The neglected roles of adjacent natural ecosystems in maintaining bacterial diversity in agroecosystems.

A central aim of community ecology is to understand how local species diversity is shaped. Agricultural activities are reshaping and filtering soil biodiversity and communities; however, ecological processes that structure agricultural communities have often overlooked the role of the regional species pool, mainly owing to the lack of large datasets across several regions. Here, we conducted a soil survey of 941 plots of agricultural and adjacent natural ecosystems (e.g., forest, wetland, grassland, and desert) in 38 regions across diverse climatic and soil gradients to evaluate whether the regional species pool of soil microbes from adjacent natural ecosystems is important in shaping agricultural soil microbial diversity and completeness. Using a framework of multiscales community assembly, we revealed that the regional species pool was an important predictor of agricultural bacterial diversity and explained a unique variation that cannot be predicted by historical legacy, large-scale environmental factors, and local community assembly processes. Moreover, the species pool effects were associated with microbial dormancy potential, where taxa with higher dormancy potential exhibited stronger species pool effects. Bacterial diversity in regions with higher agricultural intensity was more influenced by species pool effects than that in regions with low intensity, indicating that the maintenance of agricultural biodiversity in high-intensity regions strongly depends on species present in the surrounding landscape. Models for community completeness indicated the positive effect of regional species pool, further implying the community unsaturation and increased potential in bacterial diversity of agricultural ecosystems. Overall, our study reveals the indubitable role of regional species pool from adjacent natural ecosystems in predicting bacterial diversity, which has useful implication for biodiversity management and conservation in agricultural systems.

Exploring trade-offs between residential and industrial functions in rural areas and their ecological impacts across transitioning agricultural systems: Evidence from the metropolitan suburbs of China.

The rapid transition of agricultural systems substantially affects residential and industrial land use systems in rural areas, often generating spatiotemporal trade-offs between residential and industrial functions and producing considerable ecological impacts, which has thus far not been well understood. We conduct an indicator-based assessment of transitioning agriculture systems, and then links the transitioning agricultural systems to trade-offs between residential and industrial functions from 2005 to 2020 by using a case study-the metropolitan suburbs of Beijing, China. Also, the associated ecological impacts of the trade-offs are characterized based on the calculation of the ecological quality index (EQI) and ecological contribution rate. The results show that trade-offs between residential and industrial functions in the metropolitan suburbs have gradually adapted to the different agricultural systems in transition, which can be characterized by increasing industrial function as well as declining residential function, together with the diversification of land use into a mixed pattern. Additionally, along with the transitioning process comes a U shape of the ecological quality curve, which indicates that relentless industrial sprawl into regions where the agricultural system has a low capacity for technology, as well as decay in rural areas attributed to a rural exodus and industrial decline in semi-subsistence agricultural areas, even cause ecological degradation. In general, trade-offs between residential and industrial functions (especially for the non-agricultural production function) in rural areas could partially and temporally generate unfavorable ecological impacts, but it seems to be a favorable phenomenon to promote ecological quality in the long term. Therefore, to achieve rural sustainable planning, it is necessary for land use management to observe the trade-offs between residential and industrial functions while avoiding negative impacts, such as low-density land use patterns, disordered land use functions, and eco-environmental deterioration. Such effective strategies can contribute to the feasible implementation of policies aiming to achieve the compatible development of liveable residences, highly efficient industrial production, and eco-friendly operations in rural areas.

Assessing the energy trap of industrial agriculture in North America and Europe: 82 balances from 1830 to 2012.

Early energy analyses of agriculture revealed that behind higher labor and land productivity of industrial farming, there was a decrease in energy returns on energy (EROI) invested, in comparison to more traditional organic agricultural systems. Studies on recent trends show that efficiency gains in production and use of inputs have again somewhat improved energy returns. However, most of these agricultural energy studies have focused only on external inputs at the crop level, concealing the important role of internal biomass flows that livestock and forestry recirculate within agroecosystems. Here, we synthesize the results of 82 farm systems in North America and Europe from 1830 to 2012 that for the first time show the changing energy profiles of agroecosystems, including livestock and forestry, with a multi-EROI approach that accounts for the energy returns on external inputs, on internal biomass reuses, and on all inputs invested. With this historical circular bioeconomic approach, we found a general trend towards much lower external returns, little or no increases in internal returns, and almost no improvement in total returns. This "energy trap" was driven by shifts towards a growing dependence of crop production on fossil-fueled external inputs, much more intensive livestock production based on feed grains, less forestry, and a structural disintegration of agroecosystem components by increasingly linear industrial farm managements. We conclude that overcoming the energy trap requires nature-based solutions to reduce current dependence on fossil-fueled external industrial inputs and increase the circularity and complexity of agroecosystems to provide healthier diets with less animal products. Supplementary Information: The online version contains supplementary material available at 10.1007/s13593-023-00925-5.

Soil multitrophic network complexity enhances the link between biodiversity and multifunctionality in agricultural systems.

Belowground biodiversity supports multiple ecosystem functions and services that humans rely on. However, there is a dearth of studies exploring the determinants of the biodiversity-ecosystem function (BEF) relationships, particularly in intensely managed agricultural ecosystems. Here, we reported significant and positive relationships between soil biodiversity of multiple organism groups and multiple ecosystem functions in 228 agricultural fields, relating to crop yield, nutrient provisioning, element cycling, and pathogen control. The relationships were influenced by the types of organisms that soil phylotypes with larger sizes or at higher trophic levels, for example, invertebrates or protist predators, appeared to exhibit weaker or no BEF relationships when compared to those with smaller sizes or at lower trophic levels, for example, archaea, bacteria, fungi, and protist phototrophs. Particularly, we highlighted the role of soil network complexity, reflected by co-occurrence patterns among multitrophic-level organisms, in enhancing the link between soil biodiversity and ecosystem functions. Our results represent a significant advance in forecasting the impacts of belowground multitrophic organisms on ecosystem functions in agricultural systems, and suggest that soil multitrophic network complexity should be considered a key factor in enhancing ecosystem productivity and sustainability under land-use intensification.

A hierarchical N-mixture model to estimate behavioral variation and a case study of Neotropical birds.

Understanding how and why animals use the environments where they occur is both foundational to behavioral ecology and essential to identify critical habitats for species conservation. However, some behaviors are more difficult to observe than others, which can bias analyses of raw observational data. To our knowledge, no method currently exists to model how animals use different environments while accounting for imperfect behavior-specific detection probability. We developed an extension of a binomial N-mixture model (hereafter the behavior N-mixture model) to estimate the probability of a given behavior occurring in a particular environment while accounting for imperfect detection. We then conducted a simulation to validate the model's ability to estimate the effects of environmental covariates on the probabilities of individuals performing different behaviors. We compared our model to a naïve model that does not account for imperfect detection, as well as a traditional N-mixture model. Finally, we applied the model to a bird observation data set in northwest Costa Rica to quantify how three species behave in forests and farms. Simulations and sensitivity analyses demonstrated that the behavior N-mixture model produced unbiased estimates of behaviors and their relationships with predictor variables (e.g., forest cover, habitat type). Importantly, the behavior N-mixture model accurately characterized uncertainty, unlike the naïve model, which often suggested erroneous effects of covariates on behaviors. When applied to field data, the behavior N-mixture model suggested that Hoffmann's woodpecker (Melanerpes hoffmanii) and Inca dove (Columbina inca) behaved differently in forested versus agricultural habitats, while turquoise-browed motmot (Eumomota superciliosa) did not. Thus, the behavior N-mixture model can help identify habitats that are essential to a species' life cycle (e.g., where individuals nest, forage) that nonbehavioral models would miss. Our model can greatly improve the appropriate use of behavioral survey data and conclusions drawn from them. In doing so, it provides a valuable path forward for assessing the conservation value of alternative habitat types.

Soil microbial community structures are shaped by agricultural systems revealing little temporal variation.

Many studies in soil microbial ecology are undertaken with a single sampling event, with the influence of temporal progression rarely being considered. Under field conditions, soil samples were taken from different agricultural systems; a sown grassland to maize rotation (MC), an intensively managed permanent grassland (INT), as well as extensively managed permanent grasslands with high (EXT_HP), low to sufficient (EXT_LP) and deficient available P (EXT_DP), six times throughout the 2017 growing season. Thus, this study aimed to determine if any differences in soil microbiome structures between both sharply contrasting (MC - INT - EXT), slightly differing (EXT_HP - EXT_DP) and quite similar (EXT_HP - EXT_LP and EXT_LP - EXT_DP) agricultural systems persist through changing growth conditions within the growing season. For both fungal and bacterial community structure, the influence of agricultural system (CV = 0.256, P < 0.001 and CV = 0.145, P < 0.01, respectively) was much greater than that of temporal progression (√CV = 0.065 and 0.042, respectively, both P < 0.001). Importantly, nearly all agricultural systems persistently harbored significantly distinct fungal community structures across each of the six sampling events (all at least P < 0.05). There were not as many pairwise differences in bacterial community structure between the agricultural systems, but some did persist (MC and EXT_HP âˆ¼ EXT_DP, all P < 0.001). Additionally, persistent indicator fungal OTUs (IndVal >0.7, P ≤ 0.05) associated to each agricultural system (except EXT_LP) were found in each of the six sampling events. These results highlight the temporal stability of pairwise differences in soil microbiome structures between established agricultural systems through changing plant growth conditions, even between those with a comparable management regime. This is a highly relevant finding in informing the sampling strategy of studies in soil microbial ecology as well as for designing efficient soil biodiversity monitoring systems.

Assessing catchment scale water quality of agri-food systems and the scope for reducing unintended consequences using spatial life cycle assessment (LCA).

Life cycle assessment is a multidisciplinary framework usually deployed to appraise the sustainability of various product or service supply-chains. Over recent decades, its use in the agri-food sector has risen sharply, and alongside this, a wide range of methodological advances have been generated. Spatial-life cycle assessment, defined in the current document as the interpretation of life cycle assessment results within a geographical nature, has not gone unexplored entirely, yet its rise as a sub-method of life cycle assessment has been rather slow relative to other avenues of research (e.g., including the nutritional sciences within life cycle assessment). With this relative methodological stagnation as a motivating factor, our paper combines a process-based model, the Catchment Systems Model, with various life cycle impact assessments (ReCiPe, Centre for Environmental Studies and Environmental Product Declaration) to propose a simple, yet effective, approach for visualising the technically feasible efficacy of various on-farm intervention strategies. As water quality was the primary focus of this study, interventions reducing acidification and eutrophication potentials of both arable and livestock farm types in the Southeast of England were considered. The study site is an area with a marked range of agricultural practices in terms of intensity. All impacts to acidification potential and eutrophication potential are reported using a functional unit of 1 ha. Percentage changes relative to baseline farm types, i.e., those without any interventions, arising from various mitigation strategies, are mapped using geographical information systems. This approach demonstrates visually how a spatially-orientated life cycle assessment could provide regional-specific information for farmers and policymakers to guide the restoration of certain waterbodies. A combination of multiple mitigation strategies was found to generate the greatest reductions in pollutant losses to water, but in terms of individual interventions, optimising farm-based machinery (acidification potential) and fertiliser application strategies (eutrophication potential) were found to have notable benefits.

Phytonanotechnology applications in modern agriculture.

With the rapidly changing global climate, the agricultural systems are confronted with more unpredictable and harsh environmental conditions than before which lead to compromised food production. Thus, to ensure safer and sustainable crop production, the use of advanced nanotechnological approaches in plants (phytonanotechnology) is of great significance. In this review, we summarize recent advances in phytonanotechnology in agricultural systems that can assist to meet ever-growing demands of food sustainability. The application of phytonanotechnology can change traditional agricultural systems, allowing the target-specific delivery of biomolecules (such as nucleotides and proteins) and cater the organized release of agrochemicals (such as pesticides and fertilizers). An amended comprehension of the communications between crops and nanoparticles (NPs) can improve the production of crops by enhancing tolerance towards environmental stresses and optimizing the utilization of nutrients. Besides, approaches like nanoliposomes, nanoemulsions, edible coatings, and other kinds of NPs offer numerous selections in the postharvest preservation of crops for minimizing food spoilage and thus establishing phtonanotechnology as a sustainable tool to architect modern agricultural practices.

How accurate are yield estimates from crop cuts? Evidence from smallholder maize farms in Ethiopia.

Agricultural statistics and applied analyses have benefitted from moving from farmer estimates of yield to crop cut based estimates, now regarded as a gold standard. However, in practice, crop cuts and other sample-based protocols vary widely in the details of their implementations and little empirical work has documented how alternative yield estimation methods perform. Here, we undertake a well-measured experiment of multiple yield estimation methods on 237 smallholder maize plots in Amhara region, Ethiopia. We compare yield from a full plot harvest with farmer assessments and with estimates from a variety of field sampling protocols: W-walk, transect, random quadrant, random octant, center quadrant, and 3 diagonal quadrants. We find that protocol choices are important: alternative protocols vary considerably in their accuracy relative to the whole plot, with absolute mean errors ranging from 23 (farmer estimates) to 10.6 (random octant). Furthermore, while most methods approximate the sample mean reasonably well, the divergence of individual measures from true plot-level values can be considerable. We find that randomly positioned quadrants outperform systematic sampling schemes: the random octant had the best accuracy and was the most cost-effective. The nature of bias is non-classical: bias is correlated with plot size as well as with plot management characteristics. In summary, our results advocate that even "gold standard" crop cut measures should be interpreted cautiously, and more empirical work should be carried out to validate and extend our conclusions.

Occurrence and human health risk assessment of pharmaceuticals and personal care products in real agricultural systems with long-term reclaimed wastewater irrigation in Beijing, China.

Reclaimed wastewater (RW) is increasingly used to irrigate agricultural land and to alleviate agricultural water shortages worldwide. This usage has resulted in concerns about soil contamination by pharmaceuticals and personal care products (PPCPs) and the human health risks associated with dietary crop intake. In this study, we systematically analysed the occurrence and accumulation of 11 PPCPs and one active metabolite in soils and various crops (cucumber, eggplant, long bean and wheat) from realistic RW irrigation fields with different irrigation histories (20, 30 and 40 years) in Beijing and evaluated the human health risks associated with the consumption of these crops. The 11 PPCPs and one active metabolite were detected at concentrations ranging from 0.67 to 22.92 ng L-1 in RW, 0.029-28.13 µg kg-1 in irrigated soil, and <0.01-28.01 µg kg-1 in crops. The concentrations of N4-acetyl-sulfamethoxazole and triclosan were higher than those of other PPCPs, with respective concentrations of 14.39-31.44 ng L-1 and 15.93-26.23 ng L-1 in RW, 10.92-23.29 µg kg-1 and 20.22-28.13 µg kg-1 in irrigated soil and 17.92-28.01 µg kg-1 and 8.92-14.91 µg kg-1 in crops. However, the estimated threshold of toxicological concern (TTC) and hazard quotient (HQ) values revealed that the concentrations of N4-acetyl-sulfamethoxazole and triclosan in crops irrigated with RW should be considered a de minimis risk to human health. The concentrations of 11 PPCPs and one active metabolite in soils and crops and the calculated fruit bioconcentration factors (BCFs) did not display obvious increases associated with the duration of RW irrigation in real agricultural systems (P > 0.05). The concentrations of the studied PPCPs in the RW used for irrigation followed different patterns from the concentrations detected in the irrigated soils and crops. Although the concentrations of sulfamethoxazole, sulfisoxazole, sulfamethazine and trimethoprim in RW were higher than those of many other studied PPCPs, their respective values in the irrigated soils and crops did not display a similar tendency. The uptake and accumulation of PPCPs varied among the crop species (P < 0.05). Although PPCPs were detected in eggplant, long bean and wheat (BCFs: not applicable-1.67, 0.03-1.35 and 0.01-5.01, respectively), PPCPs accumulated at increased levels in cucumber (BCFs 0.03-18.98). The estimated TTC and HQ values showed that the consumption of crops irrigated long-term with RW presents a de minimis risk to human health. However, further studies with more PPCPs and additional crop species need to be conducted, the synergistic effects of chemical mixtures of multiple PPCPs and the toxic effects of PPCP metabolites should be elucidated to obtain more reliable information on the safety of wastewater reuse for irrigation.

Global ammonia emissions from synthetic nitrogen fertilizer applications in agricultural systems: Empirical and process-based estimates and uncertainty.

Excessive ammonia (NH3 ) emitted from nitrogen (N) fertilizer applications in global croplands plays an important role in atmospheric aerosol production, resulting in visibility reduction and regional haze. However, large uncertainty exists in the estimates of NH3 emissions from global and regional croplands, which utilize different data and methods. In this study, we have coupled a process-based Dynamic Land Ecosystem Model (DLEM) with the bidirectional NH3 exchange module in the Community Multiscale Air-Quality (CMAQ) model (DLEM-Bi-NH3 ) to quantify NH3 emissions at the global and regional scale, and crop-specific NH3 emissions globally at a spatial resolution of 0.5° × 0.5° during 1961-2010. Results indicate that global NH3 emissions from N fertilizer use have increased from 1.9 ± 0.03 to 16.7 ± 0.5 Tg N/year between 1961 and 2010. The annual increase of NH3 emissions shows large spatial variations across the global land surface. Southern Asia, including China and India, has accounted for more than 50% of total global NH3 emissions since the 1980s, followed by North America and Europe. Rice cultivation has been the largest contributor to total global NH3 emissions since the 1990s, followed by corn and wheat. In addition, results show that empirical methods without considering environmental factors (constant emission factor in the IPCC Tier 1 guideline) could underestimate NH3 emissions in context of climate change, with the highest difference (i.e., 6.9 Tg N/year) occurring in 2010. This study provides a robust estimate on global and regional NH3 emissions over the past 50 years, which offers a reference for assessing air quality consequences of future nitrogen enrichment as well as nitrogen use efficiency improvement.

Trade-offs in the provisioning and stability of ecosystem services in agroecosystems.

Changes in land use generate trade-offs in the delivery of ecosystem services in agricultural landscapes. However, we know little about how the stability of ecosystem services responds to landscape composition, and what ecological mechanisms underlie these trade-offs. Here, we develop a model to investigate the dynamics of three ecosystem services in intensively managed agroecosystems, i.e., pollination-independent crop yield, crop pollination, and biodiversity. Our model reveals trade-offs and synergies imposed by landscape composition that affect not only the magnitude but also the stability of ecosystem service delivery. Trade-offs involving crop pollination are strongly affected by the degree to which crops depend on pollination and by their relative requirement for pollinator densities. We show conditions for crop production to increase with biodiversity and decreasing crop area, reconciling farmers' profitability and biodiversity conservation. Our results further suggest that, for pollination-dependent crops, management strategies that focus on maximizing yield will often overlook its stability. Given that agriculture has become more pollination-dependent over time, it is essential to understand the mechanisms driving these trade-offs to ensure food security.

On the accuracy of crop production and water requirement calculations: Process-based crop modeling at daily, semi-weekly, and weekly time steps for integrated assessments.

Integrated models are crucial for evaluation of the complex interactions and trade-offs among policy choices and socioeconomic, technical, and environmental processes. The use of process-based crop models as components of integrated models offers the possibility of significantly improving such analyses; however, challenges exist in terms of simulation scales and degree of integration. Therefore, this study evaluates the applicability of coarser-than-daily simulation time steps to simulate long-term crop yields in integrated models, and the impacts of aggregated weather input data on yields for a water-driven crop-process model based on the FAO AquaCrop model. We ran simulations at daily, semi-weekly, and weekly time steps in conjunction with coarser temporal resolution (weekly) weather input data for three crops in four locations over ten years to represent a range of crops and growing environments. Simulation results were compared to a reference case from AquaCrop using daily time step with daily weather data. Model skill for simulating crop biomass and yield and water demands was assessed statistically for each of these four hypothetical farms. Visual representations were also used to compare simulated soil moisture, crop canopy, and actual evapotranspiration values. Weekly climate data led to overestimation of crop biomass and yield regardless of the time step used. High agreements and low bias errors were realized for crop production and water estimates at daily and semi-weekly time steps, whereas weekly simulations showed poorer performance. Longer time steps intensified the impacts of weather input data aggregation, and overestimation became more pronounced with increases in time step length. The findings have important implications for integrated assessments that couple crop models with other socioeconomic, environmental, or hydrologic models, and provide guidance for modelers involved in interdisciplinary agricultural and water resources applications, including policy assessments, evaluation of water and food security, and resource use and efficiency under climate change.

The century experiment: the first twenty years of UC Davis' Mediterranean agroecological experiment.

The Century Experiment at the Russell Ranch Sustainable Agriculture Facility at the University of California, Davis provides long-term agroecological data from row crop systems in California's Central Valley starting in 1993. The Century Experiment was initially designed to study the effects of a gradient of water and nitrogen availability on soil properties and crop performance in ten different cropping systems to measure tradeoffs and synergies between agricultural productivity and sustainability. Currently systems include 11 different cropping systems-consisting of four different crops and a cover crop mixture-and one native grass system. This paper describes the long-term core data from the Century Experiment from 1993-2014, including crop yields and biomass, crop elemental contents, aerial-photo-based Normalized Difference Vegetation Index data, soil properties, weather, chemical constituents in irrigation water, winter weed populations, and operational data including fertilizer and pesticide application amounts and dates, planting dates, planting quantity and crop variety, and harvest dates. This data set represents the only known long-term set of data characterizing food production and sustainability in irrigated and rainfed Mediterranean annual cropping systems. There are no copyright restrictions associated with the use of this dataset.

Falls From Agricultural Machinery: Risk Factors Related to Work Experience, Worked Hours, and Operators' Behavior.

Objective We investigated the risk factors for falls when egressing from agricultural tractors, analyzing the role played by worked hours, work experience, operators' behavior, and near misses. Background Many accidents occur within the agricultural sector each year. Among them, falls while dismounting the tractor represent a major source of injuries. Previous studies pointed out frequent hazardous movements and incorrect behaviors adopted by operators to exit the tractor cab. However, less is known about the determinants of such behaviors. In addition, near misses are known to be important predictors of accidents, but they have been under-investigated in the agricultural sector in general and as concerns falls in particular. Method A questionnaire assessing dismounting behaviors, previous accidents and near misses, and participants' relation with work was administered to a sample of Italian tractor operators ( n = 286). Results A mediated model showed that worked hours increase unsafe behaviors, whereas work experience decreases them. Unsafe behaviors in turn show a positive association with accidents, via the mediation of near misses. Conclusions We gave a novel contribution to the knowledge of the chain of events leading to fall accidents in the agricultural sector, which is one of the most hazardous industries. Applications Besides tractor design improvements, preventive training interventions may focus on the redesign of the actual working strategies and the adoption of engaging training methods in the use of machinery to optimize the learning of safety practices and safe behaviors.

Water quality index for agricultural systems in Northwest Uruguay.

Agricultural systems have experienced rapid expansion and intensification in the last several decades. In Uruguay, since the beginning of 2000, the most common cropping systems have included soybeans. Currently, this crop is expanding towards lowlands traditionally occupied by rice in rotation with pastures. However, the environmental effects of agricultural intensification and diversification are not well known. Thus, some indices have been proposed to quantify the changes in agricultural production systems and assess water quality. The main goal of this study was to develop a water quality index (WQI) to assess the impacts of the diversification of rice production systems in northwest Uruguay. The study was carried out in an agricultural basin where other summer crops have been incorporated in the rice-pasture sequence. Agriculture intensification and crop diversification indices were calculated using information provided by farmers. Water samples were collected downstream of the production area before crop sowing and after crop harvest (2008-2009 to 2010-2011 and 2016-2017 to 2017-2018). Biochemical oxygen demand, nitrates, total phosphorus, fecal coliforms, and total suspended solids were the variables that mainly explained the effects of the agricultural activities on water quality. The proposed water quality index included these unweighted variables, which allowed for the pre-sowing and post-harvest to be differentiated, as well as the degree of diversification. Therefore, the proposed WQI constitutes a tool that can be used to evaluate the water quality in an agricultural basin. Likewise, it can be used to select agricultural sequences that generate the least possible impacts on the associated water resources.

Brief history of agricultural systems modeling.

Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the "next generation" models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.

Next generation agricultural system data, models and knowledge products: Introduction.

Agricultural system models have become important tools to provide predictive and assessment capability to a growing array of decision-makers in the private and public sectors. Despite ongoing research and model improvements, many of the agricultural models today are direct descendants of research investments initially made 30-40 years ago, and many of the major advances in data, information and communication technology (ICT) of the past decade have not been fully exploited. The purpose of this Special Issue of Agricultural Systems is to lay the foundation for the next generation of agricultural systems data, models and knowledge products. The Special Issue is based on a "NextGen" study led by the Agricultural Model Intercomparison and Improvement Project (AgMIP) with support from the Bill and Melinda Gates Foundation.

Toward a new generation of agricultural system data, models, and knowledge products: State of agricultural systems science.

We review the current state of agricultural systems science, focusing in particular on the capabilities and limitations of agricultural systems models. We discuss the state of models relative to five different Use Cases spanning field, farm, landscape, regional, and global spatial scales and engaging questions in past, current, and future time periods. Contributions from multiple disciplines have made major advances relevant to a wide range of agricultural system model applications at various spatial and temporal scales. Although current agricultural systems models have features that are needed for the Use Cases, we found that all of them have limitations and need to be improved. We identified common limitations across all Use Cases, namely 1) a scarcity of data for developing, evaluating, and applying agricultural system models and 2) inadequate knowledge systems that effectively communicate model results to society. We argue that these limitations are greater obstacles to progress than gaps in conceptual theory or available methods for using system models. New initiatives on open data show promise for addressing the data problem, but there also needs to be a cultural change among agricultural researchers to ensure that data for addressing the range of Use Cases are available for future model improvements and applications. We conclude that multiple platforms and multiple models are needed for model applications for different purposes. The Use Cases provide a useful framework for considering capabilities and limitations of existing models and data.

Integrated emergy and economic evaluation of lotus-root production systems on reclaimed wetlands surrounding the Pearl River Estuary, China.

Lotus (Newnbo nucifera, Gaertn) is the most important aquatic vegetable in China, with a cultivation history of over 3000 years. The emergy, energy, material, and money flows of three lotus root cultivation modes in Wanqingsha, Nansha District, Guangzhou, China were examined using Energy Systems Language models and emergy evaluation to better understand their ecological and economic characteristics on multiple spatial and temporal scales. The natural resource foundations, economic characteristics and sustainability of these modes were evaluated and compared. The results showed that although all three modes were highly dependent on purchased emergy inputs, their potential impacts as measured by the local (ELRL) and global (ELRW) environmental loading ratios were less than 1.2 and 0.7, respectively. The lotus-fish mode was the most sustainable with its emergy index of sustainable development (EISD) 2.09 and 2.13 times that of the pure lotus and lotus-shrimp modes, respectively. All three lotus-root production modes had superior economic viability, since their Output/Input ratio ranged from 2.56 to 4.95. The results indicated that agricultural systems may have different environmental impacts and sustainability characteristics at different spatial and temporal scales, and that these impacts and characteristics can be simultaneously explored using integrated emergy and economic evaluations.