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Though their small size, ponds play a disproportionately crucial role in eliminating nitrogen (N) transporting to downstream freshwaters. As significant water infrastructures, ponds are non-sustainable due to loss of storage capacity resulting from sedimentation. However, the effects of pond sedimentation on N removal is widely neglected in landscape N processing. The NUFER (Nutrient flows in Food chains, Environment and Resources use) model was employed to estimate N runoff from 1960 to 2018. We reconstructed the dynamic of number and storing capacity of about 14 million ponds due to construction and sedimentation from 1960 to 2018, projecting these trends into the year 2060. Our approach incorporated first-order kinetic reactions, including water residence time (HRT), to estimate N removal of ponds, utilizing data 6 monitoring ponds and 81 ponds from literature studies. Our analysis reveals a fourteen-fold increase in N runoff over the past six decades, rising from 0.8 Mt N in 1960 to 11.4 Mt N in 2018. Due to the initial rapid expansion of ponds, N removal by ponds increased from 6.4 % in 1960 to 13.6 % in 1990. Sedimentation is prevalent in ponds, particularly in small ponds with a sedimentation accumulation rate of 2.96 cm yr-1. Pond sedimentation, which reduces HRT, resulted in a decrease in pond N removal percentage to 11.2 % in 2018 and a projected 7.4 % by the year 2060, assuming similar sediment accumulation rates persist in the future. Overall, our findings underscore the non-negligible role of ponds as landscape nodes in N cycling. Urgent mitigation measures are needed to extend the lifetime of existing ponds and sustain their critical role in water quality management.

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Risk index tools have the potential to assist farmers in making strategic decisions regarding their farm design to manage losses of nutrients. Such tools require a vulnerability framework, and these are often based on scores or rankings. These frameworks struggle to take account of interactions between elements of the physical environment. Process-based simulation models inherently take account of interactions and may be a viable alternative to score-based methods. We describe the method to populate a database of transport factors that covers the agricultural lands of New Zealand that is designed for usage as the susceptibility framework within a risk index tool. The method gives both leaching and runoff transport factors and gives values by month. The simulation model used had already been validated for simulating water and nitrogen balances and the generated spatial patterns of the transport factors was validated via expert assessment. These features allow good representation of the risks posed across a wide range of farming activities.•Use of a simulation model to quantify transport factors.•Captures the interactions between soil and weather factors in the physical environment.•Produces a country-wide database intended as a susceptibility framework for a risk index tool.

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Excessive nitrogen (N) application in wheat-maize cropping systems was adjusted towards more sustainable practices to reduce hydrological N losses while maintaining crop yield. In comprehensive quantification of N management effects on crop yield, N use efficiency (NUE), hydrological N losses, and soil nitrate residual across eight seasons, we have added to growing evidence of strategies beneficial for sustainable crop production with lower hydrological N losses. The results show that adjusted N practices enhanced crop yield and NUE, as compared to farmer's practices, but benefits varied with N rates and types. Optimized N treatment (OPT, 180 kg N ha-1 in both maize and wheat seasons) with or without straw returning produced the most crop yield. They increased maize yield by 5.5% and 7.3% and wheat yield by 6.2% and 3.2% on average, as compared to farmer's practice with huge N application (FP, 345 kg N ha-1 and 240 kg N ha-1 in maize and wheat). Regulation of N release through amendment with controlled release urea at a rate of 144 kg N ha-1 crop-1 (CRU treatment) obtained 4.4% greater maize yield than FP, and sustained a similar wheat yield with less N input, resulting in the highest crop NUE. Additionally, CRU was most effective in mitigating hydrological N loss, with 39.5% and 45.5% less leachate N and 31.9% and 35.9% less runoff N loss than FP in maize and wheat seasons. Synthetic N input correlated significantly and positively with runoff and leachate N losses, indicating it was one of the dominant factors driving hydrological N losses. Moreover, compared to OPT, additional straw returning (STR) or substituting 20% of the nutrients by duck manure (DMS) further reduced runoff N discharges due to the fact that organic matter incorporation increased resilience to rainfall. N over-application in FP caused considerable nitrate accumulation in the 0-90-cm soil profile, while the adjusted N practices, i.e., OPT, STR, CRU, and DMS treatments effectively controlled it to a range of 79.6-92.9 kg N ha-1. This study suggests that efforts using optimized N treatment integrated with CRU or straw returning should be encouraged for sustainable crop production in this region.

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To boost crop production, China uses almost a third of the world's nitrogen (N) fertilizer. However, N losses due to enhanced application of N fertilizers has led to surface water and groundwater pollution. A reduction in N losses without reducing crop yields is possible by increasing nitrogen use efficiency (NUE), which is important for the effective management of local crop production and water quality. This study used two representative agricultural counties in China (Quzhou and Qiyang) to assess if it is possible to achieve N loss thresholds in surface and groundwater by optimizing N management measures while maintaining actual crop production. We used a spatially explicit N balance model to assess the spatial variation in actual N inputs to soil and N losses to water, and in critical N losses and associated agricultural N inputs. We also used this model to calculate the spatial variation in actual NUEs and the required NUE to align actual crop production with N thresholds. We then assessed the feasibility of achieving the necessary NUE changes through optimizing agricultural N management strategies. It was found that actual N input exceeded critical N input in 95 and 83 % of the agricultural area in Quzhou and Qiyang, respectively. To meet actual crop production without exceeding N loss thresholds, the NUE needs to increase with 11 to 15 % whereas the total N input needs to be reduced by 37 %. NUE gaps can be closed by reducing N rates, enhancing organic manure recycling, and using efficiency-enhancing fertilizers, with optimal combinations being dependent on site conditions.

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Excess agricultural nitrogen, mainly from manure and chemical fertilizers, is a primary source of nutrient pollution and presents serious environmental threats to natural ecosystems and human health. Improvements in nitrogen-use efficiency in crop production are critical for addressing the triple challenge of food insecurity, environmental degradation, and climate change. Approaches such as sustainable intensification that stress technological innovations have received the most attention. But science-based cropland use planning, a promising complementary approach, has so far been largely overlooked. Here we develop a spatially integrated economic-ecological modeling method to assess this previously unexplored potential for improving the economic-environmental performance of crop production by examining the seasonal and spatial implications of cropland and fertilizer use in Bangladesh. In doing so, we aim to make the modeling method accessible to researchers and practitioners interested in achieving the dual goal of food production and environmental sustainability for countries that are characterized by seasonal and spatial variations in crop mix and cropping practices.-The modeling method combines economic and ecological models to explore trade-offs between food security and environmental sustainability.-The modeling method considers both spatial and seasonal dimensions when measuring trade-offs.-The modeling method is highly suitable for adaptation to study topics concerning food security-ecosystem service trade-offs or to inform the design of related models.

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Crop residue return is an effective, eco-friendly tillage method for decreasing reactive nitrogen (Nr) losses via surface runoff. However, the associated variation in Nr characteristics and its prospective mechanisms are not well understood. We systematically evaluated the response of Nr runoff loss and N variation in standing water to the abiotic and biotic parameters of soil in a paddy field after 6 years of straw return. Five experimental treatments of different fertilization strategies in combination with straw return were tested during the rice growth season. The results indicated that under equivalent fertilizer input, long-term straw return significantly reduced Nr runoff loss by 11.5% (P < 0.05), even though the loss increased with N fertilizer addition. We report that variations in abiotic soil properties (P < 0.05) and bacterial communities (P < 0.01) were both responsible for Nr loss differences between the rice growth stages and among the tested fertilizing patterns. Soil inorganic nitrogen (r = 0.18) had a significant positive influence on Nr runoff loss, but this effect was surpassed by the overall negative influence of soil organic carbon (r = -0.43), soil pH, (r = -0.40), and bacterial community composition (r = -0.14), which was especially apparent during the tillering stage. Our results emphasize the importance of jointly considering biotic and abiotic factors in soil and standing water when characterizing the effects of long-term straw return and N addition on Nr runoff loss, which will aid in mitigating N-based agricultural non-point pollution.

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Agricultural production in the EU has increased strongly since the 1940s, partly driven by increased nitrogen (N) fertiliser and manure inputs. Increased N inputs and associated losses, however, adversely affect air and water quality, with widespread impacts on terrestrial and aquatic ecosystems and human health. Managing these impacts requires knowledge on 'safe boundaries' for N inputs, i.e., N flows that do not exceed environmental thresholds. We used a spatially explicit N balance model for the EU to derive boundaries for N losses and associated N inputs for three environmental thresholds: (i) N deposition onto natural areas to protect terrestrial biodiversity (critical N loads), (ii) N concentration in runoff to surface water (2.5 mg N l-1) to protect aquatic ecosystems and (iii) nitrate (NO3-) concentration in leachate to groundwater (50 mg NO l-1) to meet the EU drinking water standard. Critical N losses and inputs were calculated for ~40,000 unique soil-slope-climate combinations and then aggregated at country- and EU-level. To respect thresholds for N deposition, N inputs in the EU need to be reduced by 31% on average, ranging from 0% in several countries to 59% in Ireland and Denmark. The strongest reductions are required in intensive livestock regions, such as Benelux, Brittany and the Po valley. To respect thresholds for N concentration in runoff to surface water, N inputs need to be reduced by 43% on average, ranging from 2% in Estonia to 74% in the Netherlands. Average critical N inputs in view of the threshold for NO3- concentration in leachate to groundwater are close to actual (year 2010) inputs, even though leaching thresholds are exceeded in 18% of agricultural land. Critical N inputs and their exceedances presented in this paper can inform more targeted mitigation policies than flat-rate targets for N loss reductions currently mentioned in EU policies.

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Nitrogen (N) runoff loss from croplands due to excessive anthropogenic N additions is a principal cause of non-point source water pollution worldwide. Quantitative knowledge of regional-scale N runoff loss from croplands is essential for developing sustainable agricultural N management and efficient water N pollution control strategies. This meta-analysis quantifies N runoff loss rates and identifies the primary factors regulating N runoff loss from uplands (n = 570) and paddy (n = 434) fields in the Yangtze River Basin (YRB). Results indicated that total N (TN) runoff loss rates from uplands and paddy fields consistently increased from upstream to downstream regions. Runoff depth, soil N content and fertilizer addition rate (chemical fertilizer + manure) were the major factors regulating variability of TN runoff loss from uplands, while runoff depth and fertilizer addition rate were the main controls for paddy fields. Multiple regression models incorporating these influencing factors effectively predicted TN runoff loss rates from uplands (calibration: R2 = 0.60, n = 242; validation: R2 = 0.55, n = 104) and paddy fields (calibration: R2 = 0.70, n = 189; validation: R2 = 0.85, n = 82). Models estimated total cropland TN runoff loss load in YRB of 0.54 (95% Cl: 0.23-1.33) Tg, with 0.30 (95% Cl: 0.15-0.56) Tg from uplands and 0.24 (95% Cl: 0.08-0.77) Tg from paddy fields in 2017. Guangxi, Jiangxi, Fujian, Hunan and Henan provinces within the YRB were identified as cropland TN runoff loss hotspots. Models predicted that TN runoff loss loads from croplands in YRB would decrease by 0.8-13.7% for five scenarios, with higher TN load reductions occurring from scenarios with decreased runoff amounts. Reducing upland TN runoff loss should focus primarily on soil N utilization and runoff management, while reducing N fertilizer addition and runoff provided the most sensitive strategies for paddy fields. Integrated management of water, soil and fertilizer is required to effectively reduce cropland N runoff loss.

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Reliable detection and attribution of changes in nitrogen (N) runoff from croplands are essential for designing efficient, sustainable N management strategies for future. Despite the recognition that excess N runoff poses a risk of aquatic eutrophication, large-scale, spatially detailed N runoff trends and their drivers remain poorly understood in China. Based on data comprising 535 site-years from 100 sites across China's croplands, we developed a data-driven upscaling model and a new simplified attribution approach to detect and attribute N runoff trends during the period of 1990-2012. Our results show that N runoff has increased by 46% for rice paddy fields and 31% for upland areas since 1990. However, we acknowledge that the upscaling model is subject to large uncertainties (20% and 40% as coefficient of variation of N runoff, respectively). At national scale, increased fertilizer application was identified as the most likely driver of the N runoff trend, while decreased irrigation levels offset to some extent the impact of fertilization increases. In southern China, the increasing trend of upland N runoff can be attributed to the growth in N runoff rates. Our results suggested that increased SOM led to the N runoff rate growth for uplands, but led to a decline for rice paddy fields. In combination, these results imply that improving management approaches for both N fertilizer use and irrigation is urgently required for mitigating agricultural N runoff in China.

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Rising human demand and climatic variability have created greater uncertainty regarding global food trade and its effects on the food security of nations. To reduce reliance on imported food, many countries have focused on increasing their domestic food production in recent years. With clear goals for the complete self-sufficiency of rice production, Sri Lanka provides an ideal case study for examining the projected growth in domestic rice supply, how this compares to future national demand, and what the associated impacts from water and fertilizer demands may be. Using national rice statistics and estimates of intensification, this study finds that improvements in rice production can feed 25.3 million Sri Lankans (compared to a projected population of 23.8 million people) by 2050. However, to achieve this growth, consumptive water use and nitrogen fertilizer application may need to increase by as much as 69 and 23 %, respectively. This assessment demonstrates that targets for maintaining self-sufficiency should better incorporate avenues for improving resource use efficiency.

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