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Accurate prediction of crop coefficient (K c) is necessary for proper irrigation management. We explored CropSyst for determining irrigation requirements of apple trees and for accuracy of K c prediction. Values of K c were compared to those obtained, over 2002–2010, from lysimeter-grown trees. Over these years, trees had different ratios of height (H) to width (W). CropSyst predicted irrigation requirements using tree light interception and water uptake sub-model components. Parameters of the model were adjusted using data obtained from the lysimeter in 2010. Tree light interception sub-model was verified by 2007 data. After parameterization, good agreement was found between simulated and measured K c over different seasons. The porosity coefficient of the canopy was related to changes in tree’s H/W ratio and leaf overlapping. Accordingly, different porosity values could be estimated for each year. When yearly changes in canopy porosity was considered, CropSyst improved K c prediction and generated relevant information for managing irrigation under changing canopy shape for apple trees.

Like many intensive vegetable production systems, the greenhouse-based system on the south-eastern (SE) Mediterranean coast of Spain is associated with considerable NO3− contamination of groundwater. Drip irrigation and sophisticated fertigation systems provide the technical capacity for precise nutrient and irrigation management of soil-grown crops which would reduce NO3− leaching loss. The VegSyst crop simulation model was developed to simulate daily crop biomass production, N uptake and crop evapotranspiration (ETc). VegSyst is driven by thermal time and consequently is adaptable to different planting dates, different greenhouse cooling practices and differences in greenhouse design. It will be subsequently incorporated into a practical on-farm decision support system to enable growers to more effectively use the advanced technical capacity of this horticultural system for optimal N and irrigation management. VegSyst was calibrated and validated for muskmelon grown in Mediterranean plastic greenhouse in SE Spain using data of four melon crops, two grown in 2005 and two in 2006 using two management strategies of water and N management in each year. VegSyst very accurately simulated crop biomass production and accurately simulated crop N uptake over time. Model performance in simulating dry matter production (DMP) over time was better using a double radiation use efficiency (RUE) approach (5.0 and 3.2 g MJ−1 PAR for vegetative and reproductive growth phases) compared to a single RUE approach (4.3 g MJ−1 PAR). The simulation of ETc over time, was very accurate in the two 2006 muskmelon crops and somewhat less so in the two 2005 crops. The error in the simulated final values, expressed as a percentage of final measured values was −1 to 6% for DMP, 2–11% for crop N uptake, and −11 to 6% for ETc. VegSyst provided effective simulation of DMP, N uptake and ETc for crops with different planting dates. This model can be readily adapted to other crops.

In many crop models, the process of radiation transmittance through the canopy is normally described as an exponential attenuation process (Beer’s Law equation), which is assumed to be valid for canopies covering the ground with a random spatial distribution of leaves. However, for discontinuous canopies, where a distinctive row pattern of plant exists, there is a more complex situation because of the presence of gaps between individual plants. This must be accounted for when characterizing radiation relationships for these kinds of systems, in particular when short time-scales are of interest. Photosynthetically active radiation (PAR) transmittance (τPAR) is more commonly studied and reported than global solar radiation (Sg) transmittance (τSg). However, both PAR and Sg are important in radiative transfer sub-models used in plant growth simulation. In this work simultaneous measurements of τSg and τPAR under discontinuous canopies were performed, and the hourly changes in radiation transmittance for PAR and global solar radiation were characterized. Two methods were assessed to transform between τSg and τPAR. The two methods yielded similar results for low values of transmittance, but disagreement occurred for higher values of transmittance. The method based on a fixed value for the ratio of extinction coefficients for PAR and Sg outperformed the method based on a linear relationship between τPAR and τSg with average relative errors (RE) of 7.97% vs. 13.29% and 2.84% vs. 7.77% for hourly and daily time-scale, respectively.

Prediction of plant water status is necessary for the judicious application of regulated deficit irrigation. CropSyst, a generic crop growth model that is applicable to fruit trees, was used to forecast plant water potential for irrigation management recommendations in a pear orchard. Plant water potential is predicted along with tree transpiration using Ohm’s law analogy. The parameters of the model were adjusted by using field measured data on a lysimeter-grown pear tree. After adjustment, and using the same lysimeter data, a satisfactory agreement was found between simulated and measured tree transpiration, light interception, and stem water potential. Model simulations were also performed for other independent field data. These corresponded to eight different conditions of a deficit-irrigated field experiment in a pear orchard. Each condition differed in soil texture, time of irrigation cut-off, crop load, and tree leaf area. Deficit irrigation was managed first by withholding irrigation until reaching a threshold in midday stem water potential of −1.5 MPa. Subsequently, irrigation was applied at fixed proportions of full irrigation requirements. Simulations with CropSyst were used as decision support system that could work independently of stem water potential measurements. Simulations in all eight sites were satisfactory at providing adequate time without irrigation during the first part of the deficit period. A highly significant relationship (r 2 = 0.71) between predicted and measured stem water potentials was found for a simulation period of 40 days. Simulations for longer periods (i.e. 74 days) decreased the r 2 to 0.61, and for this reason after resuming irrigation, slight deviations were found for the average stem water potential in two out of five sites. In conclusion, CropSyst produced relevant information for managing deficit irrigation in simulation periods shorter than 40 days.

Two methods are presented that allow converting the van Genuchten (VG) soil-water-retention (SWR) model parameters to equivalent parameters of the Campbell (C) model. The first method builds on an available solution to convert the VG model to an equivalent Brooks-Corey (BC) model, but accounts for the fact that the effective water content of the BC model and the C model are not the same, because the C model lacks the residual water content parameter, θr . The second method (II) relies on a curve-fitting exercise (minimizing the differences between the VG and C-SWR curves) to derive simultaneously the optimal capacity-matching point and SWR-matching point. Results indicate that the second method outperforms the first method and yields an improved equivalence. To bypass the time-consuming curve-fitting exercise, based on Method II two empirical regression equations are presented that allow rapid calculation of the two C-parameters, air-entry and Campbell-b, by the corresponding VG-SWR parameters.

The Yakima River Reservoir system supplies water to ~180,000 irrigated hectares through the operation of five reservoirs with cumulative storage of ~30% mean annual river flow. Runoff is derived mostly from winter precipitation in the Cascade Mountains, much of which is stored as snowpack. Climate change is expected to result in earlier snowmelt runoff and reduced summer flows. Effects of these changes on irrigated agriculture were simulated using a reservoir system model coupled to a hydrological model driven by downscaled scenarios from 20 climate models archived by the 2007 Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report. We find earlier snowmelt results in increased water delivery curtailments. Historically, the basin experienced substantial water shortages in 14% of years. Without adaptations, for IPCC A1B global emission scenarios, water shortages increase to 27% (13% to 49% range) in the 2020s, to 33% in the 2040s, and 68% in the 2080s. For IPCC B1 emissions scenarios, shortages occur in 24% (7% to 54%) of years in the 2020s, 31% in the 2040s and 43% in the 2080s. Historically unprecedented conditions where senior water rights holders suffer shortfalls occur with increasing frequency in both A1B and B1 scenarios. Economic losses include expected annual production declines of 5%-16%, with greater probabilities of operating losses for junior water rights holders.

3USDA-ARS, Crops Systems and Global Change Laboratory, Beltsville, Maryland Simulation models are valuable tools to evaluate soil processes, crop growth, and production responses under varied agroclimatic and input-management production conditions. In this study, an upgraded potato (Solanum tuberosum L.) crop growth simulation model (CSPotato) was integrated with a multi-year, multi-crop simulation model (CropSystVB). The integrated CropSystVB-CSPotato model facilitated prediction of soil processes, and growth and yields of different crops in a potato rotation system under center pivot irrigation. The integrated model was validated using two years (2001-2002) of field data on ‘Ranger Russet’ cultivar grown in a Quincy fine sand in the Pacific Northwest Columbia Basin production region under different nitrogen-management practices. This study showed good agreement between the measured and predicted yields as well as N uptake across different N management practices in both years. The predicted water as well as nitrogen drainage below the potato rooting depth (0.6 m) was greater in 2002 than that in 2001. This study demonstrated that the upgraded potato model integrated with CropSystVB can be used as a valuable decision tool to predict the crop yields, fate, and transport of N in irrigated potato rotation cropping systems.

Climate change is expected to bring potentially significant changes to Washington State’s natural, institutional, cultural, and economic landscape. Addressing climate change impacts will require a sustained commitment to integrating climate information into the day-to-day governance and management of infrastructure, programs, and services that may be affected by climate change. This paper discusses fundamental concepts for planning for climate change and identifies options for adapting to the climate impacts evaluated in the Washington Climate Change Impacts Assessment. Additionally, the paper highlights potential avenues for increasing flexibility in the policies and regulations used to govern human and natural systems in Washington.

Soil carbon cycling is an essential component of agroecosystems models. Simulating soil carbon ( C s ) cycling has become an issue of societal importance for C s storage can play a role reducing the rate of increase of atmospheric CO 2 concentration. To participate in carbon trading markets, growers have to evaluate their local, site-specific options to increase C s or reduce C s losses. This paper introduces C-Farm, a daily time step cropping systems model that allows calculating the Cs balance using a one-pool soil organic matter sub-module. In C-Farm the C s turnover rate depends non-linearly on C s and on environmental and management controls. Two long-term experiments were selected to evaluate C-Farm: a wheat-summer fallow 70+ years experiment at Pendleton, Oregon, and the continuous wheat experiment at the Rothamsted experiment station in the United Kingdom. C-Farm simulated well the long-term C s evolution observed in these experiments. In addition, simulations performed in the dryland US Pacific Northwest show its applicability for assessing C s storage rates in a region with large variation in precipitation. C-Farm can be easily customized to a large array of local conditions, providing robust estimates of short- and long-term on-farm carbon storage rates. The model is being further developed to provide estimates of nitrous oxide emission.

Nitrogen removal in wetlands is achieved through two pathways: (a) N cycling and (b) storage. N cycling is a permanent removal pathway. There has been an increasing interest in the development of technologies to alleviate permanent nitrogen removal limitation in constructed wetlands by ensuring prevalence of conditions enhancing N cycling. The purpose of this study is to review an emerging technology of vegetated submerged bed constructed wetland system aimed at improving nitrogen removal in wetlands through rational system design. The design and performance of this system type is evaluated. The oxygen transfer capacity and nitrogen removal mechanisms on system performance are evaluated. Constructed wetland combinations most commonly consist of vertical flow (VF) and horizontal flow (HF) beds where VF and HF are aimed at nitrification and denitrification, respectively. Nitrate nitrogen accumulation is the most limiting factor in typical VF based systems.

A 2-year rotation of winter wheat (Triticum aestivum L.)-summer fallow is a dominant cropping system in the dryland region of the Pacific Northwest United States. Traditional, tillage-based summer fallow relies on a soil mulch to disrupt capillary continuity to conserve seed-zone water for early establishment of winter wheat. However, tillage to create the soil mulch and to subsequently fertilize and control weeds often results in unacceptable levels of wind erosion due to the burial of crop residues and the exposure of fine soil particles. Chemical (no-till) fallow (CF) and reduced-tillage fallow (RT) are two alternatives for reducing wind erosion. Our objectives were: (i) to assess the effects of CF and RT on seed- and root-zone temperature and water regimes; and (ii) to test the Simultaneous Heat and Water (SHAW) model for simulating management effects on soil temperature and water. Weather data, soil temperature, and water content were monitored in paired CF and RT treatments during April 2003-March 2004. The RT treatment was observed to retain more seed-zone water over summer compared to CF, consistent with relevant literature for Mediterranean environments and of critical importance to farmers. During the wet winter, CF gained more water than RT because of later planting of winter wheat, and thus less water use. Observed soil temperatures were higher in the CF due to its lower dry soil albedo, higher bulk density and thermal diffusivity than in the RT. SHAW-simulated water contents followed the general trend of the field data, though it slightly under-predicted soil water content for CF and over-predicted for RT. SHAW under-predicted soil temperature during the dry summer and over-predicted for the wet (November-December) period yet the overall trend was properly described with differences between simulations and observations decreasing with soil depth. Overall, SHAW proved adequate in simulating seed-zone and whole-profile soil water and temperature, and therefore may serve as a useful modeling tool for tillage and residue management.

Accurate prediction of evaporative water loss from dryland agricultural soils requires knowledge of diffusive resistance. Our objective was to experimentally determine the effective vapor diffusion coefficients and the diffusive resistances for water vapor through wheat (Triticum aestivum L.) residue layers. A laboratory diffusion chamber was designed to investigate the effects of wheat residue type (straw vs. chaff), residue amount (2, 4, 8, and 12 Mg ha -1 ), and residue orientation (horizontal vs. vertical straw) on vapor diffusion. The diffusion chamber consisted of two well-stirred chambers separated by a residue layer. In one chamber, a constant saturated water vapor pressure was maintained, while in the other chamber, vapor pressure increased with time due to the diffusion of vapor through the residue layer. Vapor pressure was measured with a humidity sensor. The diffusion equation was used to obtain the effective diffusion coefficient from the measured data by inverse modeling. Results showed that for both straw and chaff residue, increasing the quantity of residue did not necessarily decrease the effective diffusion coefficient but did increase the diffusive resistance. For the same quantity of residue, chaff had a lower effective diffusion coefficient than straw, but the diffusive resistances were similar. The diffusive resistances were in the order of 800 to 3700 s m -1 for 2 to 12 Mg ha -1 residue treatments. The factor affecting the diffusive resistance the most was the thickness of the residue layer: the thicker the residue layer, the larger the diffusive resistance.

We compare 27 wheat models' yield responses to interannual climate variability, analyzed at locations in Argentina, Australia, India, and The Netherlands as part of the Agricultural Model Intercomparison and Improvement Project (AgMIP) Wheat Pilot. Each model simulated 1981-2010 grain yield, and we evaluate results against the interannual variability of growing season temperature, precipitation, and solar radiation. The amount of information used for calibration has only a minor effect on most models' climate response, and even small multi-model ensembles prove beneficial. Wheat model clusters reveal common characteristics of yield response to climate; however models rarely share the same cluster at all four sites indicating substantial independence. Only a weak relationship (R2???0.24) was found between the models' sensitivities to interannual temperature variability and their response to long-term warming, suggesting that additional processes differentiate climate change impacts from observed climate variability analogs and motivating continuing analysis and model development efforts. Compares interannual climate response of 27 wheat models at four locations.Calculates the diminishing return of constructing multi-model ensembles for assessment.Identifies similarities and major differences of model responses.Differentiates between interannual temperature sensitivity and climate change response.

The most important question in supplemental irrigation management is when and how much water to apply. It is a laborious and expensive task to develop supplemental irrigation schedules solely by conventional field experimentation. A cropping system simulation model (CropSyst) was evaluated for its ability to simulate growth, biomass, grain yield and evapotranspiration (ET) of wheat sown early with supplemental irrigation (SI). This was computed under rainfed conditions in a semiarid climate with cold winters in the highlands of Turkey. Experimental data from three growing seasons (1998–2001) were used. The experimental design incorporates Bezostia bread wheat cultivar tested under two main treatments: no irrigation at sowing (rainfed-A1) and irrigation at sowing with 50 mm of water (A2); and two sub treatments: rainfed (B1) and spring supplemental irrigation to replenish the total water requirement at 0–90 cm soil profile (B2) at the Ankara Research Institute of Rural Services. Crop input parameters were selected from the model documentation and experimental data. A few cultivar-specific parameters were adjusted within a narrow range of typical fluctuations by model calibration. Results showed that CropSyst was able to simulate yield, biomass and evapotranspiration as observed in the field experiments. Overall, the Willmott Index of agreement between simulated and observed values of grain yield, biomass and ET were 0.98, 0.76 and 0.91, respectively. CropSyst model predicted better the seasonal evapotranspiration under full supplemental irrigated conditions (A2B2) than under rainfed conditions (A1B1), with values of the Willmott index of agreement being 0.97 and 0.89, respectively. The model was run for 20 years (1982–2001) including the 4-year experimental period. Data showed that wheat grain yield could be improved by 15, 19 and 25% with applying only 50 mm of water at the sowing time of 15 October, 1 October and 15 September, respectively. In 80% of the cases, the respective SI applications would give 2.75, 2.7 and 2.95 t ha−1, of the long-term average rainfed yield of 2.1 t ha−1, respectively.

Several methods have been proposed to simulate yield in crop simulation models. In this work, we present a simple method to estimate harvest index (HI) of grain crops based on fractional post-anthesis phase growth (f G = fraction of biomass accumulation that occurred in the post-anthesis phase). We propose that HI increases in a linear or curvilinear fashion in response to f G. The linear model has two parameters, the intercept (HIo) and the slope (s). The curvilinear model was assumed to be monotonic: HI = HIx � (HIxHIo) � exp(� kf G); where HIx is the asymptote, HIo is the intercept andk is a constant modulating the rate of HI increase. The models were tested for barley (Pullman, WA and Uruguay), wheat (Pullman, WA) and sorghum (Australia). A positive relationship between HI and f G was in general evident. For barley, the linear model appropriately represented the response of HI to fG, with both HIo and s in the vicinity of 0.3. For wheat HIo and s were 0.34 and 0.21, respectively, but the curvilinear model yielded a slightly better fitting than the linear model. For sorghum, both linear and linear-plateau models fitted data reasonably well. It is shown that the models work well in crops source-limited or source-sink co-limited during grain filling, but in sink-limited conditions the magnitude of the limitation needs to be characterized to compute HI. A major advantage of this method is that the parameters of the linear or curvilinear model are readily calibrated from yield data and biomass measurements at anthesis and harvest. # 2007 Elsevier B.V. All rights reserved.

Metadata only record In the Sahel region of West Africa, the traditional organization of the population and the grazing land avoided overexploitation of pastures. Since independence in the 1960s, grazing lands have been opened to all without specific guidance, and the vulnerability of the pastures to degradation has increased. Rotational grazing is postulated as a possible solution to provide higher pasture productivity, higher animal loads per unit land, and perhaps improved soil carbon storage. The objective of this study was to conduct a simulation-based assessment of the impact of rotational grazing management on pasture biomass production, grazing efficiency, animal grazing requirement satisfaction, and soil carbon storage in the Madiama Commune, Mali. The results showed that grazing intensity is the primary factor influencing the productivity of annual pastures and their capacity to provide for animal grazing requirements. Rotating the animals in paddocks is a positive practice for pasture protection that showed advantage as the grazing pressure increased. Increasing the size of the reserve biomass not available for grazing, which triggers the decision of taking the animals off the field, provided better pasture protection but reduced animal grazing requirements satisfaction. In terms of soil carbon storage, all management scenarios led to reduction of soil carbon at the end of the 50-year simulation periods, ranging between 4% and 5% of the initial storage. The differences in reduction as a function of grazing intensity were of no practical significance in these soils with very low organic matter content, mostly resistant to decomposition. SysCoor-2 (Farm/Enterprise)

This paper argues that an integrated assessment (IA) approach, combining simulation modelling with deliberative processes involving decision makers and other stakeholders, has the potential to generate credible and relevant assessments of climate change impacts on farming systems. The justification for the approach proposed is that while simulation modelling provides an effective way of exploring the range of possible impacts of climate change and a means of testing the consequences of possible management or policy interventions, the interpretation of the outputs is highly dependent on the point of view of the stakeholder. Inevitably, whatever the responses to climate change, there will be trade-offs between the benefits and costs to a range of stakeholders. The use of a deliberative process that includes stakeholders, both in defining the topics addressed and in debating the interpretations of the outcomes, addresses many of the limitations that have been previously identified in the use of computer-based tools for agricultural decision support. The paper further argues that the concepts of resilience and adaptive capacity are useful for the assessment of climate change impacts as they provide an underpinning theory for processes of change in land use systems. The integrated modelling framework (IMF) developed for the simulation of whole-farm systems is detailed, including components for crop and soil processes, livestock systems and a tool for scheduling of resource use within management plans. The use of the IMF for assessing climate change impacts is then outlined to demonstrate the range of analyses possible. The paper concludes with a critique of the IA approach and notes that issues of quantification and communication of uncertainty are central to the success of the methodology.

The formulation and evaluation of a model of radiation interception by fruit-tree orchards is presented. The model computes radiation interception based on the proportion of the orchard floor that is shaded by the trees at any given time, calculated from geometric relationships of the length of the shadow cast by the trees and the orchard configuration. Inputs include tree dimensions, orchard configuration, daily global solar radiation and a correction factor that accounts for within canopy gaps. Evaluation of the internal consistency of the model and a sensitivity analysis were carried out. Then, a test of the performance of the model was done, comparing simulated values of fractional PAR interception with field measurements from a range of orchard conditions (species, orchard design, tree dimensions, and location), including orchards on steep terrain. Despite simplifications made to reduce input data requirements as well as the inherent variability among the orchards, the model simulated reasonably well the fractional interception of radiation on an hourly time step (mean absolute error, MAE = 10.08%; Willmott index of agreement, D = 0.94). When evaluated on a daily basis, the performance of the model improved considerably (MAE = 4.47%; D = 0.98). In both time scales, the model performed similarly for the different orchard conditions considered. The model can be used to estimate both PAR and global solar radiation interception, including different types of orchard configuration such as hedgerow, overhead trained, or isolated trees planted in rectangular patterns.

Simulating grain (N g ) and straw (N s ) nitrogen (N) concentration is of paramount importance in cropping systems simulation models. In this paper we present a simple model to partition N between grain and straw at harvest for barley (Hordeum vulgare L.), wheat (Triticum aestivum L.), maize (Zea mays L.), and sorghum (Sorghum bicolor Moench). The principle of the model is to partition the aboveground N at physiologic maturity based on the relative availability of biomass and N to the grain. The inputs for the model are the harvest index (HI), representing the relative availability of biomass to the grain, and the aboveground N concentration (N t ) at harvest, representing the availability of N. The model has five parameters, of which four (the maximum and minimum achievable grain and straw N concentrations) are readily available; the parameter C requires calibration. The model was calibrated and tested for these four species without differentiating genotypes within species. The testing included diverse experiments in wheat; comparing observed and estimated N g the relative RMSE ranged from 3 to 10% (five experiments) and was 31% in one experiment in which the estimated N g exceeded consistently the observed values. For barley, maize, and sorghum, the data availability for testing was limited, but the model performed well (relative RMSE values of 7, 7, and 18%, respectively). Therefore, the model proposed seems to be robust. It remains to be determined if the parameters and the method are useful to discriminate genotypic differences in N g within a species and if the method can be applied to legume crops.

Transpiration-use efficiency, the ratio of biomass (Y) produced per unit of water transpired (T) by a crop, depends on crop characteristics and on the environment in which crops develop. Transpiration-use efficiency has been described as Y/T = kc/Da, where kc is a crop dependent constant and Da is the daytime air vapor pressure deficit. Our objectives were to determine Y/T and kc of barley grown in Pullman, WA, and to analyze the variation in Y/T and kc of barley (Hordeum vulgare L.) and wheat (Triticum aestivum L.) reported in the literature. Transpiration and biomass accumulation of barley crops were measured in the years 2000 and 2001. The coefficient kc was estimated as the slope of the regression between cumulative values of biomass and T/Da .I t ranged from 6.6 � 0.4 to 6.9 � 0.2 Pa. These figures are greater than 5.8 Pa obtained by applying equations developed by Tanner and Sinclair [Tanner, C.B., Sinclair, T.R., 1983. Efficient water use in crop production: research or re-search. In: Taylor, H.M., et al. (Eds.), Limitations to Efficient Water Use in Crop Production. ASA, Madison, WI, pp. 1‐27]. Data on kc reported in the literature, although scarce, ranged from 3.0 to 5.9 Pa for barley, and from 2.8 to 6.7 Pa for wheat, with the lower values occurring at low Da (

Radiation-use efficiency (e) is a crop-dependent coefficient widely used in crop simulation models and in the physiological interpretation of crop response to the environment and management practices. Our objectives were to: (i) determine the e of spring barley (Hordeum vulgare L.), (ii) analyze the impact of weather variables and fraction of solar irradiance intercepted (f i ) on e, and (iii) compare reported estimates of e for barley and wheat (Triticum aestivum L.) with our measurements for barley. Field experiments were conducted in 2000 and 2001 at Pullman, WA. Treatments consisted of factorial combinations of two cultivars of spring barley (Baronesse and Steptoe), two seeding densities (250 and 100 plants m -2 ), and two seeding dates (normal and late). Intercepted radiation was measured with tube solarimeters installed below the canopy during the crops life cycle and aboveground biomass obtained from weekly to biweekly samples. The extinction coefficient for solar radiation of both cultivars was 0.43, with no effect of seeding date and density. Cultivar, plant density, and f did not affect e; however, e at the normal seeding date was greater than at the late seeding date in both years (1.15 and 1.19 g MJ -1 , first seeding date, and 0.90 and 0.95 g MJ -1 , second seeding date, Years 2000 and 2001, respectively, P < 0.01). These variations were correlated with daytime vapor pressure deficit (D). The e of barley and wheat reported in the literature and those obtained in this study were linearly related to D (kPa): e = 1.88 - 0.53D (r 2 = 0.70, n = 22). Maximum values of e reported for barley and wheat are near 1.6 g MJ -1 , but our analysis suggests that these high values can only be achieved in low D environments. The effect of the evaporative demand of the atmosphere should be considered in the interpretation of measured e or in the use of e in crop simulation models.

Utilizing the weather generator ClimGen, daily solar radiation (Rs) and vapor pressure deficit (VPD) were estimated from temperature data and used to calculate evapotranspiration at five locations, representing tropical, temperate, semi-arid, and arid climates. ClimGen was calibrated for each location using the most recent 2 or 5 years of complete daily weather records. Actual and estimated values were compared on a daily and weekly (7-day running average) basis. Error indices were defined to indicate excellent to poor performance of the estimation methods. Overall in all locations, the ClimGen estimates for both daily Rs and VPD were poor to acceptable. The weekly analyses showed significant improvement in performance for both Rs and VPD estimations in arid and semi-arid locations. Daily reference crop evapotranspiration values using the FAO Penman-Monteith equation (PM ETo) were calculated using complete daily weather records. These values were compared with (1) ETo calculated with the PM model, actual temperature data, and ClimGen estimates of daily Rs, VPD, and generated wind speed (PMEst ETo), and (2) ETo calculated solely from actual daily temperature data using a calibrated version of the Hargreaves method (HGAdj ETo). The daily PMEst ETo results were poor to acceptable in all locations, but analyses for weekly periods showed improved performance to acceptable and good levels for arid and semi-arid locations. The performance of the HGAdj ETo method was also poor to acceptable for daily ET estimates in all locations, while weekly analyses showed improvement. A non-calibrated version of the Hargreaves method did not work for either daily or weekly periods. The PMEst ETo and HGadj ETo methods appeared suitable for weekly periods in arid and semi-arid locations provided that at least 2 years of complete weather records were available to calibrate the parameters required. There was no advantage in using 5 years of weather records for calibration.

CropSyst is a multi-year, multi-crop, daily time step cropping systems simulation model developed to serve as an analytical tool to study the effect of climate, soils, and management on cropping systems productivity and the environment. CropSyst simulates the soil water and nitrogen budgets, crop growth and development, crop yield, residue production and decomposition, soil erosion by water, and salinity. The development of CropSyst started in the early 1990s, evolving to a suite of programs including a cropping systems simulator (CropSyst), a weather generator (ClimGen), GIS-CropSyst cooperator program (ArcCS), a watershed model (CropSyst Watershed), and several miscellaneous utility programs. CropSyst and associated programs can be downloaded free of charge over the Internet. One key feature of CropSyst is the implementation of a generic crop simulator that enables the simulation of both yearly and multi-year crops and crop rotations via a single set of parameters. Simulations can last a fraction of a year to hundreds of years. The model has been evaluated in many world locations by comparing model estimates to data collected in field experiments. CropSyst has been applied to perform risk and economic analyses of scenarios involving different cropping systems, management options, and soil and climatic conditions. An extensive list of references related to model development, evaluation, and application is provided.

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.

Ensembles of process-based crop models are increasingly used to simulate crop growth for scenarios of temperature and/or precipitation changes corresponding to different projections of atmospheric CO2 concentrations. This approach generates large datasets with thousands of simulated crop yield data. Such datasets potentially provide new information but it is difficult to summarize them in a useful way due to their structural complexities. An associated issue is that it is not straightforward to compare crops and to interpolate the results to alternative climate scenarios not initially included in the simulation protocols. Here we demonstrate that statistical models based on random-coefficient regressions are able to emulate ensembles of process-based crop models. An important advantage of the proposed statistical models is that they can interpolate between temperature levels and between CO2 concentration levels, and can thus be used to calculate temperature and [CO2] thresholds leading to yield loss or yield gain, without rerunning the original complex crop models. Our approach is illustrated with three yield datasets simulated by 19 maize models, 26 wheat models, and 13 rice models. Several statistical models are fitted to these datasets, and are then used to analyze the variability of the yield response to [CO2] and temperature. Based on our results, we show that, for wheat, a [CO2] increase is likely to outweigh the negative effect of a temperature increase of +2 degrees C in the considered sites. Compared to wheat, required levels of [CO2] increase are much higher for maize, and intermediate for rice. For all crops, uncertainties in simulating climate change impacts increase more with temperature than with elevated [CO2]. (C) 2015 Elsevier B.V. All rights reserved.

The irrigated area served by the Canal d'Urgell, a semiarid region in the Ebro Valley (northeast Spain), presents problems of ground water pollution by nitrates. Corn (Zea mays L.) is widely grown in this area, and N fertilization in corn production is the major source of N input in the area, with application rates in excess of crop requirements. In this study, we monitored several commercial cornfields for soil NO 3 -N levels, crop N uptake, and crop productivity over a 2-yr period to quantify the relationship among soil NO 3 -N, N fertilizer rates, crop N use, and N loss through leaching. Monitoring soil NO 3 -N profiles showed that in some fields, soil NO 3 -N was transported to deeper layers in the soil during the growing season. In many cases, important accumulation of NO 3 -N was observed at the bottom of the soil at physiological maturity, increasing the risk of winter leaching. Soil N availability, calculated as preplanting soil nitrate test + N fertilizer, was neither related to plant N uptake nor final biomass and grain yield. In some plots, the occurrence of drought during the growing season was more decisive than soil-available N in explaining crop N uptake and grain yield differences. Available N levels found in the soil were above 370 kg ha -1 in all cases. Apparently, these levels were enough to satisfy the crop N requirements to achieve yields above 11 to 12 Mg ha -1 . Overall data showed that there is an excess of N in the system.

Mathematical modelling of overland flow is a critical task in simulating transport of water, sediment and other pollutants from land surfaces to receiving waters. In this paper, an overland flow routing method is developed based on the Saint-Venant equations using a discretized hillslope system for areas with high roughness and steep slope. Under these conditions, the momentum equation reduces to a unique relationship between the flow depth and discharge. A hillslope is treated as a system divided into several subplanes. A set of first-order non-linear differential equations for subsequent subplanes are solved analytically using Chezy's formula in lieu of the momentum equation. Comparison of the analytical solution of the first-order non-linear ordinary differential equations and a numerical solution using the Runge-Kutta method shows a relative error of 0·3%. Using runoff data reported in the literature, comparison between the new approach and a numerical solution of the full Saint-Venant equations showed a close agreement. Copyright © 2002 John Wiley & Sons, Ltd.

In Washington State, nitrate is the most prevalent and frequently documented groundwater contaminant. The process appears driven by irrigation development associated with excessive use of fertilizers. A significant concentration of the problem is located in the Quincy–Pasco area. A computer simulation study was conducted with the objective of estimating the amount and dynamics of nitrate leaching from a typical irrigated crop rotation in this area. A 30-year record of daily weather from Richland, WA (near Pasco), a representative sandy soil (Quincy series, mesic Xeric Torripsamments), and a center pivot-irrigated crop rotation including potato (Solanum tuberosum L.), winter wheat (Triticum aestivum L.) and maize (Zea mays L.) were considered. CropSyst, a cropping systems model, was used to simulate the interactions between soils, crops, weather, and irrigation and fertilization management scenarios. When scenarios with fertilization rates above current recommended rates were simulated, potato had the largest nitrate leaching amounts during the growing season. When fertilization approached recommended rates, the simulated nitrate leaching during the potato growing seasons was low and not different from that of maize growing seasons. Excess irrigation did not affect significantly the bulk of simulated nitrate leaching, although it enhanced leaching during crop periods. Nitrate leaching was found to be more significant during no-crop periods (fall and winter), particularly following potato. Precipitation variability resulted in cycles of nitrate accumulation in the soil followed by leaching, at times producing some large events. Overall, the simulation study showed that reducing fertilization rates was the only effective approach to reducing nitrate leaching.

Nature Plants 3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.

Regional climate change impact (CCI) studies have widely involved downscaling and bias-correcting (BC) Global Climate Model (GCM)-projected climate for driving land surface models. However, BC may cause uncertainties in projecting hydrologic and biogeochemical responses to future climate due to the impaired spatiotemporal covariance of climate variables and a breakdown of physical conservation principles. Here we quantify the impact of BC on simulated climate-driven changes in water variables (evapotranspiration, ET; runoff; snow water equivalent, SWE; and water demand for irrigation), crop yield, biogenic volatile organic compounds (BVOC), nitric oxide (NO) emissions, and dissolved inorganic nitrogen (DIN) export over the Pacific Northwest (PNW) Region. We also quantify the impacts on net primary production (NPP) over a small watershed in the region (HJ Andrews). Simulation results from the coupled ECHAM5/MPI-OM model with A1B emission scenario were firstly dynamically downscaled to 12 km resolutions with WRF model. Then a quantile mapping based statistical downscaling model was used to downscale them into 1/16th degree resolution daily climate data over historical and future periods. Two series climate data were generated according to the option of bias-correction (i.e. with bias-correction (BC) and without bias-correction, NBC). Impact models were then applied to estimate hydrologic and biogeochemical responses to both BC and NBC meteorological datasets. These impact models include a macro-scale hydrologic model (VIC), a coupled cropping system model (VIC-CropSyst), an ecohydrologic model (RHESSys), a biogenic emissions model (MEGAN), and a nutrient export model (Global-NEWS). Results demonstrate that the BC and NBC climate data provide consistent estimates of the climate-driven changes in water fluxes (ET, runoff, and water demand), VOCs (isoprene and monoterpenes) and NO emissions, mean crop yield, and river DIN export over the PNW domain. However, significant differences rise from projected SWE, crop yield from dry lands, and HJ Andrews's ET between BC and NBC data. Even though BC post-processing has no significant impacts on most of the studied variables when taking PNW as a whole, their effects have large spatial variations and some local areas are substantially influenced. In addition, there are months during which BC and NBC post-processing produces significant differences in projected changes, such as summer runoff. Factor-controlled simulations indicate that BC post-processing of precipitation and temperature both substantially contribute to these differences at region scales. We conclude that there are trade-offs between using BC climate data for offline CCI studies vs. direct modeled climate data. These trade-offs should be considered when designing integrated modeling frameworks for specific applications; e.g., BC may be more important when considering impacts on reservoir operations in mountainous watersheds than when investigating impacts on biogenic emissions and air quality (where VOCs are a primary indicator).

A modeling approach was utilized to investigate the impact of increased atmospheric CO2 and temperature on water balance, crop production, plant growth, and soil erosion. For the given scenario and the site tested, the increase of temperature resulted in a significant increase of ET, reduction of soybean canopy cover and yield, a slight increase in soil loss, and a reduction in soil moisture. However, the increase of atmospheric CO2 resulted in significant increase of crop yield and canopy cover, a slight reduction of ET, and a slight reduction of daily root zone soil moisture, storm runoff, and water induced soil erosion of the corn field.

The development of cropping systems simulation capabilities world-wide combined with easy access to powerful computing has resulted in a plethora of agricultural models and consequently, model applications. Nonetheless, the scientific credibility of such applications and their relevance to farming practice is still being questioned. Our objective in this paper is to highlight some of the model applications from which benefits for farmers were or could be obtained via changed agricultural practice or policy. Changed on-farm practice due to the direct contribution of modelling, while keenly sought after, may in some cases be less achievable than a contribution via agricultural policies. This paper is intended to give some guidance for future model applications. It is not a comprehensive review of model applications, nor is it intended to discuss modelling in the context of social science or extension policy. Rather, we take snapshots around the globe to 'take stock' and to demonstrate that well-defined financial and environmental benefits can be obtained on-farm from the use of models. We highlight the importance of 'relevance' and hence the importance of true partnerships between all stakeholders (farmer, scientists, advisers) for the successful development and adoption of simulation approaches. Specifically, we address some key points that are essential for successful model applications such as: (1) issues to be addressed must be neither trivial nor obvious; (2) a modelling approach must reduce complexity rather than proliferate choices in order to aid the decision-making process (3) the cropping systems must be sufficiently flexible to allow management interventions based on insights gained from models. The pro and cons of normative approaches (e.g. decision support software that can reach a wide audience quickly but are often poorly contextualized for any individual client) versus model applications within the context of an individual client's situation will also be discussed. We suggest that a tandem approach is necessary whereby the latter is used in the early stages of model application for confidence building amongst client groups. This paper focuses on five specific regions that differ fundamentally in terms of environment and socio-economic structure and hence in their requirements for successful model applications. Specifically, we will give examples from Australia and South America (high climatic variability, large areas, low input, technologically advanced); Africa (high climatic variability, small areas, low input, subsistence agriculture); India (high climatic variability, small areas, medium level inputs, technologically progressing; and Europe (relatively low climatic variability, small areas, high input, technologically advanced). The contrast between Australia and Europe will further demonstrate how successful model applications are strongly influenced by the policy framework within which producers operate. We suggest that this might eventually lead to better adoption of fully integrated systems approaches and result in the development of resilient farming systems that are in tune with current climatic conditions and are adaptable to biophysical and socioeconomic variability and change. (C) 2001 Elsevier Science Ltd. All rights reserved.

The scenario assumed for this study was that of a region with a complete or first-order weather station surrounded by a network of second-order stations, where only monthly air temperature data were available. The objective was to evaluate procedures to estimate the monthly α parameter of the Priestley–Taylor equation in the second-order stations by adjusting and extrapolating α values determined at the first-order station. These procedures were applied in two climatic zones of north-east Spain with semi-arid continental and semi-arid Mediterranean climates, respectively. Procedure A assumed α to be constant over each zone for each month (direct extrapolation). Procedure B accounted for differences in vapour pressure deficit and available energy for evapotranspiration between the first- and second-order stations. Procedure C was based on equating the Penman–Monteith (P–M) and Priestley–Taylor (P–T) equations on a monthly basis to solve for α. Methods to estimate monthly mean vapour pressure deficit, net radiation and wind speed were developed and evaluated. A total of 11 automated first-order weather stations with a minimum period of record of 6 years (ranging from 6 to 10 years) were used for this study. Six of these stations were located in the continental zone and five in the Mediterranean zone. One station in each zone was assumed to be first-order whereas the remainder were taken as second-order stations. Monthly α parameters were calibrated using P–M reference crop evapotranspiration (ET0) values, calculated hourly and integrated for monthly periods, which were taken as ‘true’ values of ET0. For the extrapolation of monthly α parameters, procedure A was found to perform slightly better than procedure B in the Mediterranean zone. The opposite was true in the continental zone. Procedure C had the worst performance owing to the non-linearity of the P–M equation and errors in the estimation of monthly available energy, vapour pressure deficit and wind speed. Procedures A and B are simpler and performed better. Overall, monthly P–T ET0 estimates using extrapolated α parameters and Rn−G values were in a reasonable agreement with P–M ET0 calculated on an hourly basis and integrated for monthly periods. The methods presented for the spatial extrapolation of monthly available energy, vapour pressure deficit and wind speed from first- to second-order stations could be useful for other applications. Copyright © 2001 John Wiley & Sons, Ltd.

The potential effects of future climate change were investigated, corresponding to a doubling of atmospheric CO2 from 350 to 700 ppm, on agricultural production of four different cropping systems at two Italian locations, Modena and Foggia. Climate change scenarios, derived from two general circulation models (GCMs), were used as weather input to a soil-plant growth simulator, CropSyst. This model was recently modified to include the effects of elevated CO2 on crop photosynthesis and transpiration. Six different crops in total were simulated at the two Italian sites. At Modena, a 3-year maize‐maize‐wheat rotation and a 2-year soybean‐barley‐summer sorghum rotation were studied. At Foggia, a 2-year sunflower‐wheat‐fallow rotation, and a 2-year wheat‐fallow‐spring sorghum rotation were simulated. Results suggested that the combined effects of elevated atmospheric CO2 and climate change at both sites would depress crop yields if current management practices were not modified. Specifically, predicted warmer air temperatures accelerated plant phenology, reducing dry matter accumulation and crop yields by 10‐40%. By investigating adaptation strategies, it was found that a combination of early planting for spring‐summer crops and the use of slower-maturing winter cereal cultivars succeeds in maintaining crop yields at current levels at both sites. For irrigated maize and soybean production at Modena, 60‐90% more irrigation water was required under climate change to keep grain yields at current levels. This implies that adaptation to climate change may be limited for irrigated crops, depending on site-specific water availability. © 2000 Elsevier Science B.V. All rights reserved.

CropSyst, a management-oriented crop growth model, was modified to assess crop response to salinity. The effect of salinity was included in the existing water uptake module by adding an osmotic component to the soil water potential and developing a function to account for salinity effects on root permeability. The effect of salinity on water uptake is the link to simulate crop growth reduction. A qualitative analysis showed that the model simulated expected trends of crop response to salinity as affected by cultivar tolerance, atmospheric vapor pressure deficit, and soil water availability. Comparisons with data from sprinkler line experiments were performed for barley grown at Zaragoza (Spain) in 1986 and 1989, and corn at Davis, Calif. and Fort Collins, Colo. in 1975. These experiments included different salinity and irrigation levels. At Davis, the model simulated well the effect of salinity/irrigation treatments on water use, biomass, and crop yield, with values for the Willmot index of agreement (d) generally better than 0.94 (a value of 1.0 implying perfect agreement). At Fort Collins, simulation of grain yield was less satisfactory (d fluctuated between 0.83 and 0.90), but the agreement was good for crop water use and biomass (d generally better than 0.96). The lower performance for grain yield was attributed to large and erratic variations in the observed harvest index. The agreement between simulated and observed values tended to be lower at Zaragoza, with d values fluctuating between 0.84 and 0.91 for biomass and yield in the 2 years included in this evaluation. Unusually high measured yields in 1989 and erratic variation in 1986 were attributed to small sample size. The small size (increased measurement error) of samples typically obtained in sprinkler line source experiments tends to limit their use for evaluation of simulation models.

Daily crop water uptake was simulated using algorithms from three crop simulation models, CropSyst, CERES, and EPIC (listed in order of decreasing process detail). Simulated results were compared with measurements of sap flow and soil water content for maize (Zea mays L.) growing at Prosser, WA, under a wet and a dry irrigation treatment, and with soil water content measurements for nonirrigated maize at Davis, CA. At Prosser, the dry treatment imposed only a mild stress; at Davis, the stress was severe. Simulation variables such as maximum crop evapotranspiration, root density by soil layer, and green leaf area index were provided as daily input. At Prosser, all algorithms performed similarly when simulating crop water uptake. For the wet treatment, the root mean square error (RMSE) was 0.27 to 0.28 mm d -1 , and the relative error IRE = 100 (RMSE/Measured average)] was 7.0 to 7.2%. For the dry treatment, simulation accuracy decreased (RMSE = 0.33-0.38 mm d -1 ; RE = 9.0-10.5%). The time evolution of water uptake simulated by CropSyst better depicted the measured sap flow (water uptake) difference between wet and dry treatments. Simulations of soil water content by layer for the wet treatment, compared with measurements available for 17 d, yielded RMSEs from 0.022 to 0.024 m 3 m -3 and REs from 8.5 to 9.2%. For the dry treatment (12 d of measurements), the best simulations were obtained with the water uptake algorithms from CropSyst and CERES, with RMSE = 0.015 m 3 m -3 (both models) and RE = 6.4% (CropSyst) and 6.6% (CERES), compared with RMSE = 0.019 m 3 m -3 and RE = 8.1% for EPIC. Under the severe water stress at Davis, CropSyst had the best performance. This algorithm simulated changes in soil water content by layer (8 d of measurements available) with RMSE of 0.011 m 3 m -3 and RE of 5.0%, while the RMSE and RE values for CERES and EPIC were 0.016 and 0.019 m 3 m -3 and 7.6 and 9.0%, respectively. The more process-oriented algorithm (CropSyst) showed an increasing advantage as water stress severity increased. The EPIC algorithm had the poorest performance under water stress. This could be improved by modifying the value of the water extraction distribution parameter in EPIC, but with this change the wet treatment simulations at Prosser deteriorated substantially, indicative of limitations in EPIC's simple approach.

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.

Simulations of transpiration (T) and soil water depletion using daytime and 20 min interval models of similar general structure were compared. The performance of the models were evaluated using the measurements obtained from a full-canopy corn field subjected to wet and dry irrigation treatments. The wet treatment was well irrigated throughout the season while irrigation for the dry treatment was stopped when full canopy was reached. 20 min interval evapotranspiration (ET) in the wet treatment was determined using the Bowen ratio method and was used as a crop maximum ET input to the models. For both treatments, sap flow measurements averaged over 20 min intervals were performed continuously for 2 to 9-day periods, for a total of 40 days. These devices measure crop water uptake, which can be equated to T by neglecting plant storage. Sap flow per unit green leaf area was measured simultaneously on 7–10 plants per treatment and expressed on a per unit ground area basis. In addition, soil water content and root density were measured throughout the season in both treatments. The daytime and the 20 min (with output integrated for daytime periods) models performed similarly in the simulation of T for both irrigation treatments. Overall, the root mean square error (RMSE) fluctuated from 0.28 to 0.38 mm day−1, and the relative error (RE=100×RMSE/observed mean) from 7.2 to 10.5%. Also, both models performed similarly in simulating soil water content. No advantage was found from the use of the shorter time step model. The use of a daytime integration step appears sufficient unless short-time interval details are needed as model output or for use by other components of a larger model.

A field experiment was conducted at Prosser, WA in 1993, to evaluate the consistency of field techniques for measuring evapotranspiration (ET), transpiration (T), and soil evaporation (E) from a full-canopy corn field, and to evaluate the ability of sap flow gauges to detect differences in water use of corn under two contrasting irrigation treatments: full irrigation (wet treatment) and irrigation stopped when full canopy was reached (dry treatment). Twenty-minute ET rates for the wet treatment were determined using the Bowen ratio energy balance (BREB) method. Transpiration (sap flow) rates under both irrigation treatments were measured at 20-min intervals using gauges placed on individual corn plants. Daily E rates for the wet treatment were measured using microlysimeters, and also estimated from the difference between BREB, ET and sap flow measurements. Soil–water content in both treatment areas was monitored using neutron probe and gravimetric samples. Considering the normal variation of field measurements, the agreement and consistency among the different techniques used to determine ET and its components was adequate. Despite reductions in leaf area, dry matter production and grain yield, it was possible to differentiate transpiration rates (sap flow) between dry and wet irrigation treatments only at the end of the season. Microlysimeter measurements of daytime E were generally below 1 mm, with an average of 0.6 mm, representing 13.6% of daytime ET. Night time E values were lower, with an average of 0.2 mm. Large coefficients of variation were observed. The use of microlysimeters under low soil evaporation conditions may not be practical, requiring a large number of replications to estimate the average with reasonable confidence. Results also indicated, low agreement between E estimates obtained from the difference between BREB, ET and sap flow and E measured with microlysimeters. The variability of the measurements of sap flow per unit area was large for 20-min intervals, but decreased rapidly for daily to weekly intervals. Sap flow gauges seem capable of measuring weekly flow rates of field corn within ±10% and 80–95% confidence, and daily flow rates with 80% confidence. For shorter time intervals or lower tolerable errors, the number of gauges required increases dramatically.

The need for tools to evaluate cropping systems and their impact on production and the environment is increasing. Field experiments, which are usually site specific, require considerable resources and time to test hypotheses or management options. Computer models for cropping systems simulation are an alternative, but they require calibration and validation before applications can be developed for a given region. The ability of CropSyst, a cropping systems simulation model, to simulate spring and winter wheat yields and evapotranspiration in a wheat-fallow region of eastern Washington for different tillage and residue management practices was evaluated. Validation of the model was accomplished by (a) providing initial conditions prior to planting for each cropping season of a 6-year wheat-fallow rotation and (b) providing initial conditions at the beginning of the rotation without reinitializing the model in later years. The statistical structure (mean and SD) of simulations and field data were similar. The root mean square errors fluctuated from 7 to 14% of the observed mean for grain yield and from 5 to 9% for evapotranspiration, depending on the type of evaluation, and was 17% for soil moisture profile in the case of continuous 6-year rotation simulation without reinitialization. The Wilmott index of agreement fluctuated from 0.92 to 0.97 (an index of 1.0 indicating perfect agreement), with all values but one equal or better than 0.94. Similar water production functions were obtained from simulation and field data. Based on this validation, Crop'Syst appears promising as a tool to analyze the effectiveness of management practices for water conservation in a wheat-fallow cropping region.

In this work, a study was carried out to develop and evaluate several methods for estimating the daily vapor pressure deficit (VPD) at thermo-pluviometric stations (weather stations in which only temperature and precipitation are measured) located in a region with a homogeneous climate where there is at least one complete meteorological station. Four estimation methods were compared with the method of calculation proposed by the FAO used in the equations for calculating evapotranspiration (ET), which was considered as a reference. From the analysis of the operability of each method depending on the availability of data on the results obtained, it is concluded that the method proposed for estimating the vapor pressure deficit in climatic zones where there is little meteorological information is quite reliable, and that it can, therefore, be used as an input in empirical equations that need to know the VPD for calculating ET.

Four approaches to simulate N requirements of wheat were tested using data collected at the Auzeville experiment station of INRA, near Toulouse, France, from an experiment providing a wide range of soil N available for crop uptake. In these approaches, crop N requirements are expressed in terms of characteristic plant N concentration curves (maximum, critical, and minimum), representing expected concentration for a given crop N status throughout the growing season. Modeling approaches were evaluated for their ability to discriminate between N-limited and non-limited wheat plots as well as to properly represent the upper and lower limits of observed plant N concentrations. Best results were obtained using the growth dilution concept to represent the characteristic curves, while others based on temperature sums, growth stages, or fraction of the growth cycle were less satisfactory. Simulation of crop growth and N uptake based on N requirements estimated using the growth dilution concept resulted in a relationship between biomass at harvest and N uptake that correctly described an upper boundary for all observed data points. However, simulated and observed crop N uptake on a plot by plot basis resulted in low agreement. This was attributed to uncertainty in the measurement of initial soil N and crop N uptake, and the effect of other growth reducing factors (e.g. diseases) and possibly physical and/or chemical restrictions to field N uptake normally not accounted for by crop growth models.

Rainfed crop production is the main source of food and income in Burkina Faso. Environmental conditions are characterized by low and erratic rainfall, compounded by high temperatures and radiation loads. These factors make inadequate water availability the major cause for low crop yields and frequent crop failures. This paper outlines the procedure used to investigate the water-limited growth environment of an improved millet cultivar in Burkina Faso. A daily time-step cropping system simulation model (CropSyst) was used to simulate the soil water budget components and millet production potential, both spatially and temporally, by coupling the model with databases of soil type, long-term weather, and crop management using a geographic information system (GIS). From the cropping model outputs, two agroclimatic indices (Aridity Index and Crop Water Stress Index) that show the water-limited growth environment of the millet crop throughout the country were quantified and mapped with the help of the GIS. This allowed the identification of agroclimatic zones, as determined by the crop water needs. Millet productivity decreased from the south to the north of the country in relation with rainfall isolines and soil types. Locations with less than 500 mm of annual rainfall are marginal for millet, particularly on planosols and arenosols. In regions with rainfall above 700 mm, moisture availability is not a major limiting factor for the 90-day millet production, especially on regosols, cambisols, acrisols and nitosols. Overall, the approach followed in this study appeared promising for quantifying the growth environment of millet as affected by soil type and weather. It could also help to provide guidelines for crop water management and analysis of the suitability of improved crop cultivars.

The performance of a heat balance-based sap flow gauge design, capable of alternating variable heat input (VHI) and constant heat input (CHI), was analyzed. The gauge employs differentially wired thermocouples to monitor differences in stem temperature and radial heat losses. The VHI method employs a control circuit to maintain a constant temperature difference between a thermocouple placed upstream from the gauge heater and a thermocouple at the heated portion of the plant stem. The CHI method applies a constant voltage to the gauge heater with no control circuit. Simultaneously, on the same plants, the effect of the placement of thermocouples, either inserted into the stem structure or placed on the surface, was also investigated. Sets of 15 min and daily integrated measurements were compared with gravimetric water losses determined by lysimetry. The evaluation included three plant species: sunflower (Helianthus), maize (Zea maize) and potato (Solanum tuberosum). The results showed that the VHI gauges tended to underestimate flows for both the 15 min and daily sap flows. The CHI gauges gave generally better results and were easier to implement and monitor. No consistent differences in the performance of the inserted and surface-mounted thermocouples were found; any differences that were noted were generally small and of no practical significance. An exception was the case of sunflower CHI at large flow rates, when the inserted thermocouples outperformed the surface-mounted ones. Important departures between gauge sap flows and lysimetric plant water losses at low flow rates were observed. Gauge performance was better for daily integrated flows (relative absolute error, RAE ≈ 10% for combined data) than for 15 min interval average flows (RAE = 32–36%v for combined data). However, the performance of the short-time-interval gauge improved dramatically when low flows (< 10 g h−1) were not included in the analysis.

We tested the capability of CropSyst, a cropping system simulation model, to simulate cropping systems at two locations (Modena and Foggia) representative of the largest plain areas of Italy. Experimental data collected from rotation experiments during the period 1985–1991 were used to calibrate and validate the model. The data set available was not detailed enough to test most of the sub-models, but it was adequate for a satisfactory test of overall model performance. Simulations were conducted for 6-year rotations providing initial conditions of the first year without reinitializing the model in later years in the rotation sequence. Model evaluation was performed with the understanding that a large amount of variability was present in the data and that rotation effects other than water (e.g., diseases) were not accounted for by the model. The phenology sub-model proved sufficiently accurate, showing some limitation only in the case of winter cereals. Model simulations of maize, soybean, and barley growth at Modena, and sorghum and sunflower growth at Foggia were reasonably accurate. CropSyst was able to predict neither the yields of soybean grown as a second crop, nor the durum wheat yield variability in the rotations at Foggia. Although CropSyst was able to simulate reasonably well a number of cropping systems, the development of a more detailed field data base is desirable for further evaluation and improvement of the model for Italian conditions.

The long-term productivity, sustainability, and environmental impact of cropping systems cannot be assessed adequately using conventional agronomic experiments. This pap erdiscusses the use of computer-based technologies as support tools for this type of assessment, including crop growth simulation models, weather generators, geographical information systems, and risk assessment and economic models. Comprehensive systems that integrate these technologies are just emerging, offering great potential for the analysis of agricultural systems in the future.