For decades, predominant soil biogeochemical models have used conceptual soil organic matter (SOM) pools and only simulated them to a shallow depth in soil. Efforts to overcome these limitations have prompted the development of the new generation SOM models, including MEMS 1.0, which represents measurable biophysical SOM fractions, over the entire root zone, and embodies recent understanding of the processes that govern SOM dynamics. Here we present the result of continued development of the MEMS model, version 2.0. MEMS 2.0 is a full ecosystem model with modules simulating plant growth with above- and belowground inputs, soil water and temperature by layer, decomposition of plant inputs and SOM, and mineralization and immobilization of nitrogen (N). The model simulates two commonly measured SOM pools – particulate and mineral-associated organic matter (POM and MAOM, respectively). We present results of calibration and validation of the model with several grassland sites in the US. MEMS 2.0 generally captured the soil carbon (C) stocks (R² of 0.89 and 0.6 for calibration and validation, respectively) and their distributions between POM and MAOM throughout the entire soil profile. The simulated soil N matches measurements but with lower accuracy (R² of 0.73 and 0.31 for calibration and validation of total N in SOM, respectively) than for soil C. Simulated soil water and temperature were compared with measurements, and the accuracy is comparable to the other commonly used models. The seasonal variation in gross primary production (GPP; R² = 0.83), ecosystem respiration (ER; R² = 0.89), net ecosystem exchange (NEE; R² = 0.67), and evapotranspiration (ET; R² = 0.71) was well captured by the model. We will further develop the model to represent forest and agricultural systems and improve it to incorporate new understanding of SOM decomposition.

Nitrous oxide (N₂O) exacerbates the greenhouse effect and thus global warming. Agricultural management practices, especially the use of nitrogen (N) fertilizers and irrigation, increase soil N₂O emissions. As a vital sector of global agriculture, specialty crop systems usually require intensive input and management. However, soil N₂O emissions from global specialty crop systems have not been comprehensively evaluated. Here, we synthesized 1137 observations from 114 published studies, conducted a meta‐analysis to evaluate the effects of agricultural management and environmental factors on soil N₂O emissions, and estimated global soil N₂O emissions from specialty crop systems. The estimated global N₂O emission from specialty crop soils was 1.5 Tg N₂O‐N year⁻¹, ranging from 0.5 to 4.5 Tg N₂O‐N year⁻¹. Globally, soil N₂O emissions exponentially increased with N fertilizer rates. The effect size of N fertilizer on soil N₂O emissions generally increased with mean annual temperature, mean annual precipitation, and soil organic carbon concentration but decreased with soil pH. Global climate change will further intensify the effect of N fertilizer on soil N₂O emissions. Drip irrigation, fertigation, and reduced tillage can be used as essential strategies to reduce soil N₂O emissions and increase crop yields. Deficit irrigation and non‐legume cover crop can reduce soil N₂O emissions but may also lower crop yields. Biochar may have a relatively limited effect on reducing soil N₂O emissions but be effective in increasing crop yields. Our study points toward effective management strategies that have substantial potential for reducing N₂O emissions from global agricultural soils.

Anthropogenic activities have altered approximately two‐thirds of the Earth's land surface. Urbanization, industrialization, agricultural expansion, and deforestation are increasingly impacting the terrestrial landscapes, leading to shifts of areas in artificial surface (i.e., humanmade), cropland, pasture, forest, and barren land. Land use patterns and associated greenhouse gas (GHG) emissions play a critical role in global climate change. Here we synthesized 29 years of global historical data and demonstrated how land use impacts global GHG emissions using structural equation modeling. We then obtained predictive estimates of future global GHG emissions using a deep learning model. Our results show that, from 1992 to 2020, the global terrestrial areas covered by artificial surface and cropland have expanded by 133% and 6% because of population growth and socioeconomic development, resulting in 4.0% and 3.8% of declines in pasture and forest areas, respectively. Land use was significantly associated with GHG emissions (p < 0.05). Artificial surface dominates global GHG emissions, followed by cropland, pasture, and barren land. The increase in artificial surfaces has driven up global GHG emissions through the increase in energy consumption. Conversely, improved agricultural management practices have contributed to mitigating agricultural GHG emissions. Forest, on the other hand, serves as a sink of GHG. In total, global GHG emissions increased from 31 to 46 GtCO₂eq from 1992 to 2020. Looking ahead, if current trends in global land use continue at the same rates, our model projects that global GHG emissions will reach 76 ± 8 GtCO₂eq in 2050. In contrast, reducing the rates of land use change by half could limit global GHG emissions to 60 ± 3 GtCO₂eq in 2050. Monitoring and analyzing these projections allow a better understanding of the potential impacts of various land use scenarios on global climate and planning for a sustainable future.

Landslides are considered a natural process, with hundreds of events occurring every year in many regions of the world. However, human activities can significantly affect how stable a slope or cliff is, increasing the chances of slope collapses. Moreover, agricultural irrigation has potential to saturate subsurface materials well below ground level and is known to be an important factor that can trigger landslides in many countries. A macroregional literature review of irrigation-induced landslides was developed in this investigation, considering what has been published in Chinese, English, and Spanish. A total of 115 peer-reviewed papers, books and book chapters, graduate and undergraduate theses, and government reports were found, including 82 case studies (23 in Chinese, 26 in English, and 33 in Spanish). Results from this analysis indicate that studies focusing on this important topic have increased exponentially since the 1960s, with most irrigation-induced landslides occurring in dry climates (precipitation less than 600 mm/year), with rainfall concentrated during summer months. The majority of studies have been done in the loess region of China (Asian region), followed by Peru (Latin American region), though cases were found from other macroregions (African, Indian, Russian, Angloamerican, and Indonesian). Based on this global review, new agricultural irrigation projects located in collapsible areas must include a landslide risk analysis. Cultivated areas can follow a series of measures to minimize the chances of triggering a landslide, which would put human lives, ecosystems, food production, and infrastructure at risk.

Accurate estimation of crop net primary production (NPP) and yields is fundamental for regional analyses of agroecosystem dynamics using process‐based models. In this study, we simulated croplands in the contiguous U.S. using the DayCent ecosystem model with new production algorithms. Crops were divided into crop variety groups based on regional varieties of three major crops (corn, soybeans, and winter wheat) and generic parameter values that were generated for each group. These varieties have been developed through crop breeding programs and enhance production of major crop types in different temperature and precipitation regimes. NPP and yields for the three major crops were evaluated at the county level with reported yields from the National Agricultural Statistics Service (NASS). The predictions of the multiyear average yields in all counties were more accurate than most other published results using process‐based models. DayCent predictions of yields produced an overall R² of 0.54, 0.54, and 0.38 for corn, soybean, and winter wheat, respectively, with predictions for most counties within ±20% of the NASS reported yields. Our estimations of the total annual NPP for the three crops in the contiguous U.S. are 0.24, 0.09, and 0.06 Pg C yr⁻¹ for corn, soybean, and winter wheat, respectively. Together, they contribute 7.3% to 14.8% of the total NPP for all vegetation in the contiguous U.S. We conclude that crop variety groups capture heterogeneity in NPP for major crop types and can improve biogeochemical model predictions of NPP for croplands.