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With as much as 4.8 °C increase in air temperature by end of 21st century, new crop cultivars are needed for adapting to the new climate. The objective of this study was to identify maize ( Zea mays L.) cultivar parameters that maintain yield under projected climate for late in the 21st century under full and deficit irrigation in a semi-arid region. The Root Zone Water Quality Model (RZWQM2) was calibrated with four years of maize data from northeastern Colorado, USA, under various irrigation conditions and was then used to simulate climate change effects on maize production with current management practices. Results showed that projected climate change decreased yield by 21% and biomass by 7% late in the 21st century (2070–2091) under full irrigation, compared to yield in the current climate (1992–2013). Under deficit irrigation, the corresponding reductions were 14% and 3%, respectively. Using the cultivar parameters calibrated with RZWQM2 for southern Colorado condition did not show yield decrease under future climate, but it simulated much lower yield under current climate in northeastern Colorado. A cultivar from the DSSAT (Decision Support Systems for Agrotechnology Transfer) crop database (GL 482) produced similar yield to experimental data under current climate and increased yield by 4% at full irrigation under future climate in northeastern Colorado. Using Latin Hypercube Sampling (LHS), we also identified 70 cultivars with longer maturity duration (between silking and physiological maturity) and higher grain filling rate for mitigating climate change effects on maize production. These two identified traits can guide plant breeders in developing cultivars for the future.

A correct simulation of crop responses to water stress is essential for a system model. In this study, we investigated three methods of quantifying water deficit stresses based on soil water measurements and their effects on simulating grain yield, biomass and canopy cover of corn ( Zea Mays L.). Experimental data were collected for six irrigation treatments designed to replace 40 to 100% of potential crop evapotranspiration (ETc) losses during the growing season, from 2008 to 2011 near Greeley, Colorado in a sandy loam soil (Limited Irrigation Research Farm, LIRF). Water available for plant uptake (PAW, plant available water) and the maximum PAW (MAW) in the soil were calculated for a constant 1 m soil profile from 45 days after planting till maturity. Water deficit stress factors were calculated as ratios of (1) PAW to alfalfa reference crop evapotranspiration (ETr) (WSF1), (2) PAW to MAW (WSF2), and (3) WSF2 to ETr (WSF3). Average WSF1, WSF2 and WSF3 over the growing season were related to end of the season grain yield, biomass, and fraction canopy cover measurements. These stress factors were implemented in the RZWQM2 cropping system model and the calibrated results compared with those obtained from using current stress factors in CERES-maize module in RZWQM2. The best simulation of the measured grain yields, biomass and LAI was obtained using WSF3. The modified model was also tested for simulating dryland and limited irrigation studies at Akron, CO, and irrigated corn in a sandy loam soil at Zaragoza, Spain and in a sandy soil at Gainesville, Florida, USA. In general, WSF3 gave slightly better simulations of grain yields, biomass and LAI than WSF2, WSF1 and the original stress factor.

a b s t r a c t Crop water production functions (CWPFs) are often expressed as crop yield vs. consumptive water use or irrigation water applied. CWPFs are helpful for optimizing management of limited water resources, but are site-specific and vary from year to year, especially when yield is expressed as a function of irrigation water applied. Designing limited irrigation practices requires deriving CWPFs from long-term field data to account for variation in precipitation and other climatic variables at a location. However, long-term field experimental data are seldom available. We developed location-specific (soil and climate) long-term averaged CWPFs for corn (Zea mays L.) using the Root Zone Water Quality Model (RZWQM2) and 20 years (1992-2011) of historical weather data from three counties of Colorado. Mean CWPFs as functions of crop evapotranspiration (ET), ET due to irrigation (ETa-d), irrigation (I), and plant water supply (PWS = effective rainfall + plant available water in the soil profile at planting + applied irrigation) were developed for three soil types at each location. Normalization of the developed CWPF across soils and climates was also developed. A Cobb-Douglas type response function was used to explain the mean yield responses to applied irrigations and extend the CWPFs for drip, sprinkler and surface irrigation methods, respectively, assuming irrigation application efficiencies of 95, 85 and 55%, respectively. The CWPFs developed for corn, and other crops, are being used in an optimizer program for decision support in limited irrigation water management in Colorado. © 2014 Elsevier B.V. All rights reserved.

Published in Agron. J. 106:81–94 (2014) doi:10.2134/agronj2013.0300 Copyright © 2014 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. ABSTRACT

a b s t r a c t Greater precipitation use efficiency (PUE) and economic returns by increasing cropping frequency through the addition of summer crops to the dryland winter wheat-fallow (WF) cropping system have been reported in the semiarid Central Great Plains of USA. However, due to the highly variable nature of precipitation and uncertain water availability, selection of a crop with assured positive net returns to add to the system to increase cropping frequency is a challenge in the absence of reliable seasonal precipitation forecasts. The objective of this study was to evaluate long-term yields and net returns of several potential summer crops at various soil water contents at planting to assess their potential use in increasing dryland cropping frequency. Three grain crops (corn (Zea mays L.), canola (Brassica napus), and proso millet (Panicum miliaceum L.)) and two forage crops (foxtail millet (Setaria italica L. Beauv.) and spring triticale (X Triticosecale rimpaui Wittm.)) for which the Root Zone Water Quality Model (RZWQM2) had been calibrated at Akron, CO and/or Sidney, NE, were selected for investigation through modeling. The calibrated model was used to simulate yield responses of the crops to 25, 50, 75 and 100% of plant available water (PAW) in the soil profile at planting using recorded weather data from Akron, CO and Sidney, NE (1948-2008). Average costs of production and 10-yr average commodity prices for northeast Colorado were used to calculate net returns for each of the crops at the varying PAW levels. All crops showed significant (p < 0.05) simulated yield increases in response to increasing initial PAW levels when those changes occurred in the entire 0-180 cm soil profile. The two forage crops gave greater net returns than the three grain crops for all initial PAW levels when calculated with 10-yr average prices received. Among the grain crops, proso millet was slightly more profitable than corn at Akron, while corn was the least profitable crop at Sidney. Using current commodity prices (13 September 2011) resulted in proso millet being the least profitable crop at Sidney, while corn was the most profitable grain crop at Akron and showed net returns that were similar to those found for the forage crops. The results of this study may guide the selection of a spring- or summer-planted crop and help farmers assess risk as they contemplate intensifying the WF system by using a measure or estimate of PAW at planting. Published by Elsevier B.V.

Published in Agron. J. 104:1182–1188 (2012) Posted online 7 June 2012 doi:10.2134/agronj2012.0048 Copyright © 2012 by the American Society of Agronomy, 5585 Guilford Road, Madison, WI 53711. All rights reserved. No part of this periodical may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopying, recording, or any information storage and retrieval system, without permission in writing from the publisher. I in the production of canola continues to grow as its use as a feedstock for biodiesel production (Blackshaw et al., 2011; Patil and Deng, 2009; Pavlista and Baltensperger, 2007) is evaluated in addition to its current use as a source of edible oil for human consumption (Starner et al., 1999). Th e central Great Plains of the United States is a region where canola has been considered as an alternative crop to be grown in dryland rotations with winter wheat (Nielsen, 1997, 1998), but most of the reported yields from studies done in this region have come from irrigated studies (Hergert et al., 2011; Pavlista et al., 2011). Yield results from dryland fi eld studies have not been reported across this region. Previous work at Akron, CO, (Nielsen, 1997) indicated that canola seed yield response to water use was

Currently, canola (Brassica napus L.) is gaining importance as a potential feedstock in biodiesel production industries, increasing the demand for canola production acreage. Agricultural system models that simulate canola growth and yield will help to assess the feasibility of canola production under various agroclimatic conditions. In this study, we adapted the CROPGRO model for simulation of spring canola in both Root Zone Water Quality Model (RZWQM2) and Decision Support System for Agrotechnology Transfer (DSSAT 4.0). Soil water, phenology, leaf area index (LAI) , biomass, plant height, and grain yield data from irrigation experiments conducted in 2005 on a Weld silt loam soil (fine, smectitic, mesic Aridic Argiustoll) in the semiarid climate at Akron, CO were used for model parameterization and calibration. Similar data from 1993, 1994, and 2006 were used for validation. Species and cultivar parameters for canola were developed using data from literature or by calibrating the existing CROPGRO-faba bean (Vicia faba L.) parameters. Grain yields across various irrigation levels and seasons were simulated reasonably well by RZWQM2 with root mean square error (RMSE) of215 kg ha -1 and index of agreement (d) of 0.98. Seasonal biomass development was simulated with RMSEs between 341 and 903 kg ha -1 , d between 0.55 and 0.99, and R 2 between 0.85 and 0.98. The CROPGRO-canola parameters developed were also tested within the DSSAT 4.0 cropping systems model and found to produce results with similar accuracy.

Long-term crop rotation effects on crop water use and yield have been investigated in the Central Great Plains since the 1990s. System models are needed to synthesize these long-term results for making management decisions and for transferring localized data to other conditions. The objectives of this study were to calibrate a cropping systems model (RZWQM2 with the DSSAT v4.0 crop modules) for dryland wheat (Triticum aestivum L.), corn (Zea mays L.), and proso millet (Panicum miliaceum L.) production in the wheat-corn-millet (WCM) rotation from 1995 to 2008, and then to evaluate the model from 1992-2008 for two additional rotations, wheat―fallow (WF) and wheat-corn-fallow (WCF) on a Weld silt loam soil under no-till conditions. Measured biomass and grain yield for the above three rotations were simulated reasonably well with root mean squared errors (RMSEs) ranging between 1147 and 2547 kg ha ―1 for biomass, and between 280 and 618 kg ha ―1 for grain yield. Corresponding index of agreement (d) ranged between 0.70 and 0.95 for biomass, and between 0.87 and 0.97 for grain yield. The validated model was further used to evaluate two additional crop rotations: wheat-millet-fallow (WMF) and wheat-corn-millet-fallow (WCMF) (1993-2008) without prior knowledge of the two rotations. We found that the model simulated the mean and range of yield and biomass of the three crops well. These results demonstrated that RZWQM2 can be used to synthesize long-term crop rotation data and to predict crop rotation effects on crop production under the semiarid conditions of eastern Colorado.

Dryland farming strategies in the High Plains must make efficient use of limited and variable precipitation and stored water in the soil profile for stable and sustainable farm productivity. Current research efforts focus on replacing summer fallow in the region with more profitable and environmentally sustainable spring and summer crops. In the absence of reliable precipitation forecasts for the crop growing season, farmers rely mainly upon knowledge of plant available water (PAW) in the soil profile at planting for making crop choice decisions. To develop a decision support strategy for crop selection based on initial PAW, experiments were conducted with spring triticale (X Titicosecale Wittmack), proso millet (Panicum miliaceum L.), and foxtail millet (Setaria italica L. Beauv.) under artificially controlled Low, Medium, and High initial PAW levels during 2004 and 2005 at Akron, Colorado, and Sidney, Nebraska. The objectives of this study were to adapt an existing cropping systems model for the simulation of triticale and millet and to evaluate simulations from the adapted model by comparing results with field data collected under varying initial PAW conditions. The Root Zone Water Quality Model with DSSAT v4.0 crop growth modules (RZWQM2) was used. Specifically, the Cropping System Model (CSM)–CERES–Wheat module was adapted for simulating triticale, and CSM–CERES– Sorghum (v4.0) module was adapted for simulating proso millet and foxtail millet. Soil water, leaf area index, grain yield, and biomass data for the highest PAW treatment from one crop season for each of the three crops were used to adapt and calibrate the crop modules. The models were then evaluated with data from the remaining PAW treatments. The proso millet module was further tested with four years of data from a crop rotation experiment at Akron from 2003 to 2006. Simulation results indicated that the adapted and calibrated crop modules have the potential to simulate these new crops under a range of varying water availability conditions. Consequently, these models can aid in the development of decision support tools for the season-to-season management of these summer fallow replacement crops under dryland conditions in semi-arid environments. Published by Elsevier B.V.

Accurate simulation of plant growth depends not only on plant parameters, but also on soil parameters. Although there is uncertainty in measured soil parameters and root distributions, their effects on simulated plant growth have been much less studied. This study evaluates the simulated responses of six crops, wheat (Triticum aestivum L.), maize (Zea mays L.), barley (Hordeum vulgare L.), soybean (Glycine max L. Merr.), peanut (Arachis hypogaea L.), and chickpea (Cicer arietinum L.), under various water and N management to different methods of estimating soil hydraulic properties and soil root growth factor (SRGF) in root zone water quality model (RZWQM2) that contains the decision support system for agrotechnology transfer (DSSAT) Version 4.0 plant growth models. The two methods of obtaining the soil water retention curve (SWRC) in RZWQM2 were based on (i) known soil water contents at both 33 and 1500 kPa suctions, or (ii) soil water content at 33 kPa only. The two methods of estimating saturated hydraulic conductivity (K sat ) were (i) soil texture class based average K sat or (ii) K sat calculated from effective porosity (difference between soil water contents at saturation and at 33 kPa). For the six crops, simulation results showed that the soil water balance was affected more by K sat than by SWRC, whereas the simulated crop growth was affected by both K sat and SWRC. Small variations in the SRGF did not affect soil and crop simulations, and SRGF could be estimated with a simple exponential equation.

Accurate simulation of agricultural management effects on N loss in tile drainage is vitally important for understanding hypoxia in the Gulf of Mexico. An experimental study was initiated in 1978 at Nashua, Iowa of the USA to study long-term effects of tillage, crop rotation, and N management practices on subsurface drainage flow and associated N losses. The Root Zone Water Quality Model (RZWQM) was applied to evaluate various management effects in several previous studies. In this study, the simulation results were further analyzed for management effects (tillage, crop rotation, and controlled drainage) on crop production and N loss in drain flow. RZWQM simulated the observed increase in N concentration in drain flow with increasing tillage intensity from NT (no-till) to RT (ridge till) to CP (chisel plow) and to MP (moldboard plow). It also adequately simulated tillage effects on yearly drain flow and yearly N loss in drain flow. However, the model failed to simulate lower corn and soybean yields under NT than under MP, CP, and RT. On the other hand, RZWQM adequately simulated lower yearly drain flow and lower flow-weighted N concentration in drain flow under CS (corn–soybean) and SC (soybean–corn) than under CC (continuous corn). The model adequately simulated higher corn yield under CS and SC than under CC. Applying the newly suggested N management practice for the Midwest of controlled drainage, the model simulated a 30% reduction in drain flow and a 29% decrease in N losses in drain flow under controlled drainage (CD) compared to free drainage (FD). With most of the simulations in reasonably close agreement with observations, we concluded that RZWQM is a promising tool for quantifying the relative effects of tillage, crop rotation, and controlled drainage on N loss in drainage flow. Further improvements on simulated management effects on crop yield and N mineralization are needed, however. © 2007 Elsevier B.V. All rights reserved.

Agricultural system models are tools to represent and understand major processes and their interactions in agricultural systems. We used the Root Zone Water Quality Model (RZWQM) with 26 years of data from a study near Nashua, IA to evaluate year to year crop yield, water, and N balances. The model was calibrated using data from one 0.4 ha plot and evaluated by comparing simulated values with data from 29 of the 36 plots at the same research site (six were excluded). The dataset contains measured tile flow that varied considerably from plot to plot so we calibrated total tile flow amount by adjusting a lateral hydraulic gradient term for subsurface lateral flow below tiles for each plot. Keeping all other soil and plant parameters constant, RZWQM correctly simulated year to year variations in tile flow (r 2 = 0.74) and N loading in tile flow (r 2 = 0.71). Yearly crop yield variation was simulated with less satisfaction (r 2 = 0.52 for corn and r 2 = 0.37 for soybean) although the average yields were reasonably simulated. Root mean square errors (RMSE) for simulated soil water storage, water table, and annual tile flow were 3.0, 22.1, and 5.6 cm, respectively. These values were close to the average RMSE for the measured data between replicates (3.0, 22.4, and 5.7 cm, respectively). RMSE values for simulated annual N loading and residual soil N were 16.8 and 47.0 kg N ha �1 , respectively, which were much higher than the average RMSE for measurements among replicates (7.8 and 38.8 kg N ha �1 , respectively). The high RMSE for N simulation might be caused by high simulation errors in plant N uptake. Simulated corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] yields had high RMSE (1386 and 674 kg ha �1 ) with coefficient of variations (CV) of 0.19 and 0.25, respectively. Further improvements were needed for better simulating plant N

The objective of this study was to explore if more crop-specific plant growth modules can improve simulations of crop yields, and N in tile flow under different management practices compared with a generic plant growth module. We calibrated and evaluated the Root Zone Water Quality Model (RZWQM) with the Decision Support for Agrotechnology Transfer (DSSAT v3.5) plant growth modules (RZWQM–DSSAT) for simulating tillage (NT — no till, RT — ridge till, CP — chisel plow, and MP — moldboard plow), crop rotation {CC — continuous corn, and CS — corn (Zea mays L.)–soybean [Glycine max (L.) Merr.]}, and nitrogen (N) (SA — single application at preplant, and LSNT — late spring soil N test based application) and manure (SM — fall injected swine manure) management effects on crop production and water quality. Data from 1978 to 2003 from a water quality experiment near Nashua (Nashua experiments), Iowa, USA, were used. The model was calibrated using data from one treatment plot and validated for the rest of the plots. Simulated management effects on annual N loading in tile flow were agreeable with measured effects in 85%, 99%, 88%, and 78% of the cases for tillage, crop rotation (CS vs. CC), N application timing (SA vs. LSNT), and swine manure applications (SM vs. SA), respectively. On average, the LSNT plots were simulated to have 359 kg ha �1 higher corn yield compared to SA, when the observed increase was 812 kg ha �1 . Grain yield simulations were not sensitive to differences between RT and NT, between SM and SA treatments, and between CS and CC. We conclude that considering the uncertainties of basic input data, processes in the field, and lack of site specific weather data, the results obtained with this RZWQM–DSSAT hybrid model were not much better than the results obtained earlier with the generic crop growth module. © 2007 Elsevier B.V. All rights reserved.

Process-based agricultural system models require detailed description of soil hydraulic properties that are usually not available. The objectives of this study were to evaluate the sensitivity of model simulation results to variability in measured soil hydraulic properties and to compare simulation results using measured and default soil parameters. To do so, we measured soil water retention curves and saturated soil hydraulic conductivity (Ksat) from intact soil cores taken from a long-term experimental field near Nashua, Iowa for the Kenyon–Clyde–Floyd–Readlyn soil association. The soil water retention curves could be well described using the pore size distribution index (λ). Measured λ values from undisturbed soil cores ranged from 0.04 to 0.12 and the measured Ksat values ranged from 1.8 to 14.5 cm/h. These hydraulic properties were then used to calibrate the Root Zone Water Quality Model (RZWQM) for simulating soil water content, water table, tile drain flow, and crop yield (corn and soybean) by optimizing the lateral Ksat (LKsat) and hydraulic gradient (HG) for subsurface lateral flow. The measured soil parameters provided better simulations of soil water storage, water table, and N loss in tile flow than using the default soil parameters based on soil texture classes in RZWQM. Sensitivity analyses were conducted for λ, Ksat, saturated soil water content (θs) or drainable porosity, LKsat, and HG using the Latin Hypercubic Sampling (LHS) and for LKsat and HG also using a single variable analysis. Results of sensitivity analyses showed that RZWQMsimulated yield and biomass were not sensitive to soil hydraulic properties. Simulated tile flow and N losses in tile flow were not sensitive to λ and Ksat either, but they were sensitive to LKsat and HG. Further sensitivity analyses using a single variable showed that LKsat in the tile layer was a more sensitive parameter compared to LKsat in other soil layers, and HG was the most sensitive parameter for tile flow under the experimental soil and weather conditions. © 2007 Elsevier B.V. All rights reserved.

In the 21st century, agricultural research has more difficult and complex problems to solve. The continued increase in population in the developing countries requires continued increases in agricultural production. However, the increased use of fertilizers, pesticides, and water required for the new higher yielding crop varieties has been causing environmental problems. Excessive leaching and runoff of agricultural chemicals are seriously affecting the quality of both the groundwater and surface waters. Increase in soil salinity, decline in soil organic matter, and increase in soil erosion remain the major problems in intensively farmed areas. Even the air quality is being affected. At the same time, market-based global competition is challenging the economic viability of traditional agricultural systems. Global climate change will pose additional challenges. The solution or mitigation of these changing and multiple problems will require continual improvement or changes in management and selectio...

Aiming to enhance and sustain farm production in India, an Agromet Advisory Service has been started. Numerical weather prediction output from a spectral model (T80L18) is fine-tuned by employing statistical and synoptic methods to prepare 24 h, 48 h and 72 h weather forecasts for 20 locations during the principal crop growing period of 1994. Also, weekly rainfall forecasts for 35 meteorological sub-divisions during monsoon period are produced. The accuracy of location-specific rain/no-rain event forecasts was found to be 70%. However, the root mean square error for the quantitative rainfall forecast was 17 mm; hence, there is a need for further improvement. The accuracy of maximum temperature (at 10 locations) and minimum temperature (at 18 locations) was 2 °C. The forecasts of rainfall event, temperature, cloud cover and wind speed showed considerable skill over random forecasts. The performance of trends and temporal variability in weekly sub-divisional rainfall forecasts were evaluated.

This volume of the Advances in Agricultural Systems Modeling series presents 14 different case studies of model applications to help make the best use of limited water in agriculture. These examples show that models have tremendous potential and value in enhancing site-specific water management for different soils and climates, and evaluating cropping system sustainability over the longer term, when model results are integrated with the available field measurements in experimental studies. Here we summarize applications of 11 system models commonly reported in the literature for agricultural water management along with those presented in this volume. These 11 models vary greatly in simulating agricultural system components for water balance related processes (the differences in crop growth and N balance processes were even greater among models), which need to be kept in mind when reading about the applications of each model. A sensor-based automated irrigation scheduling system is presented. Finally, we summarize further needs for experimental data and model improvements to enhance future water management applications. Abbreviations: ABA, abscisic acid; AgMIP, Agricultural Model Inter-Comparison and Improvement Project; APEX, Agricultural Policy Environmental eXtender model; APSIM, Agricultural Production Systems Simulator; CGMS, Crop Growth Monitoring System; CSM, cropping system model; CWPF, crop water production function; DSS, decision support system; DSSAT, Decision Support System for Agrotechnology Transfer; EPIC, Environmental Policy-Integrated Climate; ET, evapotranspiration; IRT, infrared thermometer; NCP, North China Plain; PALMS, Precision Agricultural Landscape Modeling System; PSA, plant–soil–atmosphere; RZWQM2, Root Zone Water Quality Model; STICS, Simulateur mulTIdisciplinaire pour les Cultures Standard; SWAP, Soil Water Atmosphere Plant; TSEB, two-source energy balance; WOFOST, WOrld FOod STudies; WUE, water use efficiency. L.R. Ahuja (laj.ahuja@ars.usda.gov), Liwang Ma (Liwang.Ma@ars.usda.gov), S.A. Saseendran (Saseendran. anapalli@ars.usda.gov), USDA-ARS, Agricultural Systems Research Unit, 2150 Centre Ave., Bldg. D, Ste. 200 Fort Collins, CO 80526. *Corresponding author. Q.X. Fang, Agronomy College, Qingdao Agricultural University, Changcheng Rd. 700, Chengyang District, Qingdao, Shandong, China, 266108 (fangqx@igsnrr.ac.cn). Robert J. Lascano, USDA-ARS, Wind Erosion and Water Conservation Research Unit, Cropping Systems Research Laboratory, 3810 4th St., Lubbock, TX 79415 (robert.lascano@ars.usda.gov). David C. Nielsen, USDA-ARS, Central Plains Resources Management Research Unit, 40335 County Rd. GG, Akron, CO, 80720-0400 (david.nielsen@ars.usda.gov). Enli Wang, CSIRO Land and Water, Christian Laboratory, Clunies Ross St., Black Mountain ACT 2601, Australia (Enli.Wang@csiro.au). Paul D. Colaizzi, USDA-ARS, Conservation and Production Research Lab., P.O. Drawer 10, 2300 Experiment Station Rd., Bushland, TX 79012-0010 (Paul.Colaizzi@ars.usda.gov). doi:10.2134/advagricsystmodel5.c15 Published December 5, 2014

Agricultural system models are potential tools for evaluating soil-water-nutrient management in intensive cropping systems. In this study, we calibrated and validated the Root Zone Water Quality Model (RZWQM) with both a generic plant growth module (RZWQM-G) and the CERES plant growth module (RZWQM-C) for simulating winter wheat (Tritictim aestivum L.) and maize (Zea mays L.) double cropping systems in the Northern China Plain (NCP), China. Data were obtained from an experiment conducted at Yucheng Integrated Agricultural Experimental Station (36 degrees 57'N, 11 degrees 36'E, 28 m asl) in the North China Plain (NCP) from 1997 to 2001 (eight crop seasons) with field measurements of evapotranspiration, soil water, soil temperature, leaf area index (LAI), biomass and grain yield. Using the same soil water and nutrient modules, both plant modules were calibrated using the data from one crop sequence during 1998-1999 when detailed measurements of LAI and biomass growth were available. The calibrated models were then used to simulate maize and wheat production in other years. Overall simulation runs from 1997 to 2001 showed that the RZWQM-C model simulated grain yields with a RMSE of 0.94 Mg ha(-1) in contrast to a RMSE of 1.23 Mg ha(-1) with RZWQM-G. The RMSE for biomass simulation was 2.07 Mg ha(-1) with RZWQM-G and 2.26 Mg ha(-1) with RZWQM-C model. The RMSE values of simulated evapotranspiration, soil water, soil temperature and LAI were 1.4 mm, 0.046 m(3) m(-3), 1.75 degrees C and 1.0 for RZWQM-G and 1.4 mm, 0.047 m(3) m(-3), 1.84 degrees C and 1.1 for RZWQM-C, respectively. The study revealed that both plant models were able to simulate the intensive cropping systems once they were calibrated for the local weather and soil conditions. Sensitivity analysis also showed that a reduction of 25% of current water and N applications reduced N leaching by 24-77% with crop yield reduction of 1-9% only. (c) 2005 Elsevier Ltd. All rights reserved.

The root zone water quality model (RZWQM) was developed primarily for water quality research with a generic plant growth module primarily serving as a sink for plant nitrogen and water uptake. In this study, we coupled the CERES-Maize Version 3.5 crop growth model with RZWQM to provide RZWQM users with the option for selecting a more comprehensive plant growth model. In the hybrid model, RZWQM supplied CERES with daily soil water and nitrogen contents, soil temperature, and potential evapotranspiration, in addition to daily weather data. CERES-Maize supplied RZWQM with daily water and nitrogen uptake, and other plant growth variables (e.g., root distribution and leaf area index). The RZWQM-CERES hybrid model was evaluated with two well-documented experimental datasets distributed with DSSAT (Decision Support System for Agrotechnology Transfer) Version 3.5, which had various nitrogen and irrigation treatments. Simulation results were compared to the original DSSAT-CERES-Maize model. Both models used the same plant cultivar coefficients and the same soil parameters as distributed with DSSAT Version 3.5. The hybrid model provided similar maize prediction in terms of yield, biomass and leaf area index, as the DSSAT-CERES model when the same soil and crop parameters were used. No overall differences were found between the two models based on the paired t test, suggesting successful coupling of the two models. The hybrid model offers RZWQM users access to a rigorous new plant growth model and provides CERES-Maize users with a tool to address soil and water quality issues under different cropping systems.

Simulation of water and nutrient processes can enhance intensive agriculture to help feed the world9s population in a sustainable manner. Due to excessive N application, environmental protection and agricultural sustainability have become major issues in agriculture. In this study, we calibrated and tested the RZWQM model to assess N management in a double-cropping system comprised of winter wheat ( Triticum aestivum L.) and corn ( Zea mays L.) at Luancheng, in the North China Plain. Data, including biomass, grain yield, soil water, and soil and crop N, were used from 2001–2003 field trials applying 200 to 800 kg N ha −1 yr −1 for five cropping seasons. In general, soil water, biomass, and grain yields were predicted better than plant N uptake or soil residual N. Once it had been tested and used to improve the understanding of N processes in this cropping system, the model was further used to evaluate the effects of alternative water and N management scenarios on N leaching. Typical application rates of both water and N could be reduced by about half based on these results, which would have high economic, social, and environmental impacts in China. The results also demonstrate the potential of RZWQM for evaluating N and water management practices in other regions and climates of the world with intensive agriculture.

Agricultural system models can provide an alternative, less time-consuming and inexpensive means of determining the optimum crop N requirement under varied soil and climatic conditions. In this context, we parameterized the Root Zone Water Quality Model (RZWQM) for winter wheat (Triticum aestivum L.) production and then evaluated it along with the CERES-Wheat model to assess their potential as N management tools at Akron, Co. Both models were evaluated with data from five N treatments (0, 28, 56, 84, and 112 kg N ha -1 ) and three crop seasons (1987-1988, 1988-1989, and 1989-1990). Data from 1987-1988 zero-N treatment were used for model calibration, and the rest of the data were used for model validation. Genetic coefficients for winter wheat (cv. TAM 107) were developed for the CERES-Wheat model. The crop parameters required for the generic crop model of RZWQM were parameterized using information from the literature or by calibration. Both models were calibrated first for soil moisture and then for biomass and grain yield. Grain yield predictions had a root mean square error (RMSE) of 500 and 363 kg ha -1 , respectively, for CERES-Wheat and RZWQM. Aboveground biomass was predicted with RMSEs of 1247 and 1441 kg ha -1 , respectively. Long-term simulations of both RZWQM and CERES for winter wheat growth using historical weather data (1912-2001) showed that 56 kg ha -1 N applied as broadcast incorporated is a viable N management option in eastern Colorado, taking into account the grain yield, crop N uptake, N leaching into groundwater, and residual soil N at harvest. Model simulations also showed that the wheat-fallow cropping system is less water use efficient than a continuous wheat system under rainfed agriculture in eastern Colorado.

Weekly cumulative rainfall forecasts were made for the meteorologically homogeneous areas of the Indian subcontinent, divided into meteorological subdivisions, by performing 7-day integrations of the operational Indian T80 Global Spectral Model every Wednesday during the six southwest monsoon seasons of 1994–99. Objective evaluations of the bias and accuracy of these forecasts during that 6-yr period are made through various forecast verification methods and are presented here. The skill or relative accuracy of the forecasts and some verification measures are quantified by computing the Heidke skill score (HSS), Hanssen–Kuipers discriminant (HKS), threat score (TS), hit rate (HR), probability of detection (POD), bias score, and false-alarm rate (FAR). The study revealed that the T80 model has a tendency to underpredict rainfall over most of the subdivisions falling on the windward side of the Western Ghats and sub-Himalayan areas. The model exhibited negative bias in rainfall simulations over the...

Crop growth simulation models, properly validated against experimental data have the potential for tactical and strategic decision making in agriculture. Such validated models can also take the information generated through site specific experiments and trials to other sites and years. For proper calibration and evaluation of crop simulation models, there is a need for collection of a comprehensive minimum set of data on soil, weather and crop management in all agronomic experiments. Keeping this in view, field experiments were conducted at Rajendranagar (17°19' N, 78°23' E; 542.3 m amsl) during 1994-97 for three popular varieties of rice viz. Sambamasuri, Rajavadlu and Tellahamsa under irrigated conditions and data collected. Genetic coefficients required for running the CERES-Rice v3.5 model were calculated and the performance of the model under the climate of the area was evaluated. The results of the study show that the model simulations of date of flowering for Sambamasuri, Rajavadlu and Tellahamsa were within an average error of 6.2, 5.7 and 6.7 days respectively. Similar errors in predictions of physiological maturity dates were 7.6, 6.7 and 7.2 days. The error in grain yield predictions by the model averaged at 7.9%, 8.3%, and 5.7% respectively for the three crop varieties. These results indicate that the CERES Rice v3.5 model is capable of prediction of grain yield and phenological development of the crop in the climatic conditions of Andhra Pradesh with reasonable accuracy and hence, the model have the potential for its use as a tool in making various strategic and tactical decisions related to agricultural planning in the state.

The CERES-Rice crop simulation model, calibrated and validated for the varieties PR106 in NW India. IR36 in central India and Jaya in south India, is used for nalysing the effect of climate change on rice productivity in the country. Plausible climate change scenario for the Indian subcontinent as expected by the middle of the next century taking into account the projected emissions of greenhouse gases and sulphate aerosols, in a coupled atmosphere-ocean model experiment performed at Deutsches Klimarechenzentrum, Germany, is adopted for the study. The adopted scenario represented an increase in monsoon seasonal mean surface temperature of the order of about 1.5° C over the south India and 1°C over northwest and central India in the decade 2040-49 with respect to the 1980s and an increase in rainfall of the order of 2 mm per day over south India while the simulated decrease of the order about -1 mm and -1.5 mm over northwest and central India respectively. The IPCC Business-as-usual scenario projection of plant usable concentration of CO2 about 460 PPM by the middle fo the next century are also used in the crop model simulation (CERES - Rice V3 Model). Simulation studies carried out with the climate change scenarios over different parts of the country are analysed and interpreted.

The CERES-Rice v3. crop simulation model, calibrated and validated for its suitability to simulate rice production in the tropical humid climate Kerala State of India, is used for analysing the effect of climate change on rice productivity in the state. The plausible climate change scenario for the Indian subcontinent as expected by the middle of the next century, taking into account the projected emissions of greenhouse gases and sulphate aerosols, in a coupled atmosphere-ocean model experiment performed at Deutsches Klimarechenzentrum, Germany, is adopted for the study. The adopted scenario represented an increase in monsoon seasonal mean surface temperature of the order of about 1.5°C, and an increase in rainfall of the order of 2 mm per day, over the state of Kerala in the decade 2040–2049 with respect to the 1980s. The IPCC Business-as-usual scenario projection of plant usable concentration of CO2 about 460 PPM by the middle of the next century are also used in the crop model simulation. On an average over the state with the climate change scenario studied, the rice maturity period is projected to shorten by 8% and yield increase by 12%. When temperature elevations only are taken into consideration, the crop simulations show a decrease of 8% in crop maturity period and 6% in yield. This shows that the increase in yield due to fertilisation effect of elevated CO2 and increased rainfall over the state as projected in the climate change scenario nearly makes up for the negative impact on rice yield due to temperature rise. The sensitivity experiments of the rice model to CO2 concentration changes indicated that over the state, an increase in CO2 concentration leads to yield increase due to its fertilisation effect and also enhance the water use efficiency of the paddy. The temperature sensitivity experiments have shown that for a positive change in temperature up to 5°C, there is a continuous decline in the yield. For every one degree increment the decline in yield is about 6%. Also, in another experiment it is observed that the physiological effect of ambient CO2 at 425 ppm concentration compensated for the yield losses due to increase in temperature up to 2°C. Rainfall sensitivity experiments have shown that increase in rice yield due to increase in rainfall above the observed values is near exponential. But decrease in rainfall results in yield loss at a constant rate of about 8% per 2 mm/day, up to about 16 mm/day.

The CERES-Rice version 3.0 crop growth simulation model was calibrated and evaluated for the agroclimatic conditions of the state of Kerala in India. Genetic coefficients were developed for the rice crop variety Jaya and used for the model evaluation studies. In four experiments using different transplanting dates during the virippu season (June to September) under rainfed conditions (i.e. no irrigation), the flowering date was predicted within an error of four days and date of crop maturity within an error of two days. The model was found to predict the phenological events of the crop fairly well. The grain yield predicted by the model was within an error of 3 for all the transplanting dates, but the straw yield prediction was within an error of 27. The high accuracy of the grain yield prediction showed the ability of the model to simulate the growth of the crop in the agroclimatic conditions of Kerala. It can be concluded from this study that the model can be used for making various strategic and tactical decisions related to agricultural planning in the state.

Agricultural sector is one of the sensitive areas which would be influenced by the projected global warming and associated climate change. In spite of the uncertainties about the precise magnitude of climate change on regional scales, an assessment of the possible impacts of changes in key climatic elements on our agricultural resources is important for formulating response strategies. In this study, vulnerability of wheat and rice crops in northwest India to the projected climate change is examined. CERES wheat and rice models adopted for the study were validated for their ability to reproduce yields at the selected NW Indian stations. The sensitivity experiments with these models showed higher yields for both wheat and rice (28% and 15% respectively for a doubling of CO2) under elevated CO2 levels. A 3°C (2°C) rise in air temperature nearly cancels out the positive effect of elevated CO2 on the wheat (rice) yields. While the wheat crops are found to be sensitive to increase in maximum temperature, the rice crops are vulnerable to increase in minimum temperature. The combined effect of enhanced CO2 and imposed thermal stress on the wheat (rice) crop is 21% (4%) increase in yield for the irrigation schedule presently practised in the region. While the adverse impacts of likely water shortage on wheat crops would be minimised to a certain extent under elevated CO2 levels, they would largely be maintained for the rice crops resulting in about 20% net decline in rice yields. In general, acute water shortage conditions combined with the thermal stress should adversely affect both the wheat and more severely the rice productivity in NW India even under the positive effects of elevated CO2 in the future.

Excessive N and water use in agriculture causes environmental degradation and can potentially jeopardize the sustainability of the system. A field study was conducted from 2000 to 2002 to study the effects of four N treatments (0, 100, 200, and 300 kg N ha-1 per crop) on a wheat (Triticum aestivum L.) and maize (Zea mays L.) double cropping system under 70 ± 15% field capacity in the North China Plain (NCP). The root zone water quality model (RZWQM), with the crop estimation through resource and environment synthesis (CERES) plant growth modules incorporated, was evaluated for its simulation of crop production, soil water, and N leaching in the double cropping system. Soil water content, biomass, and grain yield were better simulated with normalized root mean square errors (NRMSE, RMSE divided by mean observed value) from 0.11 to 0.15 than soil NO3-N and plant N uptake that had NRMSE from 0.19 to 0.43 across these treatments. The long-term simulation with historical weather data showed that, at 200 kg N ha-1 per crop application rate, auto-irrigation triggered at 50% of the field capacity and recharged to 60% field capacity in the 0-to 50-cm soil profile were adequate for obtaining acceptable yield levels in this intensified double cropping system. Results also showed potential savings of more than 30% of the current N application rates per crop from 300 to 200 kg N ha-1, which could reduce about 60% of the N leaching without compromising crop yields. Copyright © 2008 by the American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. All rights reserved.

Planting winter cover crops such as winter rye (Secale cereale L.) after corn and soybean harvest is one of the more promising practices to reduce nitrate loss to streams from tile drainage systems without negatively affecting production. Because availability of replicated tile-drained field data is limited and because use of cover crops to reduce nitrate loss has only been tested over a few years with limited environmental and management conditions, estimating the impacts of cover crops under the range of expected conditions is difficult. If properly tested against observed data, models can objectively estimate the relative effects of different weather conditions and agronomic practices (e.g., various N fertilizer application rates in conjunction with winter cover crops). In this study, an optimized winter wheat cover crop growth component was integrated into the calibrated RZWQM-DSSAT hybrid model, and then we compared the observed and simulated effects of a winter cover crop on nitrate leaching losses in subsurface drainage water for a corn-soybean rotation with N fertilizer application rates over 225 kg N ha -1 in corn years. Annual observed and simulated flow-weighted average nitrate concentration (FWANC) in drainage from 2002 to 2005 for the cover crop treatments (CC) were 8.7 and 9.3 mg L -1 compared to 21.3 and 18.2 mg L -1 for no cover crop (CON). The resulting observed and simulated FWANC reductions due to CC were 59% and 49%. Simulations with the optimized model at various N fertilizer rates resulted in average annual drainage N loss differences between CC and CON increasing exponentially from 12 to 34 kg N ha -1 for rates of 11 to 261 kg N ha -1, but the percent difference remained relatively constant (65% to 70%). The results suggest that RZWQM-DSSAT is a promising tool to estimate the relative effects of a winter crop under different conditions on nitrate loss in tile drains, and that a winter cover crop can effectively reduce nitrate losses over a range of N fertilizer levels.

[1] Increasing competition for land and water resources due to increasing demands from rapid population growth calls for increasing water use efficiency of irrigated crops. It is important to develop location-specific agronomic practices to maximize water use efficiency (WUE). Adequately calibrated and validated agricultural systems models provide a systems approach and a fast alternative method for developing and evaluating agronomic practices that can utilize technological advances in limited irrigation agriculture. The objectives of this study were to (1) calibrate and validate the CERES-maize model under both dryland and irrigated corn (Zea mays L.) production in northeastern Colorado and (2) use the model with a long-term weather record to determine (1) optimum allocation of limited irrigation between vegetative and reproductive growth stages and (2) optimum soil water depletion level for initiating limited irrigation. The soil series was a Rago silt loam, and the initial water content on 1 January of each year was equal to field capacity in the upper 300 mm and half of the field capacity below this depth. Optimum production and WUE with minimum nitrogen (N) losses were found when (1) a water allocation ratio of 40:60 or 50:50 (uniform) between vegetative and reproductive stages for irrigations up to 100 mm, and a ratio of 20:80 for irrigations above 100 mm was used; and (2) irrigation was initiated at 20% plant-available water (PAW) (80% depletion). When available water for irrigation is limited to 100 mm, irrigating 50% of the area with 200 mm of water at 20:80 split irrigations between the vegetative and reproductive stages produced greater yield than irrigating 100% of the area with 100 mm water. Concepts developed in the study can potentially be adapted to other locations, climates, and crops. However, precise site-specific recommendations need to be developed for each soil-climate zone using the validated system model.

The ability to predict and manage crop growth under varying available water conditions is of vital importance to the agricultural community since water is the most important limiting factor for agricultural productivity, especially in semi–arid regions. This study evaluated an agricultural system model, the USDA–ARS Root Zone Water Quality Model (RZWQM), for its ability to simulate the responses of corn (Zea mays L.) growth and yield to various levels of water stress. Data sets collected in 1984, 1985, and 1986 in northeastern Colorado were used for model evaluation. Three irrigation levels were imposed in 1984 and four levels in 1985 and 1986. Measurements included soil water content in 1985, leaf area index (LAI) and aboveground biomass in 1984 and 1985, and corn yield and plant height in 1984, 1985, and 1986. The RZWQM was calibrated for the lowest (driest) irrigation treatment in 1985 and then used to predict soil water and agronomic attributes for other irrigation treatments in all three years. Overall, the model responded well to irrigation treatments and weather conditions. Prediction of plant height was adequate in 1985 and 1986. Although biomass was reasonably predicted in early and late growing seasons, it was over–predicted during the middle growing season in both 1984 and 1985. Maximum LAI and plant height were over–predicted in 1984, however. Total soil water storage was well predicted in 1985, and so was evapotranspiration (ET) during the crop growing season. Yield predictions were within 1% to 35% of measured values for all the three years. Even with a low prediction of yield in 1986, the model correctly simulated the relative increase of yield with irrigation amount. Therefore, once RZWQM is calibrated for a location, it can be used as a tool to simulate relative differences in crop production under different irrigation levels and as a guide to optimize water management.