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Recent earlier greening trends are believed to enhance terrestrial ecosystem productivity. However, advanced onset of growing season may also deplete soil water in early spring, leading to summer water stress for plant growth. In this study, we linked soil moisture with start of growing season (SGS, represented by day of year) to examine the responses of ecosystem productivity to water stress during 1982–2011 on the Mongolian Plateau. Results showed that, though not significant, earlier SGS has tendency to enhance spring productivity at north part of the study area. Nonetheless, we observed that suppressed summer photosynthesis due to phenology-induced water stress dramatically reduced annual carbon assimilation. Thus, phenology-associated changes in soil moisture have profound potential in regulating seasonal and annual productivity in arid and semi-arid ecosystems. The relationship between SGS and GPP was not observed in more mesic forest ecosystems (R 2 = 0.04, p > 0.10) nor in the agricultural area (R 2 = 0.03, p > 0.10) where practices like irrigation aim to alleviate summer water stress. Therefore, at the scale of the entire study area, earlier growing season did not translate to higher productivity (R 2 = 0.006). On the contrary, advanced SGS aggravated growing-season water stress, which in turn, supressed annual carbon assimilation in water limited area. This mechanism implies the advanced greening trends may not necessarily lead to more carbon uptake in terrestrial ecosystems but rather a carbon loss, especially in the arid and semi-arid regions. [ABSTRACT FROM AUTHOR]

• We synthesized county-level crop N budget and climate stress data during 1981-2016. • The U.S. corn N surplus dynamics are strongly regulated by climatic stresses. • A 51% increase in U.S. corn N surplus was associated with extremely hot conditions. • N surplus increases of 47% (dry) and 20% (wet) were related to extreme water stress. • The Midwest and the Northern Great Plains are hotspots of climate-driven N surplus. Increasing extreme climate conditions present significant threats to crop nitrogen (N) management and environmental conservation in the United States. Previous studies have examined the impacts of extreme climate on crop production but often overlooked its impacts on N loss from agricultural areas to the environment. Here we examined the relationship between county-level N surplus (the part of N that is not recovered by crops) and extreme climate conditions for corn in the U.S. during 1981–2016. We adopted multi-source long-term datasets of corn N budget and growing season (defined from May to August) maximum temperature and precipitation for each corn-planting county. Compared with the long-term N surplus trend, we found an increase of 51% and 47% in corn N surplus associated with extremely high temperature and extremely low precipitations, respectively. A moderate N surplus increase (20%) was associated with extremely high precipitations, while an N surplus decrease of 14% was shown to be related to extremely low temperatures. Across the U.S., the Midwest and the Northern Great Plains were identified as N surplus hotspots when extreme climate conditions occur. As the major corn-planting region, the Midwest accounted for 35% of the national extreme county-year samples but yielded 68% of the national total N surplus associated with extreme climate conditions. Our results highlighted the urgency of understanding the impacts of extreme climate conditions on crop nutrient losses and identifying the effective intervention practices to adapt to more frequent climate extremes in the future. [ABSTRACT FROM AUTHOR]

• A unified agricultural modeling framework is implemented in DLEM v4.0. • Simulated results agree well with site-scale LAI, biomass and yield measurements. • Regional simulations can well reproduce spatial-temporal patterns of crop production. • DLEM v4.0 can be used to support agricultural climate adaptation and mitigation. Agricultural decision-making by different interest groups (e.g., farmers, development agents and policy makers) usually takes place on different scales (e.g., plot, landscape and country). Currently, tools to assist decision-making are either dedicated to small-scale management guidance or large-scale assessment, which ignore the cross-scale linkages and interactions and thus may not provide robust and consistent guidance and assessment. Here, we developed an advanced agricultural modeling framework by integrating the strengths of conventional crop models in representing crop growth processes and management practices into a terrestrial biosphere model (TBM), the Dynamic Land Ecosystem Model (DLEM), to meet the cross-scale application needs (e.g., adaptation and mitigation). Specifically, dynamic crop growth processes, including crop-specific phenological development, carbon allocation, yield formation, biological nitrogen fixation processes, and management practices such as tillage, cover cropping and genetic improvements, were explicitly represented in DLEM. The new model was evaluated against site-scale observations and the results showed that the model performed generally well, with an average normalized root mean square error of 19.91% for leaf area index and 17.46% for aboveground biomass at the seasonal scale and 14.42% for annual yield. Then the model was applied to simulate corn, soybean, and winter wheat productions in the conterminous United States from 1960 to 2018. The spatial patterns of simulated crop productions were consistent with ground survey data. Our model also captured both the long-term trends and interannual variations of the total national productions of the three crops. This study demonstrates the significance of fusing conventional crop modeling techniques into TBMs to establish a unified modeling framework, which holds the potential to address climate impacts, adaptation and mitigation across varied spatiotemporal scales. [ABSTRACT FROM AUTHOR]