Agricultural System Models Research Articles

Quantitative model of irrigation effect on maize yield by deep neural network

A rapidly expanding world population and extreme climate change have made food production a crucial challenge in the twenty-first century. Improving crop management could be an effective solution for this challenge. However, due to the associated cost and time to perform field works, researchers are widely rely on agricultural systems modeling to examine the impacts of different crop management scenarios. Meanwhile, due to the complexity of agricultural systems modeling, their applications in producing practical knowledge for producers are limited. Meanwhile, deep learning techniques have been recognized as the preferred method compared to other machine learning techniques, especially when dealing with large datasets. In addition, deep learning techniques are easily adopted by non-experts due to the feature of learning ability that can automatically discover the classifications from raw data. Meanwhile, one of the drawbacks of using deep learning techniques is the training time, which can last anywhere from a couple of weeks to even a few months. Therefore, the goal of this study is to examine the applicability of deep learning techniques to compute a numerical model of crop growth. In this study, an agricultural systems model known as the Decision Support System for Agrotechnology Transfer (DSSAT) is used to evaluate the impacts of irrigation amount and time of application on crop yield. A deep learning network is utilized and trained by incorporating the large amounts of DSSAT models inputs (i.e., precipitation date, precipitation amount, irrigation date irrigation amount) and output (i.e., maize yield at the end of the growing season). However, in order to simplify the process, we combined the amount of irrigation and rainfall together and presented them in the form of the amount of water per day. Experimental results have demonstrated the effectiveness of this proposed deep learning technique in crop yield prediction.

How climate scenarios alter future predictions of field-scale water and nitrogen dynamics and crop yields

Climate change scenarios are widely used for exploring future changes in environmental systems. However, many aspects of the uncertainties associated with the use of climate change scenarios in environmental systems modeling have not yet been studied sufficiently. We explore how the way that baseline scenarios are defined and general circulation model (GCM) outputs are used affects climate change impact assessments of agricultural systems. Our study builds on a previously validated agricultural systems model, the Root Zone Water Quality Model (RZWQM), coupled with the Decision Support System for Agrotechnology Transfer (DSSAT), which models a tiled-drained field in central Illinois of the United States and uses nine GCM outputs to investigate the effects. Our model simulations demonstrated the following three results. Firstly, the evaluation of climate change impacts presented a significant difference between the types of baseline used. The baseline scenario should be defined using the bias-corrected retrospective GCM outputs. Secondly, once GCM outputs are bias-corrected, the selective use of GCM outputs did not add significant value over using all available GCM outputs to provide more plausible future descriptions of agricultural systems' responses. Notably, however, selective use may have impacts comparable to carbon dioxide (CO2) emission scenarios in the field-scale agricultural climate change impact assessments. Thirdly, raw GCM outputs should be avoided for the predictions of field-scale agricultural systems’ responses to climate change. Our findings can help provide a clearer picture of how GCM outputs should be used in agricultural systems modeling and might enable us to have more plausible descriptions of how future agricultural systems might unfold.

Optimizing Nitrogen and Residue Management to Reduce GHG Emissions while Maintaining Crop Yield: A Case Study in a Mono-Cropping System of Northeast China

Reducing the use of nitrogen fertilizers and returning straw to field are being promoted in northeast China (NEC). In this paper, the agricultural production system model (APSIM) was applied to assess the long-term variations of crop yield and soil GHG emissions in a maize mono-cropping system of NEC, and the simulation results were combined with lifecycle assessment to estimate annual GHG emissions (GHGL) and GHG emission intensity (GHGI, GHG emissions per unit yield) of different agricultural practices. Under current farmers’ practice, emissions due to machinery input (including production, transportation, repair, and maintenance) and soil organic carbon (SOC) decline accounted for 15% of GHGL, while emissions from nitrogen fertilizer input (production and transportation) and direct N2O emissions from soil accounted for the majority (~60% of GHGL). Current farmers’ practice in terms of N application and residue management are nearly optimal for crop production but not for climate change mitigation. Reducing N input by 13% and increasing straw retention by 20% can maintain crop yield and SOC, and also reduce GHGL and GHGI by 13% and 11%, respectively. However, it is not feasible to incorporate the straw used as household fuel into soil, which could incur substantial fossil CO2 emissions of 3.98 Mg CO2-eq ha−1 resulting from the substitution of coal for straw. APSIM was successful in simulating crop yield, N2O emissions, and SOC change in NEC, and our results highlight opportunities to further optimize management strategies (especially for the nitrogen and straw management) to reduce GHG emissions while maintaining crop yield.

Soil Phosphorus Modeling for Modern Agriculture Requires Balance of Science and Practicality: A Perspective.

The use of phosphorus (P) fertilizers in arable crop and pastoral systems is expected to change as modern agriculture is challenged to produce more food with fewer inputs. Agricultural systems models offer a dual purpose to support and integrate recent scientific advances and to identify strategies for farmers to improve nutrient efficiency. However, compared with nitrogen and carbon, advances in P modeling have been less successful. We assessed the potential opportunity of P modeling to increase P efficiency for modern agriculture and identified the current challenges associated with modeling P dynamics at the field scale. Three major constraints were (i) a paucity of detailed field datasets to model strategies aimed at increasing P use efficiency, (ii) a limited ability to predict P cycling and availability under the local effects of climate change, and (iii) a restricted ability to match measured soil P fractions to conceptual and modelable pools in soils with different mineral properties. To improve P modeling success, modelers will need to walk a tightrope to balance the roles of assisting detailed empirical research and providing practical land management solutions. We conclude that a framework for interdisciplinary collaboration is needed to acquire suitable datasets, continually assess the need for model adjustment, and provide flexibility for progression of scientific theory. Such an approach is likely to advance P management for increased P use efficiency.

Can measured soil hydraulic properties simulate field water dynamics and crop production?

Agricultural system models simulate soil water dynamics and crop evapotranspiration (ETc) and growth to enhance soil and crop management. To achieve this, simulations must be critically evaluated against field experimental data in different field management conditions. In many evaluations, simulations deviated from field measurements, which can be due to the quality of model input parameters. Replacing soil hydraulic properties indirectly derived from soil textural data by measured soil hydraulic property may decrease the discrepancy between measured and simulated soil water status. What is the benefit of using measured soil hydraulic properties in a model instead of pedo-transfer-function (PTF) based approaches to estimate the hydraulic properties indirectly? The objective of this study was to investigate the effect of using measured soil hydraulic property input parameters (SHPIP) as Root Zone Water Quality Model (RZWQM2) inputs compared to PTF-based indirectly derived hydraulic parameter inputs with and without calibration. A field experiment with soybean, corn, and fallow soil was conducted. Five model scenarios using measured SHPIP and SHPIP derived from soil texture as model inputs were created. The results indicate that, RZWQM2 showed a high sensitivity to the SHPIP calibration for fallow and corn season. Uncalibrated measured SHPIP yielded better simulation results than other SHPIP scenarios with regard to soil water flux, crop evapotranspiration, and soybean yield during the validation. While, the calibration of the SHPIP in corn and fallow slightly helped soybean soil water prediction at the surface depth. Hence, with representative measurements of SHPIP, it was possible to improve model simulations even without calibrating the input parameters.

On the relation between soil water holding capacity and dryland crop productivity

Plant available water holding capacity (PAWC) influences the efficiency of rainfall used by crops and consequently crop productivity in dryland cropping systems. However, how soil PAWC interacts with rainfall to determine crop yield has not been quantified systematically. In this study, we used an agricultural production systems model (APSIM) and wheat as an example to quantify crop growth and yield in response to a wide range of soil PAWCs. Results show that wheat yield increased with soil PAWCs following a negative exponential curve. There is a critical PAWC above which PAWC has no impact. A summary model incorporating the impact of soil PAWC, annual rainfall and the fraction of rainfall during growing season together could explain over 95% variations in long-term averaged wheat yield. The results and the summary model provide the scientific basis to systematically explain crop yield variation in response to soil PAWC change across contrasting climates.

Do farmers misuse pesticides in crop production in China? Evidence from a farm household survey.

In China, pesticide overuse and underuse have been studied. However, little empirical evidence has been provided for pesticide misuse. Using data from a farm household survey in 2016, we quantitatively analyzed Chinese farmers' pesticide misuse in the production of rice, apple, tea, and greenhouse vegetables. The results show that pesticide misuse is common in China. However, the misuse is found to be lower in rice production than in the production of apple, tea, and greenhouse vegetables. Compared with that of the major pests, pesticide misuse is much more common for the control of the secondary pests. In a considerable number of observations, some farmers even failed to indicate the pests they controlled or the pesticides they used. Pesticide misuse may result in both the overuse and underuse of pesticides. In China, pesticide misuse could be attributed to the lack of pest management knowledge among farmers, limited accessibility to a high-quality governmental agricultural extension service, misleading information, and absence of pest prediction and forecast. Hence, a socialized agricultural extension system and new agricultural extension models adapted to different farmers are expected to be developed and implemented in China. © 2019 Society of Chemical Industry.

Use of identifiability analysis in designing phenotyping experiments for modelling forage production and quality.

Agricultural systems models are complex and tend to be over-parameterized with respect to observational datasets. Practical identifiability analysis based on local sensitivity analysis has proved effective in investigating identifiable parameter sets in environmental models, but has not been applied to agricultural systems models. Here, we demonstrate that identifiability analysis improves experimental design to ensure independent parameter estimation for yield and quality outputs of a complex grassland model. The Pasture Simulation model (PaSim) was used to demonstrate the effectiveness of practical identifiability analysis in designing experiments and measurement protocols within phenotyping experiments with perennial ryegrass. Virtual experiments were designed combining three factors: frequency of measurements, duration of the experiment. and location of trials. Our results demonstrate that (i) PaSim provides sufficient detail in terms of simulating biomass yield and quality of perennial ryegrass for use in breeding, (ii) typical breeding trials are insufficient to parameterize all influential parameters, (iii) the frequency of measurements is more important than the number of growing seasons to improve the identifiability of PaSim parameters, and (iv) identifiability analysis provides a sound approach for optimizing the design of multi-location trials. Practical identifiability analysis can play an important role in ensuring proper exploitation of phenotypic data and cost-effective multi-location experimental designs. Considering the growing importance of simulation models, this study supports the design of experiments and measurement protocols in the phenotyping networks that have recently been organized.

Review of Agricultural and Rural Development System Models and Frameworks to Support Farming as a Business via Benchmarking: The Case of Tanzania

Review of Agricultural and Rural Development System Models and Frameworks to Support Farming as a Business via Benchmarking: The Case of Tanzania

Integrating GLEAMS sedimentation into RZWQM for pesticide sorbed sediment runoff modeling

Sediment transport from agricultural fields to native waterways is a significant pollution vector, not just for the bulk sediment, but also for additional mass of pesticides traveling offsite that are sorbed to soil particles. Existing field scale models that track plant growth as well as the fate and transport of applied pesticides lack an integrated sediment transport component. This study sought to address this lack of available modeling tools for researchers and regulators by integrating the sediment and surface flow components of Groundwater Loading Effects of Agricultural Management Systems (GLEAMS) model into the mature Root Zone Water Quality Model (RZWQM) to create a derivative model named RZWQM-Sed. Previous research identified RZWQM as a quick running, agricultural field scale model that accurately estimated offsite transport of solutes. Unlike other well performing field scale agricultural models, the full source code of RZWQM was available for modification and extension. However, RZWQM lacked a sediment component and thus could not measure all pollutants moving offsite. GLEAMS sedimentation was chosen for integration due to its well documented history, compatibility with the RZWQM codebase, and source code availability. Sensitivity analysis of the RZWQM runoff variables showed that the residual water content, saturated water content, and bubbling pressure from the Brooks-Corey equation had the highest influence for RZWQM followed by the saturated hydraulic conductivity and the non-Brooks-Corey bubbling pressure. Analysis of GLEAMS variables showed the most significant variables are the USLE parameters (cfact, pfact, ksoil) and Manning's N. The latter variable only showed sensitivity at very high surface roughness while the USLE parameters had a linear relationship over the entire domain. The integrated model was calibrated and validated using multiple real-world datasets spanning ranges of space and time. The final model performed well in the primary task of predicting the mass of sorbed chemicals in the tailwater (Nash-Sutcliffe coefficient > 0.3).

Robust fractional programming approach for improving agricultural water-use efficiency under uncertainty

Water-use efficiency and uncertainty treatment are foci in the modeling of agricultural water management systems. To address these challenging issues, a robust fractional programming (RFP) method that coupled fractional programming with robust optimization was developed in this study to improve agricultural water-use efficiency under uncertainty. RFP improved upon the fractional programming by being able to tackle highly uncertain information without known distributions. It also extended the capability of the robust optimization method in addressing ratio optimal problems. To demonstrate its effectiveness and applicability, RFP was applied to a long-term agricultural water resources management problem in arid north-west China, where water scarcity and low water-use efficiency hindered local development. It generated benefit- and risk-explicit plans for crop pattern adjustments. Vegetables were recommended as the preferred crop. A number of scenarios combining different fluctuation and protection levels were analyzed and interpreted with practical implications. It was observed that higher water-use efficiency could be achieved through reducing parametric uncertainty and risk-aversion levels. Simulation experiments validated that the benefits claimed by the RFP model were sufficiently conservative and could be reliably achieved. The comparisons of RFP results against the baseline operations and those from two other alternatives demonstrated that, RFP could result in higher resource-use efficiency and controllable system-violation risks. The developed approach is also applicable to other optimization problems aiming at enhancing resource-use efficiency under uncertainty.

KAJIAN PRODUKTIVITAS DAN EFISIENSI PEMANFAATAN LAHAN SISTEM PERTANIAN TERPADU PADA AGROEKOSISTEM LAHAN KERING DATARAN RENDAH DESA FATUKANUTU KABUPATEN KUPANG

Research about land productivity and efficiency of integrated farming systems on dryland agroecosystems had been conducted in farmers owned land in Fatukanutu village with land characteristics at a slope of <15% and altitude <500 m above the sea level. This research is secondyear research, which was carried out from January to November 2018. The purposes of this study are to implement an integrated farming system in dryland and lowland agroecosystems, to assess land productivity in an integrated agricultural system model (SPT) and the efficiency of land use on cropping patterns in the SPT model in lowland and dryland agroecosystems. The results showed that the model applied in the field is a banana-based integrated farming system, which is following the local and potential components such as livestock, food crops (maize), vegetables and fodder ( lamtoro) with intercropping harvesting system (Tumpangsari and TumpangGilir). Compared to monoculture crops, land productivity in integrated farming systems in dryland banana zone in agroecosystems is more productive. Also, cropping patterns in SPT model are beneficial because the LER value is > 1, there is efficiency in land use on banana-based SPT, with the LER value of banana-corn 1.15, banana-vegetable 1.65 and banana-maize-vegetable 2.80

Building and evaluation of a dynamic model for assessing impact of smallholder endowments on food security in agricultural systems in highland areas of central America (SASHACA)

Smallholder agricultural systems have an important role in world food production. Agricultural smallholders in highland areas of the Mesoamerican dry-corridor are central to improve farming systems. The key issue is that the subsistence smallholders are joint producer-consumers, with insufficient food production due to extreme environmental and weather conditions or incomes to supplement their own production from market sources. Modelling offers an approach for testing and improving knowledge about complex systems. The aim of this paper is to describe and assess a biophysical and socio-economic model of the smallholder agricultural systems based on maize-bean intercropping in highland areas of Central America. A Vensim® DSS system dynamics model was developed for assessing the impact of Smallholder endowments on food security and to identify critical points to achieve sustainable food security and poverty alleviation in Agricultural Systems in Highland Areas of Central America (SASHACA). The SASHACA model integrates scientific and practical knowledge of crop management, labour, soil water content, soil nitrogen, food consumption and economic components of the system. Model evaluation was conducted through a wide set of tests for assessment of dynamic models and statistical comparison of simulated versus observed data derived from surveys. The model simulates realistic outputs and presents logical behavioural representation. The maximum relative uncertainty of output variables ranged from 30% to 53% for univariate and multivariate sensitivity analyses respectively. The model proved to be adequate for assessing food security under scenarios in low data availability areas. The SASHACA model could be adapted to simulate a wide range of smallholder agricultural systems in highland areas of Central America, and potentially in other locations.

Simulating hydrologic cycle and crop production in a subsurface drained and sub-irrigated field in Southern Quebec using RZWQM2

Agricultural system models are promising tools in evaluating the agro-environmental effects of water management practices. However, very few models have been tested using a comprehensive hydrologic data set. The present study’s objective was to evaluate the hydrologic component of RZWQM2 (Root Zone Water Quality Model) using a comprehensive hydrological dataset including subsurface tile drainage, subirrigation, soil water content, sap flow and crop growth data such as leaf area index, crop yield and crop growth stages. Drawing on 2008 and 2009 data from a farm site in Southern Quebec, the RZWQM2 model showed accurate simulation in soil water content, sap flow, growth stage, leaf area index, and crop yield. While mean values for growing season tile flow under both free drainage (FD) and controlled drainage with subirrigation (CD-SI) were reasonably accurate, winter tile flow was significantly overestimated, indicating RZWQM2’s reliability to be compromised by its imperfect winter drainage process. Accordingly, a Kalman filter technique was applied to enhance model reliability and reduce predictive uncertainties. A novel RZWQM2 model equipped with a Kalman filter algorithm adequately simulated, in both calibration and validation phases, the hydrology and corn growth which occurred under both FD and CD-SI systems at the selected field site. Simulation results suggest that RZWQM2 model can be used for water management under subsurface drained and irrigated field and the Kalman filter technique significantly improved the accuracy of RZWQM2 model in simulating winter drainage in cold areas.

A Weather Generator-Based Statistical Downscaling Tool for Site-Specific Assessment of Climate Change Impacts

Abstract. Statistical downscaling approaches are usually used to bridge the gap between climate model outputs and data requirements of impact models such as crop and soil erosion models. This study synthesizes, integrates, and standardizes a statistical downscaling method that was initially developed in 2005 and subsequently evaluated and improved during the last decade. A new downscaling software program, Generator for Point Climate Change (GPCC), has been developed to automate and visualize the method to assist end users with detailed technical and user documentation. GPCC readily generates daily time series of climate change scenarios for local and site-specific climate change impact studies using monthly projections from global climate models or regional climate models. The downscaled variables include precipitation and maximum and minimum temperatures. This software provides a simple but effective climate downscaling tool for assessing the impacts of climate change on crop production, soil hydrology, and soil erosion at a field scale. The tool can also provide an alternative downscaling method to facilitate the international collaborative efforts of the Agricultural Model Intercomparison and Improvement Project (AgMIP) for simulation of world food production and food security assessment. The detailed downscaling methods, their scientific bases, and the advantages of GPCC over other commonly used downscaling methods are presented. GPCC is written in the Matlab language, and a standalone version can be run on Windows XP or above without Matlab software. The tool has a graphical user interface that is simple and easy to generate downscaled climates as well as to visualize downscaled outputs. Each interface tab and key button and their functions are described to facilitate its widespread application. Keywords: Agricultural system models, Climate change impacts, Generator for point climate change, Interface, Statistical downscaling, Stochastic weather generator.

Development of an irrigation scheduling software based on model predicted crop water stress

Modern irrigation scheduling methods are generally based on sensor-monitored soil moisture regimes rather than crop water stress. Crop water stress is difficult to measure in real-time, but can be computed using agricultural system models. In this study, an irrigation scheduling method and its facilitate software based on RZWQM2 model (Root Zone Water Quality Model) predicted crop water stress were developed and evaluated. The timing of irrigation was based on the occurrence of model-simulated water stress, while the depth of irrigation was based on the fraction of the soil moisture deficit (K) needed to replenish the soil water content (θ) at any given time to field capacity (θfc), i.e., from θt0 to θt0+K(θfc-θt0). The predicted water stress for different K values was tested based on RZWQM2 scenarios calibrated against data collected in a drip-irrigated corn (Zea mays L.) field near Greeley, Colorado, USA between 2008 and 2010, and in a sprinkler irrigated soybean [Glycine max (L.) Merr.] field in Noxubee, Mississippi, USA in 2014. For the Colorado site, the simulated full irrigation (K =1) using this newly developed water stress-based irrigation approach saved 30.5%, 17.3% and 7.1% in total irrigation depth in successive years, whereas higher frequency with 60–90% of full irrigation at each event (0.6 ≤ K ≤ 0.9) provided water savings of as much as 35%, 30%, and 16%, respectively. The water stress-based irrigation scheme showed that crop yield was not affected, with a negligible change about 0.03–3.81% decrease. These water savings were a result of the water stress-based irrigation regime maintaining sufficient water to meet crop root water uptake requirements without constantly fully rehydrating the soil, thereby minimizing evaporation from the soil surface and soil water storage after grain filling. For the Mississippi site, this newly developed water stress-based irrigation software could improve crop yield by 291 kg ha−1 though consume 3.43 cm more water than field irrigation regime. Similarly, high frequency irrigation (lower K) under water stress-based regime resulted in higher water use efficiency. This study suggested that the water stress-based irrigation scheme could save water use and maintain crop yield in semi-arid region, while in humid region it could increase crop yield while consume more water. Further work is needed to install this system in an irrigated field and test its performance under different climate and soil conditions.

Adapting crop rotations to climate change in regional impact modelling assessments

The environmental and economic sustainability of future cropping systems depends on adaptation to climate change. Adaptation studies commonly rely on agricultural systems models to integrate multiple components of production systems such as crops, weather, soil and farmers' management decisions. Previous adaptation studies have mostly focused on isolated monocultures. However, in many agricultural regions worldwide, multi-crop rotations better represent local production systems. It is unclear how adaptation interventions influence crops grown in sequences. We develop a catchment-scale assessment to investigate the effects of tactical adaptations (choice of genotype and sowing date) on yield and underlying crop-soil factors of rotations. Based on locally surveyed data, a silage-maize followed by catch-crop-wheat rotation was simulated with the APSIM model for the RCP 8.5 emission scenario, two time periods (1985–2004 and 2080–2100) and six climate models across the Kaituna catchment in New Zealand. Results showed that direction and magnitude of climate change impacts, and the response to adaptation, varied spatially and were affected by rotation carryover effects due to agronomical (e.g. timing of sowing and harvesting) and soil (e.g. residual nitrogen, N) aspects. For example, by adapting maize to early-sowing dates under a warmer climate, there was an advance in catch crop establishment which enhanced residual soil N uptake. This dynamics, however, differed with local environment and choice of short- or long-cycle maize genotypes. Adaptation was insufficient to neutralize rotation yield losses in lowlands but consistently enhanced yield gains in highlands, where other constraints limited arable cropping. The positive responses to adaptation were mainly due to increases in solar radiation interception across the entire growth season. These results provide deeper insights on the dynamics of climate change impacts for crop rotation systems. Such knowledge can be used to develop improved regional impact assessments for situations where multi-crop rotations better represent predominant agricultural systems.

Global sensitivity and uncertainty analysis of nitrate leaching and crop yield simulation under different water and nitrogen management practices

Assessing the sensitivity and uncertainty of soil-crop models is beneficial to model calibration and development of best water and N management practices. This study adopted the Morris screening method and the Sobol’ variance-based method, combined with an agricultural system model (WHCNS), to analyze the global sensitivity and uncertainty of nitrate leaching and crop yield to model input parameters under different water and N management practices. A two-year field experiment was conducted in a desert oasis of Inner Mongolia, China using a factorial combination of standard (Istd, standard, 750mm per season; Nstd, standard, 138kgNha−1) and conservation (Icsv, conservation, 570mm per season; Ncsv, conservation, 92kgNha−1) levels of irrigation and N fertilization: IstdNstd, IstdNcsv, IcsvNstd and IcsvNcsv. Sensitivity analysis (SA) based on this experiment showed that nitrate leaching demonstrated significant sensitivity to soil hydraulic and crop parameters, but generally low sensitivity to N transformation parameters. Based on Sobol’ SA, crop parameters accounted for 64.3%, 63.2%, 39.2% and 39.2% of simulated nitrate leaching variability for the IstdNstd, IstdNcsv, IcsvNstd and IcsvNcsv treatments, respectively. The greater the crop water and N stress, the stronger the parameters interaction. Uncertainty analysis showed the average amount of nitrate leaching under Istd (135.3kgNha−1) to be 2.3 times greater than under Icsv (58.0kgNha−1); however, the distributions of yield between the four treatment combinations did not show significant differences. Overall, irrigation practice was the main factor influencing the parameter sensitivities and the uncertainty of nitrate leaching and crop yield simulation.

Maintaining rice production while mitigating methane and nitrous oxide emissions from paddy fields in China: Evaluating tradeoffs by using coupled agricultural systems models

China is the largest rice producing and consuming country in the world, accounting for more than 25% of global production and consumption. Rice cultivation is also one of the main sources of anthropogenic methane (CH4) and nitrous oxide (N2O) emissions. The challenge of maintaining food security while reducing greenhouse gas emissions is an important tradeoff issue for both scientists and policy makers. A systematical evaluation of tradeoffs requires attention across spatial scales and over time in order to characterize the complex interactions across agricultural systems components. We couple three well-known models that capture different key agricultural processes in order to improve the tradeoff analysis. These models are the DNDC biogeochemical model of soil denitrification-decomposition processes, the DSSAT crop growth and development model for decision support and agro-technology analysis, and the regional AEZ crop productivity assessment tool based on agro-ecological analysis. The calibration of eco-physiological parameters and model evaluation used the phenology and management records of 1981–2010 at nine agro-meteorological stations spanning the major rice producing regions of China. The eco-physiological parameters were calibrated with the GLUE optimization algorithms of DSSAT and then converted to the counterparts in DNDC. The upscaling of DNDC was carried out within each cropping zone as classified by AEZ. The emissions of CH4 and N2O associated with rice production under different management scenarios were simulated with the DNDC at each site and also each 10×10km grid-cell across each cropping zone. Our results indicate that it is feasible to maintain rice yields while reducing CH4 and N2O emissions through careful management changes. Our simulations indicated that a reduction of fertilizer applications by 5–35% and the introduction of midseason drainage across the nine study sites resulted in reduced CH4 emission by 17–40% and N2O emission by 12–60%, without negative consequences on rice yield.

Winter rye as a cover crop reduces nitrate loss to subsurface drainage as simulated by HERMES

HERMES is a widely used agricultural system model; however, it has never been tested for simulating N loss to subsurface drainage. Here, we integrated a simple drain flow component into HERMES. We then compared the predictions to four years of data (2002–2005) from central Iowa fields in corn-oybean with winter rye as a cover crop (CC) and without winter rye (NCC). We also compared the HERMES predictions to the more complex Root Zone Water Quality Model (RZWQM) predictions for the same dataset. The average annual observed and simulated N loss to drain flow were 43.8 and 44.4kgN/ha (NCC) and 17.6 and 18.9kgN/ha (CC). The slightly over predicted N loss for CC was because of over predicted nitrate concentration, which may be partly caused by slightly under predicted average annual rye shoot N (observed and simulated values were 47.8 and 46.0kgN/ha). Also, recent research from the site suggests that the soil field capacity may be greater in CC while we used the same soil parameters for both treatments. A local sensitivity analysis suggests that increased field capacity affects HERMES simulations, which includes reduced drain flow nitrate concentrations, increased denitrification, and reduced drain flow volume. HERMES-simulated cumulative monthly drain flow and annual drain flow were reasonable compared to field data and HERMES performance was comparable to other published drainage model tests. Unlike the RZWQM simulations, however, the modified HERMES did not accurately simulate the year to year variability in nitrate concentration difference between NCC and CC, possibly due in part to the lack of partial mixing and displacement of the soil solution. The results suggest that 1) the relatively simple model HERMES is a promising tool to estimate annual N loss to drain flow under corn-soybean rotations with winter rye as a cover crop and 2) soil field capacity is a critical parameter to investigate to more thoroughly understand and appropriately model denitrification and N losses to subsurface drainage.