Greenhouse gas emissions from US irrigation pumping and implications for climate-smart irrigation policy.

Irrigation reduces crop vulnerability to drought and heat stress and thus is a promising climate change adaptation strategy. However, irrigation also produces greenhouse gas emissions through pump energy use. To assess potential conflicts between adaptive irrigation expansion and agricultural emissions mitigation efforts, we calculated county-level emissions from irrigation energy use in the US using fuel expenditures, prices, and emissions factors. Irrigation pump energy use produced 12.6 million metric tonnes CO2e in the US in 2018 (90% CI: 10.4, 15.0), predominantly attributable to groundwater pumping. Groundwater reliance, irrigated area extent, water demand, fuel choice, and electrical grid emissions intensity drove spatial heterogeneity in emissions. Due to heavy reliance on electrical pumps, projected reductions in electrical grid emissions intensity are estimated to reduce pumping emissions by 46% by 2050, with further reductions possible through pump electrification. Quantification of irrigation-related emissions will enable targeted emissions reduction efforts and climate-smart irrigation expansion.

Important constraints on soil organic carbon formation efficiency in subtropical and tropical grasslands.

More than 10% of Australia's 49 M ha of grassland is considered degraded, prompting widespread interest in the management of these ecosystems to increase soil carbon (C) sequestration-with an emphasis on long-lived C storage. We know that management practices that increase plant biomass also increase C inputs to the soil, but we lack a quantitative understanding of the fate of soil C inputs into different soil organic carbon (SOC) fractions that have fundamentally different formation pathways and persistence in the soil. Our understanding of the factors that constrain SOC formation in these fractions is also limited, particularly within tropical climates. We used isotopically labelled residue (13 C) to determine the fate of residue C inputs into short-lived particulate organic matter (POM) and more persistent mineral-associated organic matter (MAOM) across a broad climatic gradient (ΔMAT 10°C) with varying soil properties. Climate was the primary driver of aboveground residue mass loss which corresponded to higher residue-derived POM formation. In contrast, MAOM formation efficiency was constrained by soil properties. The differential controls on POM and MAOM formation highlight that a targeted approach to grassland restoration is required; we must identify priority regions for improved grazing management in soils that have a relatively high silt+clay content and cation exchange capacity, with a low C saturation in the silt+clay fraction to deliver long-term SOC sequestration.

Quantification of global and national nitrogen budgets for crop production.

Input-output estimates of nitrogen on cropland are essential for improving nitrogen management and better understanding the global nitrogen cycle. Here, we compare 13 nitrogen budget datasets covering 115 countries and regions over 1961-2015. Although most datasets showed similar spatiotemporal patterns, some annual estimates varied widely among them, resulting in large ranges and uncertainty. In 2010, global medians (in TgN yr-1) and associated minimum-maximum ranges were 73 (64-84) for global harvested crop nitrogen; 161 (139-192) for total nitrogen inputs; 86 (68-97) for nitrogen surplus; and 46% (40-53%) for nitrogen use efficiency. Some of the most uncertain nitrogen budget terms by country showed ranges as large as their medians, revealing areas for improvement. A benchmark nitrogen budget dataset, derived from central tendencies of the original datasets, can be used in model comparisons and inform sustainable nitrogen management in food systems.

Global Research Alliance N2 O chamber methodology guidelines: Guidelines for gap-filling missing measurements.

Nitrous oxide (N2 O) is a potent greenhouse gas that is primarily emitted from agriculture. Sampling limitations have generally resulted in discontinuous N2 O observations over the course of any given year. The status quo for interpolating between sampling points has been to use a simple linear interpolation. This can be problematic with N2 O emissions, since they are highly variable and sampling bias around these peak emission periods can have dramatic impacts on cumulative emissions. Here, we outline five gap-filling practices: linear interpolation, generalized additive models (GAMs), autoregressive integrated moving average (ARIMA), random forest (RF), and neural networks (NNs) that have been used for gap-filling soil N2 O emissions. To facilitate the use of improved gap-filling methods, we describe the five methods and then provide strengths and challenges or weaknesses of each method so that model selection can be improved. We then outline a protocol that details data organization and selection, splitting of data into training and testing datasets, building and testing models, and reporting results. Use of advanced gap-filling methods within a standardized protocol is likely to increase transparency, improve emission estimates, reduce uncertainty, and increase capacity to quantify the impact of mitigation practices.

Rethinking sources of nitrogen to cereal crops.

Understanding how to manage N inputs to identify the practices that maximize N recovery has been an organizing principle of agronomic research. Because growth in N fertilizer inputs is expected to continue in an ongoing effort to boost crop production over coming decades, understanding how to efficiently manage recovery of fertilizer N will be important going forward. Yet synthesis of published data that has traced the fate of 15 N-labeled fertilizer shows that less than half of the N taken up by crops is derived from current-year N fertilizer. The source of the majority of N in crops is something other than current-year fertilizer and the sources are not really known. This is true for maize (only 41% of N in crops was from current-year N fertilizer), rice (32%), and small grains (37%). Recovery of organic fertilizer N (manure, green manure, compost, etc.) in crops is low (27%), though N recovery in subsequent years (10%) was greater than that for mineral fertilizers. Thus, while research on efficiency of N fertilizer use through improved rate, type, location, and timing is important, this research fails to directly address management of the majority of the N supplied to crops. It seems likely that the majority of non-fertilizer N found in crops comes from turnover of soil and crop residue N. We encourage the research community to revisit the mental model that fertilizer is a replacement for N supply from turnover of soil organic N (SON) and consider a model in which N fertilizer augments ongoing SON turnover and makes an important longer term contribution to SON maintenance and turnover. Research focused on the efficient recovery of N current-year fertilizer inputs neglects this potential role for building soil N and managing soil N turnover, which seems likely to be the most important source of crop N.

Natural climate solutions for the United States.

Limiting climate warming to <2°C requires increased mitigation efforts, including land stewardship, whose potential in the United States is poorly understood. We quantified the potential of natural climate solutions (NCS)-21 conservation, restoration, and improved land management interventions on natural and agricultural lands-to increase carbon storage and avoid greenhouse gas emissions in the United States. We found a maximum potential of 1.2 (0.9 to 1.6) Pg CO2e year-1, the equivalent of 21% of current net annual emissions of the United States. At current carbon market prices (USD 10 per Mg CO2e), 299 Tg CO2e year-1 could be achieved. NCS would also provide air and water filtration, flood control, soil health, wildlife habitat, and climate resilience benefits.

Climate change impacts on selected global rangeland ecosystem services.

Rangelands are Earth's dominant land cover and are important providers of ecosystem services. Reliance on rangelands is projected to grow, thus understanding the sensitivity of rangelands to future climates is essential. We used a new ecosystem model of moderate complexity that allows, for the first time, to quantify global changes expected in rangelands under future climates. The mean global annual net primary production (NPP) may decline by 10 g C m-2  year-1 in 2050 under Representative Concentration Pathway (RCP) 8.5, but herbaceous NPP is projected to increase slightly (i.e., average of 3 g C m-2  year-1 ). Responses vary substantially from place-to-place, with large increases in annual productivity projected in northern regions (e.g., a 21% increase in productivity in the US and Canada) and large declines in western Africa (-46% in sub-Saharan western Africa) and Australia (-17%). Soil organic carbon is projected to increase in Australia (9%), the Middle East (14%), and central Asia (16%) and decline in many African savannas (e.g., -18% in sub-Saharan western Africa). Livestock are projected to decline 7.5 to 9.6%, an economic loss of from $9.7 to $12.6 billion. Our results suggest that forage production in Africa is sensitive to changes in climate, which will have substantial impacts on the livelihoods of the more than 180 million people who raise livestock on those rangelands. Our approach and the simulation tool presented here offer considerable potential for forecasting future conditions, highlight regions of concern, and support analyses where costs and benefits of adaptations and policies may be quantified. Otherwise, the technical options and policy and enabling environment that are needed to facilitate widespread adaptation may be very difficult to elucidate.

Large soil organic carbon increase due to improved agronomic management in the North China Plain from 1980s to 2010s.

Agricultural soils are widely recognized to be capable of carbon sequestration that contributes to mitigating CO2 emissions. To better understand soil organic carbon (SOC) stock dynamics and its driving and controlling factors corresponding with a period of rapid agronomic evolution from the 1980s to the 2010s in the North China Plain (NCP), we collected data from two region-wide soil sampling campaigns (in the 1980s and 2010s) and conducted an analysis of the controlling factors using the random forest model. Between the 1980s and 2010s, environmental (i.e. soil salinity/fertility) and societal (i.e. policy/techniques) factors both contributed to adoption of new management practices (i.e. chemical fertilizer application/mechanization). Results of our work indicate that SOC stocks in the NCP croplands increased significantly, which also closely related to soil total nitrogen changes. Samples collected near the surface (0-20 cm) and deeper (20-40 cm) both increased by an average of 9.4 and 5.1 Mg C ha-1 , respectively, which are equivalent to increases of 73% and 56% compared with initial SOC stocks in the 1980s. The annual carbon sequestration amount in surface soils reached 10.9 Tg C year-1 , which contributed an estimated 43% of total carbon sequestration in all of China's cropland on just 27% of its area. Successful desalinization and the subsequent increases in carbon (C) inputs, induced by agricultural projects and policies intended to support crop production (i.e. reconstruction of low yield farmland, and agricultural subsidies), combined with improved cultivation practices (i.e. fertilization and straw return) since the early 1980s were the main drivers for the SOC stock increase. This study suggests that rehabilitation of NCP soils to reduce salinity and increase crop yields have also served as a pathway for substantial soil C sequestration.

Natural climate solutions.

Better stewardship of land is needed to achieve the Paris Climate Agreement goal of holding warming to below 2 °C; however, confusion persists about the specific set of land stewardship options available and their mitigation potential. To address this, we identify and quantify "natural climate solutions" (NCS): 20 conservation, restoration, and improved land management actions that increase carbon storage and/or avoid greenhouse gas emissions across global forests, wetlands, grasslands, and agricultural lands. We find that the maximum potential of NCS-when constrained by food security, fiber security, and biodiversity conservation-is 23.8 petagrams of CO2 equivalent (PgCO2e) y-1 (95% CI 20.3-37.4). This is ≥30% higher than prior estimates, which did not include the full range of options and safeguards considered here. About half of this maximum (11.3 PgCO2e y-1) represents cost-effective climate mitigation, assuming the social cost of CO2 pollution is ≥100 USD MgCO2e-1 by 2030. Natural climate solutions can provide 37% of cost-effective CO2 mitigation needed through 2030 for a >66% chance of holding warming to below 2 °C. One-third of this cost-effective NCS mitigation can be delivered at or below 10 USD MgCO2-1 Most NCS actions-if effectively implemented-also offer water filtration, flood buffering, soil health, biodiversity habitat, and enhanced climate resilience. Work remains to better constrain uncertainty of NCS mitigation estimates. Nevertheless, existing knowledge reported here provides a robust basis for immediate global action to improve ecosystem stewardship as a major solution to climate change.

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.

Towards a new generation of agricultural system data, models and knowledge products: Design and improvement.

This paper presents ideas for a new generation of agricultural system models that could meet the needs of a growing community of end-users exemplified by a set of Use Cases. We envision new data, models and knowledge products that could accelerate the innovation process that is needed to achieve the goal of achieving sustainable local, regional and global food security. We identify desirable features for models, and describe some of the potential advances that we envisage for model components and their integration. We propose an implementation strategy that would link a "pre-competitive" space for model development to a "competitive space" for knowledge product development and through private-public partnerships for new data infrastructure. Specific model improvements would be based on further testing and evaluation of existing models, the development and testing of modular model components and integration, and linkages of model integration platforms to new data management and visualization tools.

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.

Review and analysis of strengths and weaknesses of agro-ecosystem models for simulating C and N fluxes.

Biogeochemical simulation models are important tools for describing and quantifying the contribution of agricultural systems to C sequestration and GHG source/sink status. The abundance of simulation tools developed over recent decades, however, creates a difficulty because predictions from different models show large variability. Discrepancies between the conclusions of different modelling studies are often ascribed to differences in the physical and biogeochemical processes incorporated in equations of C and N cycles and their interactions. Here we review the literature to determine the state-of-the-art in modelling agricultural (crop and grassland) systems. In order to carry out this study, we selected the range of biogeochemical models used by the CN-MIP consortium of FACCE-JPI (http://www.faccejpi.com): APSIM, CERES-EGC, DayCent, DNDC, DSSAT, EPIC, PaSim, RothC and STICS. In our analysis, these models were assessed for the quality and comprehensiveness of underlying processes related to pedo-climatic conditions and management practices, but also with respect to time and space of application, and for their accuracy in multiple contexts. Overall, it emerged that there is a possible impact of ill-defined pedo-climatic conditions in the unsatisfactory performance of the models (46.2%), followed by limitations in the algorithms simulating the effects of management practices (33.1%). The multiplicity of scales in both time and space is a fundamental feature, which explains the remaining weaknesses (i.e. 20.7%). Innovative aspects have been identified for future development of C and N models. They include the explicit representation of soil microbial biomass to drive soil organic matter turnover, the effect of N shortage on SOM decomposition, the improvements related to the production and consumption of gases and an adequate simulations of gas transport in soil. On these bases, the assessment of trends and gaps in the modelling approaches currently employed to represent biogeochemical cycles in crop and grassland systems appears an essential step for future research.

Grassland management impacts on soil carbon stocks: a new synthesis.

Grassland ecosystems cover a large portion of Earths' surface and contain substantial amounts of soil organic carbon. Previous work has established that these soil carbon stocks are sensitive to management and land use changes: grazing, species composition, and mineral nutrient availability can lead to losses or gains of soil carbon. Because of the large annual carbon fluxes into and out of grassland systems, there has been growing interest in how changes in management might shift the net balance of these flows, stemming losses from degrading grasslands or managing systems to increase soil carbon stocks (i.e., carbon sequestration). A synthesis published in 2001 assembled data from hundreds of studies to document soil carbon responses to changes in management. Here we present a new synthesis that has integrated data from the hundreds of studies published after our previous work. These new data largely confirm our earlier conclusions: improved grazing management, fertilization, sowing legumes and improved grass species, irrigation, and conversion from cultivation all tend to lead to increased soil C, at rates ranging from 0.105 to more than 1 Mg C·ha-1 ·yr-1 . The new data include assessment of three new management practices: fire, silvopastoralism, and reclamation, although these studies are limited in number. The main area in which the new data are contrary to our previous synthesis is in conversion from native vegetation to grassland, where we find that across the studies the average rate of soil carbon stock change is low and not significant. The data in this synthesis confirm that improving grassland management practices and conversion from cropland to grassland improve soil carbon stocks.

Can native clonal moso bamboo encroach on adjacent natural forest without human intervention?

Native species are generally thought not to encroach on adjacent natural forest without human intervention. However, the phenomenon that native moso bamboo may encroach on surrounding natural forests by itself occurred in China. To certificate this encroaching process, we employed the transition front approach to monitor the native moso bamboo population dynamics in native Chinese fir and evergreen broadleaved forest bordering moso bamboo forest in Tianmu Mountain Nature Reserve during the period between 2005 and 2014. The results showed that the bamboo front moved toward the Chinese fir/evergreen broadleaved stand with the new bamboo produced yearly. Moso bamboo encroached at a rate of 1.28 m yr(-1) in Chinese fir forest and 1.04 m yr(-1) in evergreen broadleaved forest, and produced 533/437 new culms hm(-2) yr(-1) in the encroaching natural Chinese fir/evergreen broadleaved forest. Moso bamboo coverage was increasing while adjacent natural forest area decreasing continuously. These results indicate that native moso bamboo was encroaching adjacent natural forest gradually without human intervention. It should be considered to try to create a management regime that humans could selectively remove culms to decrease encroachment.

Effects of moso bamboo encroachment into native, broad-leaved forests on soil carbon and nitrogen pools.

Across southern China, Moso bamboo has been encroaching on most neighboring secondary broad-leaved forests and/or coniferous plantations, leading to the land cover changes that alter abiotic and biotic conditions. Little is known about how this conversion alters soil carbon (C) and nitrogen (N). We selected three sites, each with three plots arrayed along the bamboo encroachment pathway: moso bamboo forest (BF); transition zone, mixed forest plots (MF); and broad-leaved forest (BLF), and examined how bamboo encroachment affects soil organic C (SOC), soil total N, microbial biomass C (MBC), microbial biomass N (MBN), water-soluble organic C (WSOC), and water-soluble organic N (WSON) in three forests. Over nine years, moso bamboo encroachment leads to a decrease in SOC and total soil N, an increase in MBC and WSOC, and a decrease in MBN and WSON. Changes in soil C and N occurred mainly in the topsoil. We conclude that moso bamboo encroachment on broadleaved forest not only substantially altered soil C and N pools, but also changed the distribution pattern of C and N in the studied forest soils. Continued bamboo encroachment into evergreen broadleaved forests seems likely to lead to net CO2 emissions to the atmosphere as ecosystem C stocks decline.

Molecular composition of soil organic matter with land-use change along a bi-continental mean annual temperature gradient.

Soil organic matter (SOM) is critical for maintaining soil fertility and long-term agricultural sustainability. The molecular composition of SOM is likely altered due to global climate and land-use change; but rarely are these two aspects studied in tandem. Here we used molecular-level techniques to examine SOM composition along a bi-continental (from North to South America) mean annual temperature (MAT) gradient from seven native grassland/forest and cultivated/pasture sites. Biomarker methods included solvent extraction, base hydrolysis and cupric (II) oxide oxidation for the analysis of free lipids of plant and microbial origin, ester-bound lipids from cutin and suberin, and lignin-derived phenols, respectively. Solid-state 13C nuclear magnetic resonance (NMR) was used to examine the overall composition of SOM. Soil cultivation was found to increase the amount of microbial-derived compounds at warmer temperatures (up to 17% increase). The cultivated soils were characterized by much lower contributions of plant-derived SOM components compared to the native soils (up to 64% lower at the coldest site). In addition, cultivation caused an increase in lignin and cutin degradation (up to 68 and 15% increase, respectively), and an increase in the amount of suberin-derived inputs (up to 54% increase). Clear differences in the molecular composition of SOM due to soil cultivation were observed in soils of varying mineral composition and were attributed to disturbance, different vegetation inputs, soil aggregate destruction and MAT. A high organic allophanic tropical soil was characterized by its protection of carbohydrates and nitrogen-containing compounds. The conversion of native to cultivated land shows significant shifts in the degradation stage of SOM. In particular, cutin-derived compounds which are believed to be part of the stable SOM pool may undergo enhanced degradation with long-term cultivation and disruption of soil aggregates. On a per year basis, the total amount of cutin decreased only at the two forest sites that were converted to pasture, likely due to cutin degradation or to changes in vegetation and litter quality associated with land-use change. Overall, our study highlights that the implementation of different agricultural management practices enhances the degradation of recalcitrant SOM compounds that may become a source of atmospheric CO2 with increasing land-use and climate change.