Sometimes a fifth application of ammonium nitrate sulfate was made through the pivot irrigation system after a heavy rainfall event A heavy rainfall event for potato production in north Florida region is defined as a rainfall of 76 mm in 3 days or mm in 7 days [ 47 ]. Model evaluations for water and N transport in soil profile were not carried out in the present study and can found in the work of Albert [ 28 ].
We assumed that the most sensitive parameters saturation and field capacity; [ 48 ] required for calculation of soil water balance did not change over short period of time and N removal by plants represented a major portion in the overall N budget. Hence more focus was given to evaluation of plant N uptake and yield. Table 2 were adopted from work of Albert [ 28 ] to simulate water and N transport through the soil profile for the same soil as used for the current study. An additional modification to the calibrated model included initialization of soil organic matter pools through use of the CENTURY model to simulate N mineralization [ 49 — 50 ].
The model initializes the soil organic C by subtracting the supplied FOM previous crop residue from the total measured organic C to obtain the humic SOM. The SOM3 value was estimated according to Basso et al [ 49 ]. The initial conditions of the soil profile at the beginning of the simulation was set in the soil files for each pivot. The simulation start date was set at 15 November, close to the date when the farmer harvested the fall row crop information on previous crop is presented in Table 1.
The period between harvest of fall row crop and planting of spring potato was kept as fallow in all simulations for all pivots during the study period. The initial soil water content, organic N, ammonium, and nitrate-N concentrations, were set based on the estimates of soil sampling performed during fall season at harvest. Finally, model evaluations were performed on plant N accumulation and dry matter production at plant maturity to verify the reliability of the calibrated SUBSTOR-Potato model by comparing the simulation output to measured values.
Performance of model simulations goodness-of-fit and its accuracy in prediction were evaluated using statistical indicators of root mean square error RMSE , and the Wilmot index of agreement d value [ 51 — 52 ] The root mean square error RMSE [ 53 ] between observed and simulated values and d-index [ 54 ] were computed as: 2 3 Where, Pi and Oi are the predicted or simulated and observed values, respectively, n is the number of observations.
Both RMSE and Willmott d-index help in evaluating the simulation capability of the model better than a correlation coefficient r or r 2 or line [ 52 ]. Lower RMSE and a higher d value close to 1. A d value of zero indicates no predictability [ 52 ]. The model simulated value was found to lie within one standard deviation of the observed values. The comparisons between model simulated and observed values suggested a close agreement supported by statistical indices RMSE and Willmott d-index used to evaluate the accuracy of the model Figs.
The model-simulated values and observed values of the root, shoot, and tuber N concentrations were found acceptable with a high d 0. Dry tuber yields showed a good agreement between model simulated and observed values with a d value of 0. However the model over estimated aboveground dry matter for fields and during growing season Fig. The aboveground dry matter from other fields and sampling years were in good agreement with model simulated values Fig. The fact that the plants were harvested near maturity may have increased the loss of senescent leaves; hence the measured values were lower than the model simulated values.
Error bars represent one standard deviation about the average measured value. Solid line represents line. Fresh tuber yield also showed good agreement between model simulated and observed values as indicated by high d-value 0. During the growing season, the plants suffered from late blight disease and the yield was reduced resulting in inflated RMSE values. The model simulated tuber dry matter yield did not show large deviations from observed values Fig.
The model lacks the capability to account for yield losses due to disease and hence over-estimated the yield during the growing season. Unaccounted-for N in the N budget was used to approximate environmental N loading rates and comprised chiefly of leaching loss and gaseous loss of N via volatilization and denitrification. Model simulated environmental N loading rates for most fields were in good agreement with the observed values Fig. The environmental N loading rate for field in year suffered from high variability and was not fully captured by the model although model generated N budget Table 4 showed that high amounts of mineral N was left in the soil after tuber harvest.
Examination of outliers in the observed data set revealed high soil mineral N concentrations Fig. These high soil mineral N concentrations might have resulted from late application of large amounts of N kg ha -1 in the last split 4 th split that were not utilized by potato plants. During the tuber bulking phase, the potato plant slows down N uptake and starts N translocation from leaves to tubers. The presence of large amount of mineral N might have created hot spot area in the potato beds where soil sampling was carried out. This imparted greater variability to the environmental N loading rate in field during season.
The model evaluation using the data collected during the study or evaluation period — indicated that the SUBSTOR-potato model adequately simulated N concentrations in shoot, root and tuber, plant N uptake, tuber dry matter yield, above-ground dry matter and fresh tuber yield.
The model was evaluated for drainage and soil water nitrate concentrations by Albert [ 28 ]. Upon satisfactory evaluation of the model, the model estimates were used to construct the seasonal N budget for potato grown on individual fields. The purpose of developing the simulated seasonal N budget was to identify the major N loss pathways and quantify their contribution towards the environmental N loading rates so that BMPs can be targeted by the farmer to minimize those losses.
The inputs in the N budget comprised 1 N contributions from mineralization of soil organic matter and decomposition of plant residues 2 initial mineral-N present in the soil profile before planting mineral N is defined as the sum of 1 M KCl extracted ammonium-N and nitrate-N and 3 N applied through fertilizer.
The outputs in N budget comprised 1 crop N uptake 2 mineral-N left in the soil after crop harvest 3 N lost as leaching 4 gaseous loss of N via volatilization and denitrification and 5 N immobilization by soil microbes. The budget components and their model estimates are presented in Table 4. The average total input in the N budget was kg ha -1 N. Model estimation of environmental N loading rates identified N leaching as the primary loss pathway at the study site and its contribution represented an average value of kg ha -1 season -1 N.
Mineral-N left in the soil after crop harvest was also susceptible to leaching loss in event of heavy rainfall during summer fallow period and hence represented a potential for environmental N loss via leaching. The plant N uptake values simulated by the model in this study were in agreement with the measured values reported in literature. The N that leached below the root zone was no longer available to the crops and may move to local springs and river.
Nitrogen leaching loss depends on several factors such as weather, crop type, soil characteristics, topography, drainage intensity and management practices. For example, Unlu et al. Estimated amounts of N lost via leaching in the present study were in agreement with the measured values reported by most researchers for potato production systems. Gaseous losses of N via denitrification and volatilization have been reported to be smaller in magnitude in well drained soils for potato production [ 62 ]. A study by Hyatt et al. Gaseous loss of N by via denitrification and volatilization has been found to depend on several factors temperature, soil moisture and pH, soil type, fertilizer type, improper irrigation and drainage and is highly variable over space and time 62].
Liu et al. Nitrogen lost via denitrification and volatilization predicted in the current study were in agreement with the values reported by researchers in different cropping systems; nevertheless they represent a considerable area of uncertainty. Leaching was a major loss pathway for N in this system.
The source of N in leaching losses may originate from direct sources such as fertilizer referred to as direct leaching , or from indirect sources such as mineralization of soil organic matter, organic amendments or left over plant residues refereed as indirect leaching. Thus, the contribution of direct sources towards N leaching might be high in Florida sandy soils due to shallow root system of potato and poor N and water holding capacity of the sandy soil. Crop residue roots and shoots left in the field after the tuber harvest represented a significant amount of N 64 to kg ha -1 N which could create potential for indirect N leaching upon their mineralization and in absence of a subsequent N recovery cover crop.
Typically, two months of fallow period exits between harvest of tubers and planting of the fall row crop silage corn at the study site. Kraft and Stites [ 65 ] reported an average of 40 kg ha -1 plant residue N was left in the field after tuber harvest in the Wisconsin Central Sand Plain WCSP area that had the potential to mineralize rapidly and leach below the root zone even before utilization by a subsequent crop.
In another study by Bundy and Andraski [ 39 ] on irrigated sandy soils in WCSP found that N left in the crop residue after harvest was not recovered in the subsequent crop and was lost by leaching. Thus the study site contributes N to the underlying hydrosphere through direct leaching loss as well as creates a potential for indirect leaching from two main N sources 1 left over mineral N in the soil profile after crop harvest and, 2 N from mineralization of crop residue left in the field.
Further, this study site, which is marked by unconfined aquifers and eroded Hawthorne formation consisting of thin and pocketed clay mantle atop the limestone [ 30 ], makes the site vulnerable to fast loading rates of nitrate-N directly into groundwater via leaching.
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Direct leaching losses of N might occur due to poor timing or higher rates of N fertilizer application—an area that requires further investigation. Errebhi et al. Strategies to reduce nitrate leaching into groundwater from potato production in sandy soils have been reviewed by Shrestha et al. At present, there is no consensus or a BMP to manage the potato vines after application of desiccant to prevent indirect N leaching losses from the rapidly mineralizing potato vines.
Nitrogen leaching from the crop root zone and its subsequent loading to groundwater has been studied using several techniques. Meisinger and Randall [ 21 ] used simple mass balance to calculate long term potentially leachable N to groundwater. Sebilo et al. In this study we presented a model based approach to predict the fates of N in an irrigated potato production system in sandy soil. These estimates can be used as surrogates for approximating the N loading rates to the environment and potential leachable N to estimate groundwater nitrate loading rates.
Nitrogen lost through leaching after it leaves the root zone is considered an economic loss to farmers, an agronomic loss to plants and has environmental implication for the society. Further study is required to address the causes of N leaching from potato production in the MSRB and propose solutions to minimize the losses. We acknowledge that natural factors such as soil texture, heavy rainfall events or shallow root system of the potato plant cannot be altered, however management related factors of irrigation and N management must be given consideration to reduce environmental loading of N in the MSRB.
This research explored the major sources and sinks of N for potato grown under center-pivot irrigated sandy soil in a karst dominated agricultural system. Model derived environmental N loading rates were in good agreement with the observed values for the study site. A BMP must focus on controlling both direct and indirect leaching losses from such a production system. There is a need to investigate the causes of the N loss from such system and potential solutions to minimize them in order to preserve the water quality of the Suwannee River and associated springs.
The authors thank Dr. Kelly Morgan of University of Florida for his review and providing critical suggestions to improve the manuscript. Conceived and designed the experiments: RP GH. Performed the experiments: RP. Analyzed the data: RP. Browse Subject Areas? Click through the PLOS taxonomy to find articles in your field. Abstract Recent increases in nitrate concentrations in the Suwannee River and associated springs in northern Florida have raised concerns over the contributions of non-point sources. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited Data Availability: All relevant data are within the paper.
Introduction Anthropogenic nitrogen N inputs are known to have significant impacts on terrestrial and aquatic ecosystem N cycles [ 1 — 2 ]. Download: PPT. Fig 1. Cultural Practices of the Irrigated-Potato Production System The study fields were managed by the cooperating farmer including crop selection, irrigation management, nutrient management, among other production aspects e. Table 1. Crop management information for potato production at the study farm in the MSRB during the model evaluation period — Field Methods Field methods for this study focused on collection of plant and soil samples for four successive growing seasons spring spring at several locations in the study site.
Calculation of Nitrogen Mass Balance Nitrogen mass balance for each growing season was calculated by quantifying the sources or inputs and sinks or outputs of N for potato crop using a partial N budget approach see Equation 1. Model Inputs Soil Input Data. Table 3. Mean monthly solar radiation, maximum and minimum temperatures, and monthly total rainfall at the study farm for spring growing seasons to Fig 2.
Comparisons between model simulated and observed values. Fig 3. Comparisons between model simulated and observed value for aboveground dry matter accumulation for potato at harvest maturity during model evaluation period — at several locations pivots in the study farm.
Fig 4. Comparisons between model simulated and observed values for environmental nitrogen loading rates from potato production at several locations pivots in the study farm during model evaluation period — Table 4. Model simulated seasonal N budgets for potato production at the study farm during the evaluation periods — Nitrogen budgets were prepared for individual fields pivots sampled at the study farm. Fig 5. Comparisons between model simulated and observed values for soil mineral N nitrate plus ammonium-N left at harvest maturity in 0—30 cm soil depth at several locations pivots in the study farm during model evaluation period — Conclusion This research explored the major sources and sinks of N for potato grown under center-pivot irrigated sandy soil in a karst dominated agricultural system.
Acknowledgments The authors thank Dr. References 1. View Article Google Scholar 2. Environ Sci Pollut R. View Article Google Scholar 3. J Environ Qual — View Article Google Scholar 4. Micropor Mesopor Mat — View Article Google Scholar 5. View Article Google Scholar 6. Harrington D, Maddox G, Hicks R Florida springs initiative monitoring network report and recognized sources of nitrate. Katz BG, Bohlen JF Monthly variability and possible sources of nitrate in ground water beneath mixed agricultural land use, Suwannee and Lafayette counties.
Hornsby D Influences on the distribution and occurrence of nitrate-nitrogen and total phosphorus in the water resources of the Suwannee River water management district. Am Potato J — View Article Google Scholar Hall D, Risser D Effects of agricultural nutrient management on nitrogen fate and transport in Lancaster county, Pennsylvania.
Water Resour Bull 55— J Prod Agric — Agron J — Crop Sci — Oenema O, Kros H, Vries W de Approaches and uncertainties in nutrient budgets: implications for nutrient management and environmental policies. Eur J Agron 3— In: Follett RF Ed. Monteith J L, The quest for balance in crop modeling. Agronomy J — Eur J Agron — Modelling and parameterization of the soil—plant—atmosphere system: a comparison of potato growth models.
Wageningen Press, Wageningen, The Netherlands pp — Albert MA Monitoring and modeling the fate and transport of nitrate in the Vadose zone beneath a Suwannee River Basin vegetable farm. University press of Florida, Gainesville. Florida United States Department of Agriculture.
US Dept of Agriculture. Am Potato J 69— Bundy LG, Andraski TW Recovery of fertilizer nitrogen in crop residues and cover crops on an irrigated sandy soil. Soil Sci Soc Am J — J Appl Sci Res 6: — Agr Forest Meteorol: 12 : — Agr Water Manage — Potato Res — Ritchie JT A user-oriented model of the soil water balance in wheat. Ritchie JT Soil water balance and plant water stress. Understanding options for agricultural production, pp 41—54 Kluwer academic publishers, Boston.
Soil Sci Soc Am J 69— Oper Res Int J — Wallach D, Goffinet B Mean squared error of prediction in models for studying ecological and agronomic systems. Biometrics — Willmott C J Some comments on the evaluation of model performance. Bull Am Meteorol Soc — This is particularly urgent as many developing regions increase their agricultural intensity practices and increased livestock production. Such effects can be substantially altered by management practices, crop types, and interactions with other key components of agricultural systems, and so there is utility in considering how these emissions may be modulated or exacerbated in more dynamic ESM frameworks with an explicit treatment of nitrogen, and broader biogeochemical effects.
Dynamic crops and management also enable transient simulations to assess the impacts of agricultural transitions, and the efficacy of management changes to improve soil health and sequester carbon. The following sections detail efforts to incorporate dynamic crop responses and management in ESM frameworks, and highlight the potential to include agroecosystem management as a transient anthropogenic climate forcing. Many of these models utilize plant functional type PFT frameworks, which group plant species with similar phenological and physiological attributes and assigns to them functionality responsive to evolving climate conditions.
PFTs that share the same soil fraction and column respond to environmental growing conditions and can compete for resources. More generally, this approach enables simulation of a range of ecosystem dynamics relevant to regional climate processes [ Levis , ]. A more complete description of PFT attributes can be found in Levis et al. While the increasing complexity of DGVMs or, generally, LSMs with dynamic vegetation has been a major recent advance, incorporating agriculture and management into a similar framework in the context of ESMs poses additional challenges. In general, LSMs require information about plant types, distributions, growth, and lifecycle processes in order to solve for land surface energy and water balance, nutrient stocks and flows, and exchanges of these important quantities with the climate system.
Principle to this is the simulation of plant carbon assimilation, respiration, and transpiration of water. Crop PFTs are distinctive from natural vegetation in that they usually explicitly include separate key growth phases. These broadly include and are not limited to : planting, emergence, vegetative growth, grain fill in which carbon is reallocated to maximize the harvest index , maturity, and harvest.
These parameterizations generally have dependencies on temperature, and use various growing degree day or heat accumulation formulations to determine the time to and the duration of particular growth phases. Differences between LSM crop growth representations may stem from the intended spatial scale of study or from which growth parameters was obtained , the level of detail of specific crop species' simulated development e. One challenge lies specifically in the scale at which crop parameters are obtained, as a mismatch between these and ESM resolutions may compromise simulated crop growth and enhance prediction errors [ Baron et al.
Iizumi et al. By modifying a number of parameters that related rice yield and growth to regional temperature, the authors showed that those capturing major drivers of regional interannual crop yield variability were likely to display scale dependency. Such inclusions also better represent subgrid heterogeneity, and can reduce biases to enhance model performance for regional climate applications [ Lokupitiya et al.
While many growth parameters may be obtained directly from crop model formulations or from the literature, methods also exist for ascertaining important parameters that have generally required sensitivity testing to fit. Fits based on sensitivity analysis can lead to uncertainty via the parameter ranges used, and can potentially create spurious responses at more extreme environmental conditions.
As an alternative, Bilionis et al. These newly calibrated model simulations displayed improved soy productivity when compared to available sites. Such economical methods of parameter calibration will be important to future ESM crop processes and development, particularly for regions where new crops and agricultural systems are under development e. This can be helpful when trying to isolate and understand the impact of agricultural management, particularly in more uniform, industrial growing regions.
Early work by Tsvetsinskaya et al. The authors found that the resulting changes in LAI from interactive maize crop response had a statistically significant impact on the surface energy partitioning in dry years related to large reductions in maize LAI.
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These experiments were among the first to integrate a dynamic crop component into an ESM, and demonstrate the significant regional climate sensitivity that can result from agricultural modifications to the land surface. Osborne et al. As such, the model was readily adapted for use with a GCM, and the parameterizations sufficiently generic so as to enable simulation of a range of crops [ Osborne et al.
The distribution of the crops across the land surface was ascertained using environmental suitability calculations for each crop, based upon soil moisture, accumulated thermal time, and threshold temperatures. From Osborne et al. In many areas, the introduction of dynamic crops increases the temperature variability.
From Levis et al.
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The later planting date simulation was the most consistent with observed rainfall bold dashed line. Such simulations show the sensitivity of climate simulations not just to the presence of crop PFTs, but also their timing e. Lokupitiya et al. Their crop growth methods account for critical growth stages, processes such as respiration were modified to simulate harvest removals and residue returns, and methods were also included to crudely represent crop rotations. In doing so, the authors found their improved model, SiBcrop, was better able to reproduce the growing season, interannual variability associated with crop rotations and variability in carbon exchanges.
Levis et al. Distributions of the crop PFTs were specified using maps of global crop distributions where the crop coverage area was relegated to the midlatitude regions Figure 5 c. The managed and unmanaged vegetation fractions of the model's grid cells were handled separately e. This result was particularly acute when the authors introduced a later planting date for these crops, with significant differences in the simulated climate variables between using earlier and later planting dates.
The authors highlighted that accurate representation of the cropping calendars, particularly with respect to planting, is critical to best reproduce observed rainfall and climate conditions [ Levis et al. Most recently, Liu et al. However, this may require that ample data sets are available to calibrate and parameterize the crop model formulation accordingly. These integrated model frameworks can then be utilized to understand forcings and change across these domains.
For example, the BioEarth initiative presents an integrated modeling and assessment framework that utilizes varying degrees of coupling offline to fully coupled between atmospheric climate, chemistry, and meteorology , terrestrial hydrology, crop, and ecosystem processes , aquatic, and economic models with an application towards stakeholder decision support at the regional Pacific Northwest level [ Adam et al.
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Such integrated models provide a flexible framework that can vary in complexity based upon the main research questions being asked [ Adam et al. However, the quality and resolution of available observed data sets to inform these models may constrain model validation and the types of interactions that can be explored.
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Users should also consider if the data availability and fidelity of the model simulation is high enough to warrant reliable results. However, there still exists a need to assess the robustness of climate responses to agricultural management across these modeling frameworks. Despite outstanding challenges to incorporate agricultural management in ESMs, DGVMs and ecosystem models can enable better representation of carbon cycling, in part due to their incorporation of aboveground and belowground vegetative litter pools.
These litter pools decompose at varying rates to emulate the effects of active and passive carbon stores, and emissions to the atmosphere [ Bonan et al. As the ambitious Paris Climate Agreement now enters force, momentum has been building for a global initiative to build soil carbon stocks by 0. However, there exists a relative paucity of data sets to evaluate the potential of this soil carbon accumulation rate to be realized across larger spatial scales. However, the outstanding uncertainties in agricultural soil carbon sequestration and atmospheric exchanges provides an entry point for ESM development and sensitivity testing across soil conditions and management practices.
Drewniak et al. For soybean in particular, there was good agreement in the representation of seasonal carbon fluxes Figure 6 a [ Drewniak et al. In addition, the authors showed the large quantities of residue return were required to slow the decline in soil carbon stocks Figure 6 b , recovering some of what is lost through conversions from natural vegetation [ Drewniak et al.
In addition, the inclusion of nitrogenous fertilizers was found to be a limiting factor in the amount of carbon stored [ Drewniak et al. The authors also noted that any simulated soil disturbance due to cultivation will decrease soil carbon content, though how much is dependent on management practices. Such findings are consistent with observed assessments and comparisons of management practices to date: the total amount of residue and its quality largely limit soil carbon sequestration potential [ Palm et al. Indeed, agricultural inputs—fertilizer, soil amendments, and residues—are necessary considerations for quantifying soil carbon accumulation rates and fluxes in modeled environments.
From Drewniak et al. The act of cultivation leads to large decreases in soil carbon stores, where just including cropped areas did not display such sensitivity. In addition, cultivation practices which may disturb and overturn the soil, in the form of tillage and ploughing, are equally important to estimating soil carbon storage and fluxes over time and space. The authors introduced the effects of cultivation via a set of enhanced decomposition factors for litter and soil carbon pools. Overall, their simulations resulted in decreased soil carbon stores particularly in the United States Figure 6 c.
These findings are generally consistent with those of Drewniak et al. These effects are particularly acute on U. Interestingly, Levis et al. However, recent findings show agricultural crops' propensity to significantly modulate seasonal atmospheric CO 2 [ Gray et al. This is particularly true in regions where there is a paucity of management data. Rather, ESM analyses of soil carbon provide a framework for understanding the potential soil carbon fluxes given basic boundary information. However, it is possible that economic incentives and pressures may be bigger constraints on agricultural soil carbon storage than the regional biophysical limits [ Grace et al.
To facilitate better representations of biogeochemical cycling, it is critical that ESM land surface components capture important soil types, attributes, and their distributions, insofar as they are pertinent to the agricultural systems they host. Thus, the development of ESM soil representation is a parallel endeavor to representing aboveground crops and management, particularly when trying to assess the ability of agriculturally managed soils to uptake carbon [ Paustian et al. This could make building carbon stocks to sequester carbon, and mitigate climate change, more challenging.
However, if decomposition rates slow, or occur more slowly than the buildup of litter or biomass, then carbon removals become more viable. Lawrence and Slater [ ] comment that most GCMs model soil properties by using parameterizations based on mineral soils and soil texture, rather than representing organic soils and material. When incorporated, these soils retained cooler temperatures and more moisture, though not saturated at the surface, which limited soil evaporation.
This had the effect of reducing the low cloud fraction and warming summer air temperatures, improving overall model simulations. While this development is targeted toward peatlands and permafrost areas, this may have relevance to discussions for increasing soil carbon stocks to mitigate against ongoing and future climate change. The role of soil microbiology is also of prime importance to agricultural management and biogeochemical cycling, particularly for low input systems. Wieder et al. This is in contrast to more traditional methods of implicitly simulating decomposition through microbial processes.
Not accounting for these transactions may hamper estimates of biospheric carbon uptake [ Fisher et al. For example, Shi et al. The resulting simulations indicated a downregulation in global net primary production [ Shi et al. A full representation of agricultural systems cannot be complete without representation of the nitrogen cycle, accommodating for both low input and high intensity systems. Jain et al. They suggest that to most accurately capture GHG exchanges between the land surface and the climate system, dynamic nitrogen cycling could be a limiting factor and must be resolved [ Jain et al.
This is particularly important to consider as agriculture attempts to reach carbon neutrality or serve as a net sink of anthropogenic carbon. The role of nitrogen overuse or nitrogen N limitation given the region and farming system must be examined at varying scales [ Davidson and Kanter , ; Kanter et al. Efficient agricultural nitrogen cycling and uptake is also imperative to take advantage of potential CO 2 fertilization effects. However, nitrogen cycling processes are also subject to changes under changing environmental conditions and could prove to be a prime limiting factor in agricultural management for mitigation [ Stocker et al.
In general, ESMs are increasingly incorporating and improving nitrogen cycle representations, which will further aid in quantifying the agricultural nitrogen forcing entering the natural system. Some ESMs can now explicitly represent: mineralization, fixation, root uptake, atmospheric deposition, as well as denitrification and nitrogen leaching processes, along with the variety of nitrogen pools and interpool fluxes and transformations [ Dickinson et al. A comprehensive review of the nitrogen cycle features of various models can be found in Zaehle and Dalmonech [ ]. Dickinson et al. In their model, nitrogen stores and exchanges between pools were sensitive to climatic variables such as rainfall and temperature, which further affected aspects of plant growth, such as LAI [ Dickinson et al.
Acquiring reliable information about the timing and amount of nitrogen inputs can be challenging, although this can be better constrained in areas that use the most mineral fertilizer. One solution for ESMs is to prescribe a seasonal application schedule that broadly follows the amounts obtained via available data sets [ Potter et al.
For example, the CLM4. These efforts are an important initial step to including the effects of agricultural management in ESMs, particularly with respect to nutrient and organic matter input including residues. Continued developments in this vein will better constrain the role of management in agroecosystem carbon sequestration for both improved soil health and climate change mitigation. As ESMs develop better representations of agriculture, there is a need to assess the consistency or divergence in their climate responses, particularly in relation to future development trajectories.
To this end, coordinated assessments and model intercomparisons provide a mechanism to both evaluate these improved model capabilities, and bracket the range of potential responses to future scenarios of LULCC. Cramer et al. The six models showed a wide range of terrestrial carbon uptake, although most models agreed that the terrestrial biosphere was reduced in this capacity as the simulation progressed through 21st century projected conditions.
This was in part influenced by strong responses in tropical biomes [ Cramer et al. The authors suggest that the variability in magnitude and spatial patterns of carbon uptake of these natural systems warrant further investigation. This point is particularly salient for ESM development efforts, because agricultural management stands to further modify the landscape level carbon uptake potential from natural ecosystems.
These changes must be further evaluated in the context of a changing climate. These simulations compared the land cover and vegetation changes between and , with all other forcings set to modern conditions [ Pitman et al. LUCID's intercomparison revealed some important points of agreement and divergence among the modeled responses, and gave an initial indication of regional variability in climate response detailed in section 3 above.
However, these disparate implementations did also introduce a source of uncertainty among the models' results [ Pitman et al. Additionally, land cover changes that occurred between the time periods also included changes in natural ecosystems and nonagricultural land conversions. However, this intercomparison was important in highlighting a previously underdeveloped component in climate models—land surface change, particularly by way of agricultural conversions. LUCID underscored the importance of developing better methods and best practices of ESM land surface representation and intercomparison.
More recently, Brovkin et al. Accompanying these two RCPs are harmonized historical and future LULCC trajectories adapted from Integrated Assessment Modeling IAM efforts, which have integrated models of land and resource use with models of global socioeconomic development in a coordinated framework [ Van Asselen and Verburg , ; Meiyappan et al.
These land transition scenarios show increases in cultivated lands and continued conversion from natural ecosystems e. Under RCP2. This is partly due to outstanding limitations in the availability of spatial and temporal global data sets that can be used for model evaluation.
On the other hand, ESMs that represent crops as generic grasses do not explicitly resolve important biogeochemical effects, such as exchanges between carbon pools. This may have important ramifications for land cover transitions such as transitions between native forest, pasture, and intensive cropping systems and their impact on climate change [ Brovkin et al. Overall, Brovkin et al. In particular, for larger LULCC areas, there were statistically significant changes in albedo and partitioning between latent and sensible fluxes, which would contribute to the significant changes in surface air temperature found.
For these reasons, the agricultural community requested that temperature and moisture variables from the agricultural fraction of grid boxes be included as output to capture the differential impact of climate change on agricultural lands [ Ruane et al. Additionally, ESMs' relatively coarse resolution is still an outstanding limitation for agricultural representations. In addition, coordinated studies can help modeling groups leverage the needed resources and expertise for development.
HYDE 3. The provisioning and support of these data sets will enable coordinated model intercomparisons to more systematically consider complex climate interactions, particularly in rapidly developing agricultural regions.
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To realize the full utility of these model intercomparisons, and contextualize their findings and implications, more comprehensive agricultural data sets are required. These must include spatial and temporal information on crop coverage, phenology, and management conditions. However, there has been a relative paucity of reliable production and management data sets for ESM development. Many of those available—such as through the UN Food and Agriculture Organizations Statistics Database—rely on government reported production and management information. These data are generally spatially aggregated and thus eliminate needed information on spatial heterogeneity.
Even in countries with relatively advanced data collection, survey methods, and large areas of high productivity, inconsistencies in reported statistics and other data sets make model validation and process evaluation challenging [ Nazemi and Wheater , a , b ; Khan et al. As such, while there is still utility in leveraging new and evolving data sets that speak to global and regional agricultural coverage and management, there is also a need to to be wary and cognizant of their deficiencies and potential uncertainties. However, their inclusion can better inform and improve modern day ESM climate simulations, evidenced by the studies reviewed herein.
This body of work illustrates that omitted agricultural processes may be a potential source of model land surface biases. In addition, efforts toward improved remote sensing techniques for establishing crop phenology and productivity can further inform model development and contribute to benchmarking efforts [ Galford et al. For example, Galford et al. Advances have also been made using remote sensing products to determine cropped areas, intensity, and even particular crop species in regional agroecosystems.
Mondal et al. The authors found that both crops displayed a strong sensitivity to wintertime daytime temperature and monsoon onset [ Mondal et al. Khan et al. This region constitutes Pakistan's bread basket and is subject to high degree of climate variability. While the Landsat methods provided the most accurate estimates, they were constrained by much smaller scales of analysis. In contrast, a more recently available technique, a MODIS hierarchical training method, can also reliably quantify the intensity of cropping systems, or heterogeneity such as intercropping or mixed plots.
It can furthermore be applied over larger areas, making this technique potentially highly relevant for ESM applications [ Jain et al. This data collection effort coordinates across multiple remote sensing products to provide a range of resolutions for agricultural variables, including: crop mask and coverage, type, crop condition indicators, yield and biophysical variables, environmental variables pertinent to crop growth, and management [ Whitcraft et al. However, these data are required to gain a full understanding of how agriculture may impact regional environments.
To this end, Seneviratne et al. ET estimates based on mixed methods merging flux towers, remote sensing products, such as MODIS, and modeled water and energy balance, can be useful point of comparison for ESMs [ Mu et al. For example, Jung et al. Using this product, the authors identified recent global declines in land ET driven largely by soil moisture limitations in Africa and Australia [ Jung et al. Such findings may be important to constraining hydrological change in those regional agroecosystems. These deficiencies must be identified and rectified prior to these data sets' use as ESM validation and process understanding tools.
Last, there are also information deficiencies on the impacts of LULCC on soil carbon stocks, and methodological discrepancies in their measurement and quantification. This makes establishing baselines for soil carbon accumulation challenging [ Pongratz et al. When used together, these observations can lend themselves to improved ESM simulations and provide needed insight into the mechanisms by which agriculture impacts environmental and climate systems. The preceding sections provide a general review of ESMs' emerging capacities to represent agriculture and management in their land surfaces.
Table 2 summarizes the methods and resulting climates interactions for the studies summarized herein. Below, we identify several key research questions and potential lines of future inquiry and model development, emphasizing the role of coordinated efforts. However, there is convergence around a few key simulated responses. Conversions from grasses to more intensified agricultural systems in temperate regions generally lead to enhanced latent heat fluxes and reduced temperatures at the height of the growing season. This response is particularly significant when irrigation is introduced.
Thus, future ESM development work should focus on improving the representation, amount and variability, of irrigation, and include this in the suite of anthropogenic climate forcings [ Cook et al. Current agricultural biogeochemical impacts include pollution ranging from GHG emissions to coastal aquatic dead zones [ Zhang et al. The experiments discussed herein show that agricultural soil management, such tillage and residue return, can impact soil carbon stocks.
Conventional management techniques, such as high levels of tillage, reduce soil carbon and enhance carbon emissions , which can facilitate erosion over time [ Montgomery , ]. This is additionally important given the newly placed emphasis on leveraging agricultural soils to sequester anthropogenic carbon, which may require sustained management techniques e. Model development in these respects will greatly benefit from rigorous benchmarking evaluation efforts and model intercomparisons that bracket the range of simulated climate response to these forcings.
However, model intercomparisons show that varied implementation of agricultural land cover and management in ESMs lead to disparate model results, which make ascertaining and understanding robust physical responses difficult [ Pitman et al. Additionally, improved representations allow more investigation of how agriculture may amplify or detract from anthropogenic GHG forcing. An important secondary question can also be posed: to what level of detail must agricultural management be represented to capture the most robust and influential climate feedbacks and responses? As mentioned in section 7.
In this regard, coordinated benchmarking initiatives for land surface model performance will be critical to the evaluation of agricultural processes in ESMs. Luo et al. The authors also detail techniques and approaches to devising iLAMB measures useful for improving the representation of historical and current terrestrial natural ecosystems, their influence on the climate system, and projected future changes. The studies reviewed herein show that 20th century agriculture has had significant impacts on global biogeochemical and water cycling.
These impacts stem from land cover change, management choices, and even the growth attributes of modern, improved crop varieties [e. Thus, given the centrality of global agriculture as a land surface forcing, we suggest that the iLAMB efforts should include an explicit subfocus on how agricultural representations might improve model performance against critical benchmarking products and measures. There is potential scope to generalize over input information for more homogenous growing regions, such as industrialized monocultures, where management can be relatively more uniform.
However, resolving cropping and management differences, and acquiring the necessary input data, for highly heterogeneous systems—many of which lie in highly variable climate regimes such as the semiarid tropics —is more challenging. In this respect, the emerging remote sensing techniques discussed above provide a promising avenue forward. While there are many elements of this debate that cannot yet be captured by ESMs, such as the explicit impacts to faunal biodiversity and human settlements, there is scope to explore a range of other interactions and make major contributions to this line of inquiry.
This is critical to assessments of agricultural carbon sequestration, which will depend heavily on management techniques, LULCC, and climate interactions. Doing so may also help to distinguish incentives and policy regimes conducive to carbon management. To this end, ESMs should consider incorporating data sets and results associated with regional integrated assessments of food security and agricultural adaptation. These efforts have collected much information on regional cropping systems and management, and are currently developing methodologies and best practices to scale their findings.
This is particularly needed in areas where management may have strong interactions with climate processes e. These integrated assessments are also developing and evaluating potential adaptation strategies to climate change scenarios. In doing so, we may better assess their efficacy under climate change and their potential feedbacks on regional climate systems. Therefore, ESMs could be further utilized in multimodel comparisons of alternative agroecosystem management options e. This limitation could be addressed by integrated modeling efforts, discussed below, which enable interactive simulation of both biophysical and socioeconomic feedbacks in response to various production systems [ Adam et al.
Additionally, they could help identify the most biophysically effective management strategies to mitigate potential deleterious regional climate changes. For example, RCP2. These include current biofuel sources e. However, such developments are largely constrained by data availability. Where data are increasingly available, ESMs may be leveraged for more targeted assessments of commercial crops planted over large areas. To better understand the biophysical impacts, Fan et al. Such developments enable a range of regional and global climate assessments pertinent to evaluating oil palm sustainability.
These include the GHG impacts of land appropriation for oil palm, the impact of its management on nutrient and water cycling, and interactions with the regional climate system and processes [ Fan et al. Uncertainties exist in both the climate and agricultural responses e. This is largely based on available agricultural data, which often excludes the full range of observed variation.
As an alternative, coordinated sensitivity tests with these model capacities would help bracket climate responses to temporal and spatial variations in agricultural management. Such testing will help identify management components that are most impactful to regional climate systems. For example, the choice of crop and cropping system is sensitive to regional and global agricultural markets, pricing, institutional structures e. Integrated modeling could produce more comprehensive scenarios of anthropogenic emissions and global environmental change that include intersectoral feedbacks [ Di Vittorio et al.
These frameworks can also help quantify the relative roles of socioeconomic development and climate change in driving regional agricultural LULCC [ Wang et al. Thus, a critical view must be taken when evaluating and deriving meaning from the simulated results, especially if applied to stakeholder contexts.
Finally, further discussion is warranted on how, and whether, ESMs should be used to predict crop yields as a source of decision support. There is also a need to carefully consider any diminishing returns that accompany increasingly complex agricultural representations. This is particularly important given that other highly uncertain, but significantly impactful, climate system components demand further ESM development resources e. The proposed next steps in incorporating agriculture into ESMs should be to better integrate regionally representative management practices, with an emphasis on water resources used for irrigation and agroecosystem carbon cycling and nutrient management.
Current coordinated model intercomparison and benchmarking initiatives, along with ongoing data collection efforts, may be leveraged to help facilitate development of these capacities. Finally, improving and coordinating agricultural representation in ESMs will enable sensitivity testing for a range of management conditions and adaptation options. These new ESM capacities will advance our understanding on how various agricultural management and landuse practices affect critical ecosystem services, climate feedbacks, and can potentially contribute to climate change mitigation efforts.
All data for this paper are properly cited and referred to in the reference list.