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There is a growing global emphasis on sustainable agriculture to reduce human impacts and improve delivery of Sustainable Development Goals (SDGs). With increasing investment in critical zone observatories (CZOs), it becomes important to understand how sustainable agricultural knowledge is produced, shared and used between different groups including farmers, scientists and government. To explore these issues, scientists leading the knowledge exchange (KE) component of a China‐UK CZO program studied three farming regions with contrasting geologies and varying economic levels, using a practice‐based research method. We demonstrate how additional funding for social science research allowed us to understand how farmers access and share farming knowledge through bonding, bridging and linking networks, and how this varies spatially, using interviews and survey questionnaires. Knowledge flows, barriers and opportunities for designing locally suited two‐way KE activities were identified. First, we highlight the need for a more locally, socially embedded and reflexive approach to build trust and better address pressing local environmental challenges. Second, we show how social science can usefully inform KE for collaborative, international development science, to draw on local knowledge, promote research impacts and capacity building while avoiding knowledge mismatches. Lastly, a blueprint for the design and funding of future CZOs, social‐ecological and planetary health research agendas that combine science, social science, local knowledge and KE is presented, including the need for substantive social science research to take place in addition to science research in human‐modified landscapes—enabling the CZ science to be better grounded in, informed by and useful to local communities. Plain Language Summary: There is a growing global need for sustainable agriculture to reduce human impacts on the environment. To do this, we need to have a better understanding of how sustainable agricultural knowledge is produced, shared and used between different groups including farmers, scientists and government. In this paper, we explored these issues in three farming regions in China, using a practice‐based research method where we interviewed and surveyed several 100 farmers. We found that in two of the three regions, farmers access and share farming knowledge through family networks, whilst in the third region farmers learned from a broader range of groups including scientists and government. We recommend that future science studies in stressed agricultural landscapes use a more local approach to build trust and carry out science that better addresses pressing local environmental challenges. This requires us to study people, the residents in these landscapes, using social science, alongside understanding how the landscape is functioning ecologically. Lastly, we propose a new "blueprint" for funding of future science, social‐ecological and planetary health research agendas that combine science, social science, local knowledge and knowledge exchange. This will enable environmental science to be better grounded in, informed by and useful to local communities. Key Points: A blueprint for future critical zone observatories and planetary health research is presented that combines science, social science and local knowledgeWe show how social science can usefully inform knowledge exchange (KE) for collaborative science, to build capacity while avoiding knowledge mismatchesSpatial variations in how farmers learn through bonding, bridging and linking networks exist, showing the need for a local approach to KE [ABSTRACT FROM AUTHOR]

Climate is an essential element in agricultural production, and climate change inevitably have an impact on agriculture. Assessing the economic consequences of climate change requires comprehensive assessments of the impact chain from climate to crops and the economy. In our previous study, we derived a dose‐response function to estimate the response of crop yields to climate variables through a systematic review. In this paper, a dynamic multiregional input‐output model is established to assess the economic consequences of changes in agricultural production on China's regional and sectoral levels. The results show that (1) the direct economic damage is equivalent to 1% of gross domestic product (GDP) which implies the resulting economic cascade effect (ECE) that amounts to 17.8% of China's GDP. At the end of 21st century, the ECE is −0.1% to 13.6% of GDP (negative values indicate economic gains) without considering CO2 fertilization effect, of which the ECE in the most pessimistic pathway are equivalent to the total agricultural output in China today. (2) Regional‐level results show an uneven distribution of economic impact in China, which is related to the regional economic development. The least developed region in China experiences 2.8 to 8.5 times more ECE caused by climate change than the most developed region. (3) Sector‐level results show that agriculture is still the main affected sector, but in developed regions, manufacturing and services also bear part of the ECE. Plain Language Summary: Evidence from numerous studies has confirmed the impact of climate change on agriculture. This paper assesses the economic consequences of changes in agricultural production under climate change in China. We find that the direct economic damage is equivalent to 1% of gross domestic product which implies the resulting economic cascade effect that amounts to 17.8% of total gross domestic product. The most pessimistic estimate of the economic impacts in the 2090s is equivalent to China's total agricultural output today without considering CO2 fertilization effect of climate change. The economic impacts suffered by different regions of China are related to regional economic development. The least developed region in China experiences more economic damage from climate change than the most developed region. In addition to the agricultural sector, manufacturing and services are expected to experience part of the impacts, especially in developed regions. This paper hopes to provide data support for a comprehensive understanding of climate change impacts in different regions of China by assessing the economic consequences. Key Points: The economic cascading effect of climate change is approximately 18 times larger than its direct damageThe least developed region in China experiences 2.8 to 8.5 times more economic impacts than the most developed regionThe decline in agricultural output also affects manufacturing and services through trade networks, especially in developed regions [ABSTRACT FROM AUTHOR]

Increasing demands for water, driven by population growth and socioeconomic development, environmental regulations and future climate uncertainty, are highlighting limitations on water supplies. This water‐energy‐food‐environment nexus is not confined to semiarid regions but is emerging as a key business, societal, and economic risk in humid and temperate countries, where abundant water supplies and regulation have historically coped with fluctuating demands between industry, power generation, agriculture, domestic supply, and the environment. In the United Kingdom, irrigation is supplemental to rainfall, consumptive in use, and concentrated in the driest years and most resource‐stressed catchments. This paper describes an empirical application of a mixed methods approach to integrate agriculture into a robust decision‐making framework, focusing on a water‐stressed region in England. The approach shows that competing demands between sectors can be reconciled and that potential options or portfolios compatible with multisectoral collaboration and investment can be identified. The methodological challenges in forecasting agricultural demand, defining acceptable trade‐offs, managing scale and uncertainty issues, and the importance of engendering open dialogue between stakeholders are described. The study provides valuable insights for countries where similar emergent issues regarding conflicts over water demand exist. Key Points: Agricultural sector engagement is essential in securing sustainable integrated water resources management and planning at the regional levelThere is a need for equivalence in integrated water resources management processes to reflect the required levels of reliability and value of water within different sectorsUnder conditions of increasing water scarcity, opportunities for trade-offs are likely to exist between competing sectors, particularly in catchments where agricultural demand conflicts are most acute [ABSTRACT FROM AUTHOR]

Climate change‐induced shifts in snow storage and snowmelt patterns pose risks for adverse impacts to people, the environment, and irrigated agriculture. Existing research primarily focuses on evaluating these risks to irrigated agriculture at large scales, overlooking the role of local context in shaping risk dynamics. Consequently, many "at‐risk" areas lack insight into how adaptation strategies for managing risk through water supply augmentation or water conservation vary across contexts and over time. To address this gap, we develop a comprehensive index for evaluating irrigated agriculture's risk and adaptation potential to changes in snow at local scales and apply it throughout the western US. Results confirm trends toward escalating risk for changes in snow storage and snowmelt patterns over the century. However, substantial heterogeneity in the extent and drivers of risk exists due to variability in localized interactions between declines in water supply (approximately −9% ± 13% by 2100) and increased agricultural demand (approximately 7% ± 5% by 2100). Despite an existing focus on supply augmentation as a critical adaptation strategy to reduce risk, we show its effectiveness diminishes for many areas over time, declining to an average of −54% of historical augmentation potential by 2100. Conserving water through historical changes in crop acreage and type emerges as a more stable adaptation measure, reducing demand by 7%–8% regardless of time. While particularly relevant for higher elevation, less intensive agricultural settings in snowmelt‐dependent regions, findings underscore the need for strategies that support local‐scale, context‐appropriate adaptation to effectively manage escalating risk as snow changes. Plain Language Summary: Climate change‐induced shifts in snow storage and snowmelt patterns threaten water supplies for irrigated agriculture, necessitating adaptation. In agriculture, adaptation requires balancing water supply and demand locally, which is often overlooked in larger‐scale analyses. We develop a framework for evaluating risk and adaptation at local scales in the agricultural sector and apply it to the western US, which is broadly at risk for adverse effects (i.e., physical harm, economic losses, and socio‐cultural damage) if water demand cannot be met. Changing snow dynamics negatively impact many local areas over time, although impacts can often be reduced through supply augmentation and water conservation. While the degree of risk and the effectiveness of these adaptation strategies vary across regions and time, increasing demand and supply volatility diminishes opportunities for supply augmentation. This trend underscores the importance of supporting context‐appropriate adaptations for agricultural communities facing increased risk to their water supplies. Key Points: Changes to snow storage and release patterns pose localized and heterogeneous risks to irrigated agriculture, which accelerate in the futurePotential to manage risk from less snow by augmenting water storage is limited by water supply‐demand imbalances in many locations over timeConserving water by reducing demand could buffer irrigated agriculture against increased risk even as augmentation potential diminishes [ABSTRACT FROM AUTHOR]

Uttar Pradesh, with a population of 237 million, is the largest agrarian state in India, located in the Indo‐Gangetic plains. Rice cultivation is widespread across all districts of Uttar Pradesh, which have varying climate regimes, irrigation infrastructures, crop management practices, and farm sizes. The state is characterized by different agroecological zones (AEZs) with semi‐arid to sub‐humid climates with significant variability in monsoon rainfall. In this study, the impact of climate change on Kharif‐season rice is estimated using crop‐climate scenarios in Uttar Pradesh. A process‐based Crop Simulation Model, Crop Estimation through Resource and Environment Synthesis‐Rice, was simulated with bias‐corrected and downscaled climate data for historical (1995–2014) and three future periods (the 2030s, 2050s, and 2090s) for two mitigation pathways (SSP2‐4.5 and SSP5‐8.5) from the Coupled Model Intercomparison Project 6. Phenology, irrigation amount, crop evapotranspiration, yield, and water use efficiency were evaluated and assessed for all AEZs. Based on the ensemble of 16 climate models, rainfed rice yield increased in the AEZs of western Uttar Pradesh due to increased rainfall, while in eastern Uttar Pradesh yield decreased, under both shared socioeconomic pathways (SSPs). Irrigated rice yield decreased in all AEZs under both SSPs due to an increase in temperature and a decrease in the length of the growing period, with reductions of up to 20% by the 2090s. Irrigation requirements decreased from the 2030s to the 2090s due to increased rainfall and decreased crop evapotranspiration. Despite the projected increase in rainfed yield, the overall rice yield is expected to decrease in the future under both SSPs. Plain Language Summary: Uttar Pradesh is the most populated state in India, with most of the population working in the agriculture sector and having a low income. The state's vulnerability to climate change is high due to inadequate infrastructure and heavy dependence on agriculture. Rice is a crucial crop for the state, but this study shows that climate change will decrease rice yields in the future, especially for irrigated rice, due to higher temperatures and shorter growing seasons. While rainfed rice yields may increase in some regions due to increased rainfall, rice production is expected to decline overall. Key Points: Rice yield (combining irrigated and rainfed) in Uttar Pradesh, India, is projected to decrease in the future for SSP2‐4.5 and SSP5‐8.5With a projected increase in rainfall, rainfed rice yield increases in rainfall deficit zones, and irrigation decreases under both shared socioeconomic pathwaysPlanting in the early season could reduce the amount of yield loss for irrigated rice [ABSTRACT FROM AUTHOR]

Northeast China (NEC) is the most prominent grain‐producing region in China. However, it is currently facing significant impacts from climate change. Since the climate‐related impacts on crop yield in this region are a major concern for society in the future, quantifying climate change impacts on crop yields in NEC is essential to ensure future food security. This study aimed to quantify the effects of future climate change on crop yields in NEC and explore adaptation strategies using the Crop Growth Model (PCSE) driven by downscaled CMIP6 climate projections under four Shared Socioeconomic Pathways (SSPs) scenarios during 2015–2100. Results showed that there could be average reductions in crop yields of 21.4% for maize and 4.2% for soybean by the year 2100 under SSP585 compared to the 2015 baseline. The increasing temperature was the dominant factor in reducing yields, although elevated CO2 and precipitation offered partial compensation. The optimized planting date brought noticeable benefits for rice and soybean but had limited effects on maize due to heat stress. Relocating rice expansion eastward and implementing earlier planting increased yields by up to 50% but adversely decreased soybean and maize due to competition. This study enriches our comprehension of climate change impacts on NEC agriculture, while also quantifying potential benefits and constraints of evaluated adaptations. The proposed adaptations may help mitigate projected yield declines in other key agricultural regions across the globe. Adjusting crop management practices to capitalize on changing climate factors shows promise as a strategy for sustaining production globally. Plain Language Summary: Northeast China (NEC) is essential for achieving food accessibility and food security in China, but its agricultural production is seriously threatened by climate change. This study investigated how climate change could impact maize, rice, and soybean crop yields in this region from 2015 to 2100, using the PCSE crop model and forcing from the international Coupled Model Intercomparison Project 6 (CMIP6) climate projections. Simulation results suggested rising temperatures would negatively affect crop yields, especially for maize. Increased carbon dioxide and rainfall partially offset these losses. By 2100, average declines were projected for maize (−21%) and soybeans (−4%), respectively. Altering planting dates to match crop needs was an effective adaptation to boost yields, with rice benefiting the most. Relocating rice production eastward and expanding its area substantially increased simulated rice yields (+62%) but decreased other crops due to competition (−32% maize, −28% soybeans). Even with adaptations, some areas still showed yield declines, indicating extra measures may be needed to maintain production. Overall, this study suggests significant risks to crop yields from climate change in Northeast China, with adaptations like optimized planting and strategic crop relocation able to mitigate some but not all of the projected impacts. Key Points: Rising temperatures negatively impacted simulated future crop yields, especially for maizeAdjusting planting dates boosted yields for rice and soybeans but had limited benefits for maizeRelocating and expanding the rice area increased yields substantially [ABSTRACT FROM AUTHOR]

Meteorological drought, especially when influenced by human activities, significantly impacts agriculture. We assessed the Yangtze River Basin (YRB)'s crop ecosystem resilience during the 2022 southern China drought. Using the elasticity framework, we quantified crop Leaf Area Index (LAI) changes with meteorological factors and human activities (irrigation) in the YRB. Our study covered maize, wheat, early and late‐season rice, evaluating model accuracy with and without irrigation. Results indicate high accuracy (R2 > 0.8, p < 0.05) in simulating LAI changes, notably improved with irrigation. Irrigation emerged as the primary driver of LAI changes in the past two decades, except for solar radiation's impact on maize. In 2022, irrigation crucially influenced LAI changes, particularly in rice varieties. Early‐season rice and late‐season rice saw over 40% contribution from irrigation, while maize and wheat responded mainly to precipitation and radiation. Overall, 2022 yields remained stable, with late‐season rice showing an apparent increase. Drought timing correlated with maize, wheat, and early‐season rice yields, consistent with natural patterns. Conversely, late‐season rice exhibited the opposite pattern due to artificial irrigation, impacting 58% of the growing areas in 2022. The study offers insights into investigating farmland ecosystems responding to extreme droughts. Plain Language Summary: Drought, a natural disaster that has long impacted agriculture, has become more frequent and severe due to global warming. In China, droughts have traditionally affected the northern regions, but recent years have seen an increase in occurrence in the southern regions as well. The Southern Drought of 2022 was considered the worst drought in the Yangtze River Basin (YRB) since 1961. This study investigates the resilience of agricultural systems in the YRB during the severe drought of 2022. It examines the factors influencing crop Leaf Area Index variations and the impact of climate and human activities, such as irrigation. Findings deepen our understanding of drought effects on agriculture and highlight the crucial role of human irrigation interventions. Despite the severe drought, crop yields did not substantially decline, largely due to human interventions increasing late‐season rice yields. These findings have significant implications for sustainable agriculture. Key Points: Climate and irrigation both significantly impact crop Leaf Area Index (LAI) variations in the Yangtze River Basin (YRB), with irrigation being a major contributorIrrigation dominants and enhances agricultural resilience in the 2022 extreme drought in YRBOver 50% LAI change of rice driven primarily by irrigation, while maize and wheat are by precipitation and radiation respectively in 2022 [ABSTRACT FROM AUTHOR]

We apply a systems framework for analyzing the overall sustainability impacts of interventions to a case of the rice‐wheat cropping system of Punjab (India), where agricultural practices lead to air pollution‐related health impacts, over‐exploitation of groundwater, over‐use of fertilizers and reduced local crop diversity. We use this case to quantify how varying degrees of change in interventions result in sustainability impacts using an inclusive wealth‐based approach. We show that either improving the existing cropping system or inducing fundamental changes in the cropping system, can lead to substantial and wide‐ranging sustainability benefits. We also show that interventions that improve human health show the largest quantitative benefit due to the assumed high marginal value of human life. Accurate localized estimates of marginal values of stocks are needed for estimating overall sustainability impacts. Plain Language Summary: We use a systems‐based approach for studying air pollution as a challenge embedded in a broader network of sustainability issues, and analyze the cross‐sectoral impacts of policy interventions. We use the rice‐wheat cropping system in Punjab, India, as a case study, since agricultural practices in this system are associated with a number of inter‐linked sustainability challenges such as air pollution‐related health impacts, over‐exploitation of groundwater, over‐use of fertilizers and reduced local crop diversity. We analyze the sustainability impacts of varying degrees of policy‐induced change in this system and show that both incremental and fundamental changes can lead to wide‐ranging sustainability benefits. Key Points: We apply a systems framework for analyzing policy interventions to the rice‐wheat cropping system of Punjab (India)We quantify the sustainability impacts of interventions involving varying degrees of change in the system using an inclusive wealth‐based approachWe show how policy‐induced changes can lead to substantial and wide‐ranging sustainability benefits [ABSTRACT FROM AUTHOR]

Achieving the Sustainable Development Goals (SDGs) is challenging given the complex interactions between different SDGs and their spillover effects. We developed a system dynamics model—the Local Environmental and Socio‐Economic Model (LESEM)—to analyze and quantify context‐based SDG interactions at the local scale using a participatory model co‐design process with local stakeholders. The LESEM was developed for the Goulburn‐Murray Irrigation District in Victoria, Australia, to assist policymakers in analyzing local issues with a more integrated and holistic approach to sustainable development at the local scale. The process of participatory systems dynamics modeling facilitates integrated and strategic decision‐making and can help local policymakers identify and quantify potential trade‐offs and synergies that benefit multiple SDGs, which eventually leads local communities toward sustainability. We present an illustrative application of the model that quantifies SDG interactions across four high‐priority SDGs, namely clean water and sanitation (SDG 6), zero hunger (SDG 2), economic growth (SDG 8), and life on land (SDG 15). We illustrate the use of the model in assessing key SDG indicator trajectories under a business‐as‐usual (BAU) scenario from 2010 to 2050. Under the BAU, agri‐food production increased despite a decline in water resource availability, with gains driven by intensification and increased agricultural productivity. This boosted local prosperity and reduced the amount of agricultural land required to meet future agri‐food demand, thereby reducing pressures on terrestrial ecosystems and creating the space for ecological restoration and carbon storage in soils and biomass. However, agricultural intensification impacted water quality through increases in algal blooms and river salinity. Plain Language Summary: The 17 Sustainable Development Goals represent a comprehensive and ambitious plan to improve society, boost the economy, and protect the environment. However, it's challenging to achieve these goals because they interact with and impact one another in complex ways. To better understand these interactions, we apply a system dynamics model to analyze how different goals influence each other at a local level by involving the input of stakeholders. In this study, we focused on four critical goals: zero hunger, clean water and sanitation, economic growth, and life on land. We explored projections from 2010 to 2050 under business‐as‐usual trends. The results revealed that, even if water becomes scarcer, food production can still go up if we farm smarter and more efficiently. This is good for the local economy and means that less land is needed for farming, which helps the environment. It also creates room for nature to bounce back and for the soil and plants to store more carbon. However, agricultural intensification can adversely affect water quality. Our model supports decision‐makers in balancing multiple goals, identifying potential trade‐offs, and synergies that benefit the sustainable development of local communities. Key Points: We used a participatory approach to co‐design a systems model with stakeholders for analyzing local sustainabilityThe model quantifies the interactions among SDG 2, SDG 6, SDG 8, and SDG 15 and we applied it under a Business‐As‐Usual (BAU) futureUnder the BAU, agri‐food production increased despite less water due to intensification, but there were environmental spillover effects [ABSTRACT FROM AUTHOR]

Globally, sulfur (S) applications to croplands result in S inputs that often exceed historical atmospheric deposition. Sulfur is applied to crops as a fertilizer, fungicide, soil conditioner, pH regulator, and carrier for other elements. However, excess S in soils and aquatic ecosystems can have detrimental ecological and biogeochemical consequences, including soil base cation depletion, surface water acidification, hydrogen sulfide toxicity, and increased production of methyl mercury. The dichotomy between S benefits to crops and environmental consequences parallels that of nitrogen and phosphorus; however, there has not yet been a focus on developing sustainable S management plans in agriculture. We review the current literature on S cycling in agricultural systems and propose solutions that reduce S inputs, losses, and ecological consequences, including field applications of organic matter, adaptation of precision agriculture, and implementation of total maximum daily loads. We suggest opportunities for technological innovation, including analysis of remote sensing imagery to identify location and timing of S deficiencies and stresses, crop genetic modification to reduce S requirements, inoculation of plants with arbuscular mycorrhizal fungi to enhance plant S acquisition, and remediation of wetlands and other anoxic environments with high S loads. We conclude with areas for continued research on S biogeochemistry. Plain Language Summary: The amount of sulfur applied to croplands is increasing notably over the past few decades. Sulfur is applied for many reasons, including to meet crop nutritional demands and to regulate soil chemical conditions. However, large amounts of sulfur in the environment can have harmful impacts, as demonstrated by the consequences of acid deposition in the 1960s and 1970s. Thus, there is a need to better manage when, where, and how much sulfur is added in agriculture, similar to strategies for nitrogen and phosphorus. We propose several solutions to reduce the need for large sulfur inputs and reduce the amount of sulfur that is transported from the soil. These include applying organic matter to agricultural fields, using precision agriculture techniques, and adopting robust surface and groundwater monitoring plans. We also suggest that new innovations in technology—such as remote sensing, crop genetic modification, and wetland remediation—allow new opportunities to improve our management of sulfur. Key Points: It is critical to address optimal S applications in agricultural systems to meet crop needs while reducing environmental consequences of excess SField additions of organic matter, adaptation of precision agriculture, and robust surface and groundwater monitoring plans improve agricultural S managementPrecision agriculture using satellite imagery and crop genetic modifications may help manage crop S needs; remediation can reduce excess S [ABSTRACT FROM AUTHOR]

The concept of sustainability inherently spans multiple spatial scales, sectors, variables, and time horizons. This study links a recently developed method of assessing present‐day agricultural sustainability across environmental, economic, and social dimensions with a process‐based integrated assessment model, in order to allow forward‐looking analysis of sustainability by region and scenario. The sustainable agriculture matrix estimates present‐day agricultural sustainability at the national level using 18 indicator variables, of which this study estimates nine to the year 2100, using an enhanced version of the Global Change Analysis Model. Scenarios include a reference scenario, and scenarios that apply the following measures, both individually and in combination, that are thought to improve sustainability: yield intensification, transition toward more plant‐based ("flexitarian") diets, and economy‐wide greenhouse gas emissions mitigation. The scenarios illustrate considerable complexity and tradeoffs inherent to efforts to improve agricultural sustainability in all regions globally. For example, yield intensification typically increases nitrogen pollution, flexitarian diets can reduce agricultural output, and greenhouse gas mitigation efforts may either increase deforestation or crowd out crop and livestock production due to consequent bioenergy demands. However, there is considerable inter‐regional heterogeneity in the responses, and the importance of such secondary responses also differs by region. The analysis and post‐processing methods developed in this study allow quantification and visualization of the absolute and relative magnitude of the tradeoffs between agricultural sustainability indicator variables across regions, time periods, and scenarios. Plain Language Summary: This study links two fundamentally different approaches of assessing long‐term agricultural sustainability. The first one, the sustainable agricultural matrix (SAM), uses 18 economic, environmental, and social indicator variables, available at the national scale for recent historical years. This approach characterizes present‐day sustainability, but isn't well‐suited to long‐term assessment in the context of changes in socio‐economic, technological, and/or environmental conditions. The second approach involves a process‐based integrated assessment model, GCAM, which explicitly tracks the physical inputs and outputs of the agricultural sector, and how these interact with the other dynamically evolving systems represented in the model (energy, water, land, atmosphere, climate). Because GCAM only carries information sufficient to calculate 3 of the 18 variables in the SAM, these approaches for assessing sustainability are normally quite distinct. In this study we expand the capabilities of GCAM in order to allow analysis of 9 of the 18 SAM variables, and perform an assessment of the global and regional consequences to 2100 of realizing several ambitions thought to improve sustainability in general: reducing greenhouse gas emissions, transitioning toward predominantly plant‐based diets, and intensifying agriculture so as to reduce the areal extent of cropland globally. Key Points: The sustainable agriculture matrix is estimated to 2100 in the Global Change Analysis ModelYield intensification, dietary shift, and greenhouse gas mitigation scenarios are assessedAssessment of these tradeoffs in a consistent framework improves the quality of information for decision‐making [ABSTRACT FROM AUTHOR]

North China faces severe groundwater overdraft because of unsustainable groundwater use. However, consumers of local groundwater have rarely been precisely delineated. As such, identifying external consumers of groundwater‐intensive products may promote targeted policies from the consumption side, especially for the most water‐scarce Beijing‐Tianjin‐Hebei (BTH) metropolitan area, which has important links to food security and groundwater overdraft across North China. In this study, we revealed the prefecture city‐level virtual groundwater flows in the BTH region for, to our knowledge, the first time by compiling a nested multiregional input‐output table with 13 BTH cities and 28 Chinese provinces outside BTH in 2012. Our results showed that >50% of groundwater use in BTH cities was driven by agricultural supply for outside provinces, significantly exceeding the local demand of 38.8%. In addition, we simulated different scenarios that focused on redistributing the original agricultural production of the BTH region to other northern provinces. We found that these redistribution strategies would lead to 13%–67% groundwater savings relative to total groundwater use in the BTH region in 2012. Moreover, our results also indicated that BTH cities would save groundwater under higher stress in exchange for increased groundwater use in provinces under lower stress. These findings can be utilized to optimize the agricultural distribution and groundwater conservation policies in other regions or countries facing agriculture‐induced groundwater overdraft issues. Plain Language Summary: The local groundwater resources are influenced by distant consumers who would also lead to groundwater overdraft through interregional trade. The sources of severe groundwater overdraft in North China are not clear. Through combing multiregional input‐output (MRIO) table at city level of Beijing‐Tianjin‐Hebei (BTH) region (which located in North China Plain) with the MRIO at provincial level of China, our study assessed the virtual groundwater flows for cities of BTH. Our results show that groundwater overdraft of BTH is driven by distant consumption of agricultural products from other provinces. To mitigate this trade‐driven groundwater overdraft, we designed scenarios to show that importing or self‐market of agricultural products rather than export would get significant groundwater saving. In addition, adopting this virtual water strategy would decrease groundwater use of BTH with higher stress and increase groundwater use with lower stress in other provinces. Restricting export of agricultural products from BTH would be great helpful for mitigating groundwater overdraft of this region and bring about little harm to other provinces. Key Points: The groundwater scarcity of cities in Northern China is mainly trade‐driven and 51.1% of groundwater use is due to interprovincial exportsAgricultural relocation can significantly mitigate groundwater scarcity of the North cities with little harm for other regions [ABSTRACT FROM AUTHOR]

This commentary discusses new advances in the predictability of east African rains and highlights the potential for improved early warning systems (EWS), humanitarian relief efforts, and agricultural decision‐making. Following an unprecedented sequence of five droughts, 23 million east Africans faced starvation in 2022, requiring >$2 billion in aid. Here, we update climate attribution studies showing that these droughts resulted from an interaction of climate change and La Niña. Then we describe, for the first time, how attribution‐based insights can be combined with the latest dynamical models to predict droughts at 8‐month lead‐times. We then discuss behavioral and social barriers to forecast use, and review literature examining how EWS might (or might not) enhance agro‐pastoral advisories and humanitarian interventions. Finally, in reference to the new World Meteorological Organization "Early Warning for All" Executive Action Plan, we conclude with a set of recommendations supporting actionable and authoritative climate services. Trust, urgency, and accuracy can help overcome barriers created by limitedfunding, uncertain tradeoffs, and inertia. Understanding how climate change is producing predictable climate extremes now, investing in African‐led EWS, and building better links between EWS and agricultural development efforts can support long‐term adaptation, reducing chronic needs for billions of dollars in reactive assistance. In Africa and beyond, climate change brings increasingly extreme sea surface temperature (SST) gradients. Using climate models, we can often see these extremes coming. Prediction, therefore, offers opportunities for proactive risk management and improved advisory services, if we can create effective societal linkages via cross‐silo collaborations. Plain Language Summary: Eastern East Africa is extremely food insecure. Millions of farmers and pastoralists rely on two meager rainy seasons that arrive twice a year. In the 13 seasons since late 2016, the region experienced eight droughts and three exceptionally wet seasons. Seven droughts were linked to very strong Pacific sea surface temperature (SST) gradients, which arose through an interaction between climate change and La Niña. Climate change will bring more extreme Pacific and Indian Ocean SST gradients. Here, for the first time, we show that these gradients can be very well predicted by the current generation of climate models. We then discuss how such information might be used to inform risk management, agriculture, and livestock management practices. The IGAD Climate Predictions and Applications Center, Ethiopian and Kenyan meteorological agencies, and other groups are providing increasingly accurate climate information. This creates opportunities for more proactive and effective agricultural and pastoral advisory services. Trust, urgency and accuracy can lower uncertainty, reduce risk aversion, and empower poor households and cash‐strapped institutions to act and innovate. Investing now in collaborative African climate systems, participatory advisory services and proactive risk management will help counter these threatening climate extremes. Key Points: Climate change and La Niña are producing extreme Pacific sea surface temperature (SST) gradients, which can be predicted very far in advanceThese Pacific SST forecasts provide robust opportunities for predicting well wet and dry outcomes in East AfricaTrust, urgency and accuracy can overcome barriers due to limited funding, uncertain tradeoffs, and inertia by improving advisory services [ABSTRACT FROM AUTHOR]

Cover crops (CCs) can improve soil nutrient retention and crop production while providing climate change mitigation co‐benefits. However, quantifying these ecosystem services across global agricultural lands remains inadequate. Here, we assess how the use of herbaceous CCs with and without biological nitrogen (N) fixation affects agricultural soil carbon stocks, N leaching, and crop yields, using the dynamic global vegetation model LPJ‐GUESS. The model performance is evaluated with observations from worldwide field trials and modeled output further compared against previously published large‐scale estimates. LPJ‐GUESS broadly captures the enhanced soil carbon, reduced N leaching, and yield changes that are observed in the field. Globally, we found that combining N‐fixing CCs with no‐tillage technique could potentially increase soil carbon levels by 7% (+0.32 Pg C yr−1 in global croplands) while reducing N leaching loss by 41% (−7.3 Tg N yr−1) compared with fallow controls after 36 years of simulation since 2015. This integrated practice is accompanied by a 2% of increase in total crop production (+37 million tonnes yr−1 including wheat, maize, rice, and soybean) in the last decade of the simulation. The identified effects of CCs on crop productivity vary widely among main crop types and N fertilizer applications, with small yield changes found in soybean systems and highly fertilized agricultural soils. Our results demonstrate the possibility of conservation agriculture when targeting long‐term environmental sustainability without compromising crop production in global croplands. Plain Language Summary: Increasing crop productivity while maintaining a healthy environment is a major challenge for global agriculture. Cover crops (CCs), mostly grown during the fallow period and plowed into in soils, are expected to improve soil fertility and crop yields while reducing chemical fertilizer use, but their overall impacts on global croplands remain unknown. This study investigates the long‐term influence of cover cropping on three ecosystem service indicators across four dominant farming systems (wheat, maize, rice, and soybean) using an ecological model. We find that adoption of CCs can enhance soil carbon stocks, which would contribute to slowing climate change, and benefit environments through reducing nitrogen pollution to water bodies. Among the modeled cover crop species, legumes show higher potential in increasing cash crop yields than non‐legumes, but the effect is highly dependent on the crop rotation, chemical fertilizer rate, and management duration. Our results highlight that proper implementation of legume CCs can support food security and environmental sustainability in global agricultural ecosystems. Key Points: Cover crops (CCs) can increase soil carbon sequestration by 0.11–0.15 Pg C per year while reducing N leaching by 34%–41% in global croplands compared with fallow managementThe influence of CCs on cash crop yields varies widely among crop rotations, climates, management duration, and N fertilizer applicationsLegume CCs in no‐tillage system is overall identified as a promising practice to achieve environmental sustainability without compromising crop production in agricultural ecosystems [ABSTRACT FROM AUTHOR]

Simulations of crop yield due to climate change vary widely between models, locations, species, management strategies, and Representative Concentration Pathways (RCPs). To understand how climate and adaptation affects yield change, we developed a meta‐model based on 8703 site‐level process‐model simulations of yield with different future adaptation strategies and climate scenarios for maize, rice, wheat and soybean. We tested 10 statistical models, including some machine learning models, to predict the percentage change in projected future yield relative to the baseline period (2000–2010) as a function of explanatory variables related to adaptation strategy and climate change. We used the best model to produce global maps of yield change for the RCP4.5 scenario and identify the most influential variables affecting yield change using Shapley additive explanations. For most locations, adaptation was the most influential factor determining the projected yield change for maize, rice and wheat. Without adaptation under RCP4.5, all crops are expected to experience average global yield losses of 6%–21%. Adaptation alleviates this average projected loss by 1–13 percentage points. Maize was most responsive to adaptive practices with a projected mean yield loss of −21% [range across locations: −63%, +3.7%] without adaptation and −7.5% [range: −46%, +13%] with adaptation. For maize and rice, irrigation method and cultivar choice were the adaptation types predicted to most prevent large yield losses, respectively. When adaptation practices are applied, some areas are predicted to experience yield gains, especially at northern high latitudes. These results reveal the critical importance of implementing adequate adaptation strategies to mitigate the impact of climate change on crop yields. Plain Language Summary: Computer simulations are commonly used to predict how crop yield may change under future climate conditions and land management practices. For four major crops (maize, rice, wheat and soybean), we tested different statistical methods to synthesize thousands of computer simulations of crop yield change under future climate into one meta‐model which can be used to predict crop yield at any location where that crop is grown. We then predicted the change in crop yield under a likely future climate scenario (Representative Concentration Pathway 4.5) and identified which variables most explained the crop yield change. Considering both adaptive management status (whether or not adaptation practices were applied) and climate factors (average temperature, change in temperature, average precipitation, change in precipitation, CO2 concentration), we found that adaptation status was the most influential factor determining the predicted yield change for most crops. Managing cropland adaptively in the future can reduce predicted yield losses by 1–13 percentage points relative to maintaining the same management practices. We discuss which types of management practices may be the most useful for different crops, as well as which areas of the world are expected to gain or lose crop yield in the future. Key Points: Under future climate scenario Representative Concentration Pathway 4.5, average global yields of maize, rice, wheat, and soybean are projected to decrease by 6%–21%Implementing adaptive management practices could reduce this loss by 1–13 percentage points relative to maintaining the same practicesSome areas of the world, such as northern high latitudes, may see future yield increases if adaptation practices are applied [ABSTRACT FROM AUTHOR]

Producing more food with less pollution and greenhouse gas emissions is a grand challenge for the 21st century. Strategies to successfully promote win‐win outcomes for both food security and environmental health are not easy to identify. Here we comprehensively assess an ecological rice‐animal co‐culture system (RAC) (e.g., rice‐fish, rice‐duck, and rice‐crayfish) through a global meta‐analysis and identify the potential benefits of global promotion. Compared to traditional monoculture of rice or animal production, the RAC can not only reduce the demand for agricultural land areas, but also increase rice yields (+4%) as well as nitrogen use efficiency of rice (+6%). At the same time, RAC reduces nitrogen losses (−16% runoff and −13% leaching) and methane emissions (−11%), except for rice‐fish coculture systems, which are likely to increase methane emissions (+29%). Furthermore, RAC increases the net income of farmers through reducing cost of fertilizer and pesticide input and achieving higher outputs with more marketable products. According to the development stage of different countries, promotion of RAC will thus realize multiple benefits and aid sustainable intensification. Plain Language Summary: How to feed the growing world population with less pollution is a grand challenge for the 21st century. Rice‐animal co‐culture systems present an ecological integrated farming approach to tackle the challenge. Through a global consensus by synthesizing evidence from existing literature, we found that rice‐animal co‐culture systems could produce more diverse food types and nutrient sources, enhance resource use efficiency and reduce methane emissions, while increasing farmers' income. Global promotion of rice‐animal coculture system has the potential to benefit sustainable development goals. Key Points: Rice‐animal co‐culture systems could produce more diverse food with higher rice yield and extra animal proteinGlobal promotion of rice‐animal co‐culture will reduce nitrogen loss and methane emissions from rice paddiesGlobal promotion of rice‐animal co‐culture will increase income of producers by 152–171 billion US dollars annually [ABSTRACT FROM AUTHOR]

Africa is a major hotspot of food insecurity with climate change and population growth as major drivers. Irrigation expansion can sustainably increase agricultural productivity and adapt crops to climate change. We use agro‐hydrological, climate, and socio‐economic models to quantify crop production with irrigation expansion and perform food security analyses for different adaptation scenarios for African countries under baseline and 3°C warmer climate conditions. We find that under a 3°C warmer climate the total food production in Africa can only feed 1.35 billion people, when the continent's population is expected to reach 3.5 billion, leaving a food deficit equivalent to 2.15 billion people. Increasing agricultural productivity with irrigation alone will not be enough to achieve food self‐sufficiency. Therefore, future food demand will likely be met by other means such as cropland expansion or greater reliance on imports which would further expose African populations to uncertainty from the volatility in global food prices. Key Points: Sustainable agricultural intensification alone will not suffice to meet future food demand in African countriesFood insufficiency can be reduced by moderating diets and implementing water storage to irrigate during dry periodsBy halving current levels of food loss and waste, an additional 130 million people can be fed in Africa through domestic food production [ABSTRACT FROM AUTHOR]

The need for explicitly considering equity in climate change adaptation planning is increasingly being recognized. However, evaluations of adaptation often adopt an aggregated perspective, while disaggregation of results is important to learn about who benefits when and where. A typical example is adaptation of rice agriculture in the Vietnam Mekong Delta (VMD). Efforts focused on flood protection have mainly benefitted large‐scale farmers while harming small‐scale farmers. To investigate the distributional consequences of adaptation policies in the VMD, we assess both aggregate total output and equity indicators, as well as disaggregated impacts in terms of district‐level farming profitability. Doing so requires an adequate representation of the multisectoral dynamics between the human and biophysical systems which influence farming profitability. We develop a spatially explicit integrated assessment model that couples inundation, sedimentation, soil fertility and nutrient dynamics, and behavioral land‐use change and farming profitability calculation. We find that inter‐district inequality responds in a non‐linear way to climatic and socio‐economic changes and choices of adaptation policies. The patterns of who wins and who loses could change substantially when a different policy is implemented or if a slightly different uncertain future materializes. We also find that there is no simple ranking of alternative adaptation policies, so one should make trade‐offs based on agreed preferences. Accounting for equity implies exploring the distribution of outcomes over different groups over a range of uncertain futures. Only by accounting for multisectoral dynamics can planners anticipate the equity consequences of adaptation and prepare additional measures to aid the worse‐off actors. Plain Language Summary: Adaptation planning should strive for not only maximizing welfare outcomes, but also ensuring equitable outcomes among affected people. Supporting equitable adaptation planning requires looking beyond only at aggregated indicators. In this study, we evaluate the distributional outcomes of alternative adaptation policies under various scenarios to the profitability of rice farming in the Vietnam Mekong Delta. To do this, we have to explicitly account for multisectoral dynamics surrounding the agriculture sector. This is because adaptation policies and uncertain factors target different parts of the systems and interact in a non‐linear way. Hence, if we zoom in to just one sector (e.g., flood impacts to farmer's profitability), we would overlook the inherent interconnectedness between the different sectors in the system. We therefore develop an integrated assessment model encompassing multiple sectors, including inundation and sedimentation dynamics, soil fertility and nutrient dynamics, and behavioral land‐use change and farming profitability. Using this model, we find that even a slight change in uncertain scenarios or implemented adaptation policies could lead to distinctive inequality patterns between farmers across the 23 districts. Our study suggests that to support an equitable adaptation planning, one must incorporate the complex multisectoral dynamics that give shape to inequalities. Key Points: Understanding who wins and who loses under different futures can help planners in anticipating and ameliorating future inequalitiesWe show how inequality patterns are sensitive to external uncertainties and adaptation policiesExploring inequality patterns requires accounting for multisectoral dynamics, which often has implications for the modeling choices [ABSTRACT FROM AUTHOR]

The United States is a world leader in the production of maize and other crops and the agricultural success of the country is directly linked to the intensive use of fertilizers and irrigation. However, even in advanced agricultural systems, soils can become degraded over time due to factors such as soil organic matter (SOM) loss and erosion. Here, we use a series of scenario‐based model analyses to show that about one‐third of current annual US. N fertilizer use in maize agriculture is used to compensate for the long‐term loss of soil fertility through erosion and organic matter loss. This leads to over a half billion dollars per year in extra fertilizer supply costs to US farmers. These results highlight the potential to reduce both the input costs and environmental impacts of agriculture through the restoration of SOM in agricultural soils. Plain Language Summary: Land degradation in US maize agriculture leads to fertilization that costs US farmers nearly a half billion dollars per year. Key Points: The key impact of land degradation in agricultural systems is reduces soil fertility which requires considerable additional fertilizersThe costs of fertilization to replace nutrients lost during degradation amounts to a half billion dollars annually in the USPractices that restore soil fertility will also reduce the costs and environmental impacts of fertilizer use [ABSTRACT FROM AUTHOR]

Abstract Climate is an essential element in agricultural production, and climate change inevitably have an impact on agriculture. Assessing the economic consequences of climate change requires comprehensive assessments of the impact chain from climate to crops and the economy. In our previous study, we derived a dose‐response function to estimate the response of crop yields to climate variables through a systematic review. In this paper, a dynamic multiregional input‐output model is established to assess the economic consequences of changes in agricultural production on China's regional and sectoral levels. The results show that (1) the direct economic damage is equivalent to 1% of gross domestic product (GDP) which implies the resulting economic cascade effect (ECE) that amounts to 17.8% of China's GDP. At the end of 21st century, the ECE is −0.1% to 13.6% of GDP (negative values indicate economic gains) without considering CO2 fertilization effect, of which the ECE in the most pessimistic pathway are equivalent to the total agricultural output in China today. (2) Regional‐level results show an uneven distribution of economic impact in China, which is related to the regional economic development. The least developed region in China experiences 2.8 to 8.5 times more ECE caused by climate change than the most developed region. (3) Sector‐level results show that agriculture is still the main affected sector, but in developed regions, manufacturing and services also bear part of the ECE.

Reducing nitrous oxide (N2O) emissions from agriculture is critical to limiting future global warming. In response, a growing number of food retailers and manufacturers have committed to reducing N2O emissions from their vast networks of farmer suppliers by providing technical assistance and financial incentives. A key challenge for such companies is demonstrating that their efforts are leading to meaningful progress toward their climate mitigation commitments. We show that a simplified version of soil surface nitrogen (N) balance—or partial N balance—the difference between N inputs to and outputs from a farm field (fertilizer N minus crop N), is a robust indicator of direct N2O emissions from fields with maize and other major rainfed temperate‐region crops. Furthermore, we present a generalized environmental model that will allow food‐supply‐chain companies to translate aggregated and anonymized changes in average N balance across their supplying farms into aggregated changes in N2O emissions. This research is an important first step, based on currently available science, in helping companies demonstrate the impact of their sustainability efforts. Plain Language Summary: As a powerful greenhouse gas, nitrous oxide that is emitted from agriculture contributes to climate change. Reductions in these emissions are not only possible—they are critical to addressing climate change. Food companies and others wanting to reduce nitrous oxide emissions in their food supply chains are looking for a way to show evidence of progress. Our research shows that a simple calculation of nitrogen (N) balance in crop fields (N in fertilizer minus N in the harvested crop) can be used as an indicator of nitrous oxide emissions. At the large scale, reducing high N balances will reduce overall emissions. We demonstrate the strong relationship between N balance and nitrous oxide emissions and show how this simple model can be used at scale to bring about positive environmental change. Key Points: Nitrogen balance is a robust indicator of nitrous oxide emissions from agricultural croplandFood‐supply‐chain companies and others can scale up field‐level N balance values to measure progress toward sustainability goals over timeThe relationship between N balance and N2O emissions is nonlinear, so the greatest benefit will come from reducing high field‐scale N balances [ABSTRACT FROM AUTHOR]

Declining water tables are causing wells to run dry in California, but the prevalence and spatial distribution of wells that have run dry are not known beyond anecdotal and voluntary reports. Here, we apply a new, simple, and measurement‐driven method to calculate a localized water table; we show, for the first time using observations, that up to one‐in‐five wells now runs dry in California's Central Valley. The spatial distribution of wells identified as having dried up replicates hot spots of wells identified as having run dry in a voluntary reporting system, while also capturing impacts on groundwater wells that have not reportedly run dry. We assess the rates of drilling throughout the Central Valley and find, surprisingly, that domestic wells are being drilled deeper at a rate that exceeds agricultural wells across much of the central Central Valley. Because new groundwater wells are costly (i.e., $10,000 to $100,000), we suggest explicitly considering dry wells in groundwater sustainability planning to protect homes and farms from the loss of access to reliable water supplies in the future. Plain Language Summary: Anecdotal evidence suggests that groundwater level declines are causing wells to run dry in California's Central Valley, threatening access to reliable water supplies. Here we show with observational data that thousands of wells ran dry at the peak of the recent drought, especially in the southeastern Central Valley. We use groundwater well construction data to show that domestic water wells ran dry in disproportionate numbers, because typical domestic wells are shallower than typical agricultural wells. The results of this work can be used as water agencies are considering current and future impacts of groundwater depletion on groundwater users. Key Points: This work presents a systematic evaluation the prevalence of dry wells throughout the entire Central ValleyDomestic wells are being drilled deeper at a rate that exceeds agricultural wells across much of the central Central ValleyThe high cost of constructing new domestic, municipal, and agricultural wells suggests that dry wells are vital to consider in planning [ABSTRACT FROM AUTHOR]

This study focuses on the northward shift of homogeneous agro‐climate zones in Europe analyzed for the observed past and projected climate conditions for the next decades. Statistical cluster analysis is used to derive eight main agro‐climatic zones driven by two agro‐meteorological indicators, namely, active temperature sum and thermal growing season length. The northward shift of homogeneous agro‐climate zones and the corresponding change of crop growth suitability are analyzed together with the change of exposure of crops to temperature‐related climate extremes during the growing season. Gradual warming over Europe has contributed to a lengthening of the growing season and an increased active temperature accumulation, accompanied by more frequent occurrence of warm extreme climate events. Using a set of five high‐resolution regional climate scenarios, we calculate that a major part of Europe will be affected by further northward climate zone migration. In the next decades, the migration of agro‐climatic zones in Eastern Europe may reach twice the velocity observed during the period 1975–2016. Several regions of the Mediterranean may lose suitability to grow specific crops in favor of northern European regions. This indicator‐based assessment suggests that the potential advantages of the lengthening of the thermal growing season in northern and eastern Europe are often outbalanced by the risk of late frost and increased risk of early spring and summer heat waves. Key Points: Agro‐climate zones in eastern Europe have experienced a northward migration velocity of 100 km per 10 years over the past 40 yearsNorthward migration of agro‐climate zones in Europe may be up to two times faster in next decadesNegative impacts of heat stress are expected to nonlinearly increase in large parts of southern and southeastern Europe [ABSTRACT FROM AUTHOR]

Temperature and precipitation are the most important factors responsible for agricultural productivity variations. In 2018 spring/summer growing season, Europe experienced concurrent anomalies of both. Drought conditions in central and northern Europe caused yield reductions up to 50% for the main crops, yet wet conditions in southern Europe saw yield gains up to 34%, both with respect to the previous 5‐year mean. Based on the analysis of documentary and natural proxy‐based seasonal paleoclimate reconstructions for the past half millennium, we show that the 2018 combination of climatic anomalies in Europe was unique. The water seesaw, a marked dipole of negative water anomalies in central Europe and positive ones in southern Europe, distinguished 2018 from the five previous similar droughts since 1976. Model simulations reproduce the 2018 European water seesaw in only 4 years out of 875 years in historical runs and projections. Future projections under the RCP8.5 scenario show that 2018‐like temperature and rainfall conditions, favorable to crop growth, will occur less frequent in southern Europe. In contrast, in central Europe high‐end emission scenario climate projections show that droughts as intense as 2018 could become a common occurrence as early as 2043. While integrated European and global agricultural markets limited agro‐economic shocks caused by 2018's extremes, there is an urgent need for adaptation strategies for European agriculture to consider futures without the benefits of any water seesaw. Key Points: Unique concurrent spring and summer climatic anomalies affected Europe in 20182018‐like droughts could become a common occurrence as early as 2043Climate change adaptation strategies for agriculture in Europe cannot count on recurrent water seesaws [ABSTRACT FROM AUTHOR]

Understanding the dynamics of salt movement in the soil is a prerequisite for devising appropriate management strategies for land productivity of coastal regions, especially low-lying delta regions, which support many millions of farmers around the world. At present, there are no numerical models able to resolve soil salinity at regional scale and at daily time steps. In this research, we develop a novel holistic approach to simulate soil salinization comprising an emulator-based soil salt and water balance calculated at daily time steps. The method is demonstrated for the agriculture areas of coastal Bangladesh (∼20,000 km2). This shows that we can reproduce the dynamics of soil salinity under multiple land uses, including rice crops, combined shrimp and rice farming, as well as non-rice crops. The model also reproduced well the observed spatial soil salinity for the year 2009. Using this approach, we have projected the soil salinity for three different climate ensembles, including relative sea-level rise for the year 2050. Projected soil salinity changes are significantly smaller than other reported projections. The results suggest that inter-season weather variability is a key driver of salinization of agriculture soils at coastal Bangladesh. [ABSTRACT FROM AUTHOR]

Historical examples of demographic change, in China, Italy, Nigeria, Utah, Easter Island, and elsewhere, together with simple mathematics and biological principles, show that stabilizing world population before it is limited by food supply will be more difficult than is generally appreciated. United Nations population projections are wrong because they assume, in spite of the absence of necessary feedbacks, that all nations will converge rapidly to replacement-level fertility and thereafter remain at that level. Education of women and provision of contraceptives have caused dramatic reductions in fertility, but many groups, including some that are well-educated, maintain high fertility. Small groups with persistent high fertility can grow to supplant low-fertility groups, resulting in continued growth of the total population. The global average fertility rate could rise even if each country's fertility rate is falling. In some low-fertility European countries where deaths exceed births, the population continues to grow because of immigration. Producing more than two offspring is normal for all animal species with stable populations because their populations are limited by resources or predation rather than birth control. It may therefore be appropriate to view the growth of human population as the result not of excess fertility but rather of excess food. [ABSTRACT FROM AUTHOR]