ghana and hot semi‐arid Mali and Benin using an ensemble of 25 maize models. Models were calibrated with measured grain yield, plant biomass, plant N, leaf area index, harvest index and in‐season soil water content from 2‐year experiments in each country to assess their ability to simulate observed yield. Simulated responses to climate change factors were explored and compared between models. Calibrated models reproduced measured grain yield variations well with average relative root mean square error of 26%, although uncertainty in model prediction was substantial (CV = 28%). Model ensembles gave greater accuracy than any model taken at random. Nitrogen fertilization controlled the response to variations in [CO2], temperature and rainfall. Without N fertilizer input, maize (a) benefited less from an increase in atmospheric [CO2]; (b) was less affected by higher temperature or decreasing rainfall; and (c) was more affected by increased rainfall because N leaching was more critical. The model intercomparison revealed that simulation of daily soil N supply and N leaching plays a crucial role in simulating climate change impacts for low‐input systems. Climate change and N input interactions have strong implications for the design of robust adaptation approaches across SSA, because the impact of climate change in low input systems will be modified if farmers intensify maize production with balanced nutrient management. [ABSTRACT FROM AUTHOR]

Leaching from annual corn (Zea mays L.) crops is a primary source of nitrate (NO3−) pollution of ground and surface waters. Here, we compare NO3− losses from no‐till corn with losses from various alternative perennial cropping systems (switchgrass [Panicum virgatum L.], miscanthus [Miscanthus ×giganteus J.M. Greef & Deuter ex Hodkinson & Renvoiz], a native grass mixture, and restored prairie), as well as hybrid poplar (Populus nigra L. × P. maximowiczii A. Henry 'NM6'), all grown on a well‐drained soil in Michigan. Soil water was sampled from below the root zone using suction cup samplers during nonfrozen periods (March–November) between 2009 and 2016. Leaching was estimated from NO3− concentrations in soil water and modeled drainage (percolation) rates. Drainage rates were not significantly different among crops, constituting ∼30% of total annual precipitation. Aboveground net primary production (Mg ha−1 yr−1) averaged across the 7 yr was highest in poplar (30.8 ± 1.9 [SE]) followed by miscanthus (23.9 ± 2.4) and corn (20.4 ± 0.9). Volume‐weighted mean NO3− concentrations (mg N L−1) and NO3− leaching (kg ha−1 yr−1) averaged across the 7 yr were 9.2 and 34.1, 2.3 and 5.9, and 3.0 and 7.2, respectively, for corn, perennial grasses and poplar. Approximately 10 to 32% of applied N was lost as NO3− from these crops, with the highest percent losses from poplar (32%) followed by corn (20%). Perennial cropping systems leached considerably more NO3− in first few years after planting, but over 7 yr they lost much less NO3− than corn. Perennial crops may therefore help ameliorate NO3− pollution in agricultural landscapes even if they receive modest N fertilization. Core Ideas: Over 7 yr, perennial grasses and poplar trees leached much less nitrate than cornNitrate leaching from grasses and poplar was comparable with corn for 1–2 yr after planting.Perennial crops can ameliorate nitrate pollution in agricultural landscapes. [ABSTRACT FROM AUTHOR]

Highlights • Integrates advantages of the microwave and optical satellite data. • Optical image is better at representing maize field boundaries than the SAR image. • Both off or in-season optical images are useful to increase the flexibility of our proposed method. • Time-series PolSAR images are able to distinguish maize from other crops. • Random Forest can be used to reduce data redundancy without reducing accuracy. Abstract Due to its ability to penetrate the cloud, Synthetic Aperture Radar (SAR) has been a great resource for crop mapping. Previous research has verified the applicability of SAR imagery in object-oriented crop classification, however, speckle noise limits the generation of optimal segmentation. This paper proposed an innovative SAR-based maize mapping method supported by optical image, Gaofen-1 PMS, based segmentation, named as parcel-based SAR classification assisted by optical imagery-based segmentation (os-PSC). Polarimetric decomposition was applied to extract polarimetric parameters from multi-temporal RADARSAT-2 data. One Gaofen-1 image was then used for parcel extraction, which was the basic unit for SAR image analysis. The final step was a multi-step classification for final maize mapping including: the potential maize mask extraction, pure/mixed maize parcel division and an integrated maize map production. Results showed that the overall accuracy of the os-PSC method was 89.1%, higher than those of pixel-level classification and SAR-based segmentation methods. The comparison between optical- and SAR-based segmentation demonstrated that optical-based segmentation would be better at representing maize field boundaries than the SAR-based segmentation. Moreover, the parcel- and pixel-level integrated classification will be suitable for many agricultural systems with small landownership where inter-cropping is common. Through integrating advantages of the SAR and optical data, os-PSC shows promising potentials for crop mapping. [ABSTRACT FROM AUTHOR]

Maize (Zea mays L.) yield and its response to nitrogen (N) are affected by the spatial variability of the interaction between weather, management, and soil properties. The objectives of this study were (i) to evaluate the response of spatial variability of maize yield by homogeneous zones (HZs) to different N fertilizer rates under rainfed conditions, (ii) to test the ability of the SALUS (System Approach to Land Use Sustainability) model to simulate the effects of N rates on maize yield under rainfed and irrigated conditions, and (iii) to estimate spatial and temporal N fertilizer response risk in maize through the use of long-term simulations. In two field experiments in Parana, Argentina (-31.8333°, -60.5167°) in 2011 (Field 1) and 2012 (Field 2), four fertilization treatments (0, 70, 140, and 210 kg ha-1) were evaluated in different HZs. The SALUS model was used to evaluate spatial variability in yield, N response, and net revenue over the long-term period (1971-2012). Results showed that yield was significantly affected by N rate (p < 0.01) in both fields and by HZ in Field 2 (p < 0.05), whereas N response was only affected by N rate. Simulated yield was significantly affected by N. The model accounted for the spatial variability, showing HZ effect (p < 0.001) and a significant HZ × N interaction (p < 0.0001). The optimal economic return N rate differed between HZs in both fields. Our procedure demonstrated the ability to improve N management by the selection of appropriate N rates across the field, thereby improving N use efficiency and growers' profits and reducing the potential for negative environmental impacts. [ABSTRACT FROM AUTHOR]

Abstract: Estimation of nitrate leaching from agricultural systems is critical to environment impact studies. Simulation models can help to assess and understand the dynamics of nitrates in the soil and in relation to fertilizer types and agronomical management. The main hypothesis of this study was that the long term annual application of mineral and organic N fertilizers, in irrigated sandy soil areas increases dramatically the quantity of NO3 − in the soil for potential nitrate leaching. The main objectives of this research were then to quantify inorganic N concentration in soil layers affected by mineral and organic N (manure and slurry) fertilizers; and to test the SALUS crop simulation model to identify the best N management strategy able to minimize NO3 − leaching without compromising crop growth and yields. A first set of rotational simulations were carried out for the 2007 and 2008 experiments, with the goal of comparing measured and simulated results. A second set of rotational simulations were carried out for a long term assessment (50years) of the two treatments in comparisons. Additional long term rotational simulation runs were carried out considering two different fertilization strategies based on the maize crop uptake in 2007 (276kgNha−1) and on the PUA (“Best Nitrogen Management with manures”) which defines the fertilization practices adopted in the nitrate vulnerable zones (no more than 170kgNha−1 year−1). Measured data showed that in the manure treatment, the highest nitrate-N concentration was 627.3mgkg−1 at 0.10m depth on August 2008, while the lowest was 2.50mgkg−1 at 1.40m depth on July 2007. In the slurry treatment, the maximum nitrate N concentration was 344.6mgkg−1 at 0.10m depth on August 2008 and the minimum was 8.9mgkg−1 at 0.60cm depth on September 2007. The model was able to closely reproduce the measured results. The simulation results of the inorganic soil N were 1228kgha−1 and 1370kgha−1 for the manure and slurry treatment respectively versus the measured soil inorganic N content at the beginning of the experiment of 1267kgha−1 for the manure treatment and 1323kgha−1 for the slurry treatment. The simulated cumulative amount of nitrate leaching was 9251kgha−1 for the manure treatment and 11,101kgha−1 for the slurry treatment for the 50years simulations. The simulations results also showed that when the inorganic N was used alone, the amount of leaching and soil N were significantly reduced. [Copyright &y& Elsevier]

Abstract: The integration of site-specific management principles and conservation tillage practices is a rather unexploited field of research despite their economical and environmental benefits. The objectives of this research were: (1) to investigate the farm economic net return of three conservation tillage practices (NIT – Non-Inversion Tillage, MT – Minimum Tillage and NT – No-Tillage) performed at variable intensity within predefined management zones; the HS – area with a consistently higher yield and LS – area with a consistently low yield, of a maize (Zea mays, L.) field in NE-Italy (2) to identify the most economically sound tillage practice for each management zone using long-term simulation results; (3) to assess the environmental impact of the three tillage systems with regards to soil organic carbon changes, CO2 losses and nitrate leaching using the SALUS model. Field trials were carried out on an 8-ha flat field, situated near Rovigo, NE Italy, on maize (Z. mays, L.). The farm gross margin was higher for NT in the year of study as well as the long-term simulated scenarios that resulted in higher yields over time. The NT tillage practices resulted in higher economic return in the both the HS and LS areas. Total soil carbon was higher in NT due to the crop residues retained on the surface. Nitrate leaching was higher in for the MT and for the LS area. [Copyright &y& Elsevier]

Abstract: The identification of homogeneous management zones within a field is crucial for variable rate application of agronomic inputs. This study proposed a methodology to identify homogeneous management zones within a 8ha field, based on the stability of measured and simulated yield patterns in a maize–soybean–wheat crop rotation in north-east Italy. Crop growth and yield were simulated over a 14-year period (1989–2002) using CERES-Maize, CROPGRO-Soybean and CERES-Wheat models to account for weather effects on yield spatial patterns. The overlay of long-term assessments of yield spatial and temporal data allowed for the identification of two stable zones with different yield levels, one with greater yield (called HS for high and stable yield) and one with lower yield (called LS for low and stable yield). The size of the HS zone identified using 14 years of simulated yield was smaller than the one obtained when considering only yield monitor data taken during the 5-year crop rotation. The LS zone was larger when using simulated data, confirming that the consistency of temporal stability increased by increasing the years considered. The models were able to closely simulate yield across the field when site-specific inputs were used, showing potential for use in yield map interpretation in the context of precision agriculture. Results showed that a combination of GIS tools and crop growth simulation models can be used to identify temporally stable zones, which is a fundamental prerequisite for adopting variable rate technologies. [Copyright &y& Elsevier]

Despite the new equipment capabilities, uneven crop stands are still common occurrences in crop fields, mainly due to spatial heterogeneity in soil conditions, seedling mortality due to herbivore predation and disease, or human error. Non-uniform plant stands may reduce grain yield in crops like maize. Thus, detecting signs of variability in crop stand density early in the season provides critical information for management decisions and crop yield forecasts. Processing techniques applied on images captured by unmanned aerial vehicles (UAVs) has been used successfully to identify crop rows and estimate stand density and, most recently, to estimate plant-to-plant interval distance. Here, we further test and apply an image processing algorithm on UAV images collected from yield-stability zones in a commercial crop field. Our objective was to implement the algorithm to compare variation of plant-spacing intervals to test whether yield differences within these zones are related to differences in crop stand characteristics. Our analysis indicates that the algorithm can be reliably used to estimate plant counts (precision >95% and recall >97%) and plant distance interval (R2 ~0.9 and relative error <10%). Analysis of the collected data indicated that plant spacing variability differences were small among plots with large yield differences, suggesting that it was not a major cause of yield variability across zones with distinct yield history. This analysis provides an example of how plant-detection algorithms can be applied to improve the understanding of patterns of spatial and temporal yield variability. [ABSTRACT FROM AUTHOR]

Ecological practices such as intercropping maize (Zea mays) with cowpea (Vigna unguiculata L.) have been promoted to combat parasitic weeds like Striga (Striga asiatica). Intercropping has been promoted across Africa as a Striga control practice (SCP) and food security measure. Despite past efforts, millions of smallholder farmers (cultivating < 2 ha of maize) still struggle to implement SCPs. Social and ecological factors that prevent SCP implementation are well documented in the literature, but their underlying interactions have remained elusive. System dynamics modeling can uncover these interactions and assess their effect on intercropping rates as well as Striga emergence. This study presents a participatory mixed methods approach to build a system dynamics model based on two theories: diffusion of innovations and resource pool dynamics. The model estimates the population of fields where Striga emerged in response to intercropped fields when various interventions were implemented. According to model simulations, if new policies are not enacted to support intercropping, Striga is likely to spread to 2,625,000 maize fields, parasitizing almost 75% of smallholder farms across Central Malawi by 2036. The participatory approach allowed us to evaluate several policies, one of which sustained enough adopters to limit Striga emergence to < 500,000 fields, reducing the weed's threat to food security. This policy considers how input costs and erratic rainfall can lead to disadoption, therefore, supporting the implementation of five to six consecutive years of intercropping by providing both fertilizer subsidies and demonstration plots. In this study, our participatory approach has shown to develop a model that can highlight interactions in social ecological systems, their leverage points, and how they can be exploited to develop effective food security policies [ABSTRACT FROM AUTHOR]

• Maize growth models differ widely in their simulations of soil temperature. • Root-mean-square errors ranged from about 1.5 °C to 5.1 °C. • APSIM, ecosys, JULES, Expert-N, SLFT, and maizsim were the best six models. • Most of the best models used a numeric energy balance on each soil layer. • More recent soil thermal conductivity routines might help improve the models. Accurate simulation of soil temperature can help improve the accuracy of crop growth models by improving the predictions of soil processes like seed germination, decomposition, nitrification, evaporation, and carbon sequestration. To assess how well such models can simulate soil temperature, herein we present results of an inter-comparison study of 33 maize (Zea mays L.) growth models. Among the 33 models, four of the modeling groups contributed results using differing algorithms or "flavors" to simulate evapotranspiration within the same overall model family. The study used comprehensive datasets from two sites - Mead, Nebraska, USA and Bushland, Texas, USA wherein soil temperature was measured continually at several depths. The range of simulated soil temperatures was large (about 10–15 °C) from the coolest to warmest models across whole growing seasons from bare soil to full canopy and at both shallow and deeper depths. Within model families, there were no significant differences among their simulations of soil temperature due to their differing evapotranspiration method "flavors", so root-mean-square-errors (RMSE) were averaged within families, which reduced the number of soil temperature model families to 13. The model family RMSEs averaged over all 20 treatment-years and 2 depths ranged from about 1.5 to 5.1 °C. The six models with the lowest RMSEs were APSIM, ecosys, JULES, Expert-N, SLFT, and MaizSim. Five of these best models used a numerical iterative approach to simulate soil temperature, which entailed using an energy balance on each soil layer. whereby the change in heat storage during a time step equals the difference between the heat flow into and that out of the layer. Further improvements in the best models for simulating soil temperature might be possible with the incorporation of more recently improved routines for simulating soil thermal conductivity than the older routines now in use by the models. [Display omitted] [ABSTRACT FROM AUTHOR]

Leaching losses of dissolved organic carbon (DOC) and nitrogen (DON) from agricultural systems are important to water quality and carbon and nutrient balances but are rarely reported; the few available studies suggest linkages to litter production (DOC) and nitrogen fertilization (DON). In this study we examine the leaching of DOC, DON, NO 3 −, and NH 4 + from no-till corn (maize) and perennial bioenergy crops (switchgrass, miscanthus, native grasses, restored prairie, and poplar) grown between 2009 and 2016 in a replicated field experiment in the upper Midwest U.S. Leaching was estimated from concentrations in soil water and modeled drainage (percolation) rates. DOC leaching rates (kg ha−1 yr−1) and volume-weighted mean concentrations (mg L−1) among cropping systems averaged 15.4 and 4.6, respectively; N fertilization had no effect and poplar lost the most DOC (21.8 and 6.9, respectively). DON leaching rates (kg ha−1 yr−1) and volume-weighted mean concentrations (mg L−1) under corn (the most heavily N-fertilized crop) averaged 4.5 and 1.0, respectively, which was higher than perennial grasses (mean: 1.5 and 0.5, respectively) and poplar (1.6 and 0.5, respectively). NO 3 − comprised the majority of total N leaching in all systems (59–92%). Average NO 3 − leaching (kg N ha−1 yr−1) under corn (35.3) was higher than perennial grasses (5.9) and poplar (7.2). NH 4 + concentrations in soil water from all cropping systems were relatively low (<0.07 mg N L−1). Perennial crops leached more NO 3 − in the first few years after planting, and markedly less after. Among the fertilized crops, the leached N represented 14–38% of the added N over the study period; poplar lost the greatest proportion (38%) and corn was intermediate (23%). Requiring only one third or less of the N fertilization compared to corn, perennial bioenergy crops can substantially reduce N leaching and consequent movement into aquifers and surface waters. Unlabelled Image • Organic carbon leaching was unaffected by nitrogen fertilization, but it was greater in poplar than in the herbaceous crops. • Nitrogen was leached mainly as nitrate, with the balance lost mostly as organic N and relatively little as ammonium. • Leaching of organic nitrogen increased with N fertilization. • Perennial cropping systems that were moderately fertilized leached less nitrogen than more heavily fertilized corn. [ABSTRACT FROM AUTHOR]

• Evapotranspiration from rainfed continuous maize and maize and soybean crops in rotation was similar during normal years. • Water use efficiencies were lower in the drought year, mainly due to lower crop yield. • Maize and soybean crops in this region use similar amounts of water over the growing season.\ • The relative proportion of maize and soybean crops in watersheds should not affect regional water balances. This study examined evapotranspiration (ET) from no-till, rainfed maize and soybean during three growing seasons (May-Sep) of normal rainfall years (2009, 2010, 2011) and a drought year (2012) in Michigan, USA, based on daily soil water uptake measured by time-domain reflectometry at multiple depths through the root zone. During normal rainfall years, growing-season ET was similar between continuous maize (mean ± standard deviation: 471 ± 47 mm) and maize in rotation (469 ± 51 mm). During the drought year, ET decreased by only 3% for continuous maize but by 20% for maize in rotation. During the normal rainfall years, ET for soybean (453 ± 34 mm) was statistically indistinguishable from ET for maize, and was lower during the drought year (333 mm). Water use efficiency (WUE), calculated from harvest yield (grain + corn stover) and ET, was 25.3 ± 4.2 kg ha−1 mm-1 for continuous maize and 27.3 ± 3.1 kg ha−1 mm−1 for maize in rotation during the normal rainfall years, whereas WUEs for both continuous maize and maize in rotation were much lower in the 2012 drought year (14.0 and 15.5 kg ha−1 mm−1, respectively), coincident with lower production. Soybean had a much lower WUE than maize during the three normal years (6.95 ± 0.96 kg ha−1 mm−1) and the drought year (4.57 kg ha−1 mm−1), also explained by lower yield. Both maize and soybean tended to use all available water in the soil profile; there was no consistent difference in ET between these crops, while yield varied markedly from year to year. [ABSTRACT FROM AUTHOR]

• Eddy covariance measurements were used to inter-compare 29 maize models in their ability to simulate evapotranspiration (ET). • There was a huge range among the models in their simulations of ET for the initial blind phase, which continued even as more information was supplied. • Medians of the 29 models were generally close to observations. • Both simple and complex models were among the best in their ability to simulate ET. • Widely used models were consistently among the best, presumably because large numbers of users with a wide range of conditions led to improvements. Crop yield can be affected by crop water use and vice versa, so when trying to simulate one or the other, it can be important that both are simulated well. In a prior inter-comparison among maize growth models, evapotranspiration (ET) predictions varied widely, but no observations of actual ET were available for comparison. Therefore, this follow-up study was initiated under the umbrella of AgMIP (Agricultural Model Inter-Comparison and Improvement Project). Observations of daily ET using the eddy covariance technique from an 8-year-long (2006–2013) experiment conducted at Ames, IA were used as the standard for comparison among models. Simulation results from 29 models are reported herein. In the first "blind" phase for which only weather, soils, phenology, and management information were provided to the modelers, estimates of seasonal ET varied from about 200 to about 700 mm. Subsequent three phases provided (1) leaf area indices for all years, (2) all daily ET and agronomic data for a typical year (2011), and (3) all data for all years, thus allowing the modelers to progressively calibrate their models as more information was provided, but the range among ET estimates still varied by a factor of two or more. Much of the variability among the models was due to differing estimates of potential evapotranspiration, which suggests an avenue for substantial model improvement. Nevertheless, the ensemble median values were generally close to the observations, and the medians were best (had the lowest mean squared deviations from observations, MSD) for several ET categories for inter-comparison, but not all. Further, the medians were best when considering both ET and agronomic parameters together. The best six models with the lowest MSDs were identified for several ET and agronomic categories, and they proved to vary widely in complexity in spite of having similar prediction accuracies. At the same time, other models with apparently similar approaches were not as accurate. The models that are widely used tended to perform better, leading us speculate that a larger number of users testing these models over a wider range of conditions likely has led to improvement. User experience and skill at calibration and dealing with missing input data likely were also a factor in determining the accuracy of model predictions. In several cases different versions of a model within the same family of models were run, and these within-family inter-comparisons identified particular approaches that were better while other factors were held constant. Thus, improvement is needed in many of the models with regard to their ability to simulate ET over a wide range of conditions, and several aspects for progress have been identified, especially in their simulation of potential ET. [ABSTRACT FROM AUTHOR]

Abstract: Agricultural soils emit about 50% of the global flux of N2O attributable to human influence, mostly in response to nitrogen fertilizer use. Recent evidence that the relationship between N2O fluxes and N-fertilizer additions to cereal maize are non-linear provides an opportunity to estimate regional N2O fluxes based on estimates of N application rates rather than as a simple percentage of N inputs as used by the Intergovernmental Panel on Climate Change (IPCC). We combined a simple empirical model of N2O production with the SOCRATES soil carbon dynamics model to estimate N2O and other sources of Global Warming Potential (GWP) from cereal maize across 19,000 cropland polygons in the North Central Region (NCR) of the US over the period 1964–2005. Results indicate that the loading of greenhouse gases to the atmosphere from cereal maize production in the NCR was 1.7Gt CO2e, with an average 268t CO2e produced per tonne of grain. From 1970 until 2005, GHG emissions per unit product declined on average by 2.8t CO2eha−1 annum−1, coinciding with a stabilisation in N application rate and consistent increases in grain yield from the mid-1970’s. Nitrous oxide production from N fertilizer inputs represented 59% of these emissions, soil C decline (0–30cm) represented 11% of total emissions, with the remaining 30% (517Mt) from the combustion of fuel associated with farm operations. Of the 126Mt of N fertilizer applied to cereal maize from 1964 to 2005, we estimate that 2.2Mt N was emitted as N2O when using a non-linear response model, equivalent to 1.75% of the applied N. [Copyright &y& Elsevier]