The environmental sustainability of the largest citrus plantation globally is facing a great challenge in China. Further, there is a lack of quantitative, regional hotspot studies. In this study, the life cycle assessment (LCA) was used to quantify the environmental cost of citrus production based on 155 farmers’ surveys from typical citrus orchards in Danling County, southwest China, which produced 0.65% of the country’s total citrus production. The results showed that the average values of environmental risk indicated by global warming potential (GWP), acidification potential (AP), and eutrophication potential (EP) were 11,665 kg CO₂-eq ha⁻¹, 184 kg SO₂-eq ha⁻¹, and 110 kg PO₄-eq ha⁻¹, respectively. The production and utilization of fertilizer ranked the first contribution to the environmental impacts among all the environmental impacts, which contributed 92.4–95.1%, 89.4–89.8%, and 97.8–97.9% to global warming potential, acidification potential, and eutrophication potential, respectively. Specific to the contribution of fertilizers to environmental costs, the production and utilization of nitrogen (N) fertilizer accounted for more than 95% of the total environmental costs. Thus, the spatial distribution of environmental costs in this county was well matched with that of N input. Compared with the average values of investigated 155 orchards, the high yield and high N use efficiency (HH) orchard group with younger and better educated owners achieved a higher citrus yield and N use efficiency with less fertilizer input and lower environmental costs. Five field experiments conducted by local government and Danling Science and Technology Backyard were used to further certify the reduction potential of environment costs. These field results showed that the local recommendation (LR) treatment increased citrus yield and N use efficiency by 1.9–49.5% and 38.0–116%, respectively, whereas decreased environmental costs by 21.2–35.2% when compared with the local farmer practice in the HH orchard group. These results demonstrated that an optimum nutrient management based on the local field recommendation in citrus-producing areas is crucial for achieving a win-win target of productivity and environmental sustainability in China and other, similar countries.

An excess of material input in fruit orchards has brought serious environmental problems, particularly in China. However, studies on the estimation of greenhouse gas (GHG) emissions in peach orchards are limited. In this study, based on questionnaire surveys in major peach-producing regions, including the North China Plain (n = 214), as well as northwest (n = 22) and southwest (n = 33) China, the carbon footprints (CFs) of these orchards were calculated by the life cycle assessment. The potential emission reduction in each region was estimated by combining the GHG emissions and CFs with plantation areas and fruit yields. The results showed that the average GHG emissions in the North China Plain, northwest, and southwest regions were 15,668 kg CO₂-eq ha⁻¹, 10,386 kg CO₂-eq ha⁻¹, and 5580 kg CO₂-eq ha⁻¹, with corresponding CFs of 0.48 kg CO₂-eq ha⁻¹, 0.27 kg CO₂-eq ha⁻¹, and 0.20 kg CO₂-eq kg⁻¹, respectively. The main contribution source of GHG emissions in these three regions was fertilizer (77–95%), followed by electricity, pesticides, and diesel. By adopting advanced farming practices with high yield and a high partial factor productivity of fertilizer, the GHG emissions could be reduced by ~13–35%, with the highest potential reduction in the North China Plain. In conclusion, the GHG emissions and their CFs were impressively high in China’s major peach-producing regions, but these GHG emissions could be substantially decreased by optimizing nutrients and irrigation management, including the rational selection of fertilizer rates and types with water-saving irrigation systems or practices (e.g., mulching) for increasing fertilizer and water use efficiency, and maintaining a sustainable peach production in China or similar countries.

With increasing application of nitrogen (N), phosphorus (P), and potassium (K) fertilizers, especially in China’s fruit crops, the agronomic responses of fruit crops to fertilization may be reduced with time. Thus, the quantification of these responses would be useful for establishing nutrient recommendation and fertilizer management for fruit crops. Here, a meta-analysis including 552 paired data for agronomic response and 1283 sets for amounts of optimal fertilization from 293 field studies in China were performed to systemically quantify these variations of yield response (YR), relative yield (RY), agronomic efficiency (AE), and partial factor productivity (PFP) in response to the application of N, P, or K fertilizer under different groups including fruit crop types, time, and regions. The results showed that the average YRs to N, P or K fertilizer were 7.6, 5.2, or 5.9 t ha⁻¹, indicating related RYs of 78.0%, 82.9%, or 82.4%, respectively. All of the RYs for N, P, or K application in studies after 2000 were higher and less variable than those before 2000. Higher RYs were also shown for deciduous fruit trees when compared with evergreen fruit trees. The average AEs of N, P, and K fertilizer in China’s fruit crops were 29.1, 32.4 and 20.2 kg kg⁻¹, all of them were negatively correlated with fertilizer rate. Due to a higher yield response and less fertilizer rate, annual crops (mainly watermelon and melons) had significantly higher AE than that of perennial crops. The average PFPs of N, P, and K fertilizer in China’s fruit crops were 129, 205, and 113 kg kg⁻¹, all of which showed a declining trend with time. These findings demonstrated that the building-up of soil indigenous nutrient supply (indicated by RY) together with improving fruit varieties, as well as pest management and other forms of management could make external fertilization less important for increasing the yield of fruit crops in China. A rational nutrient management is therefore crucial for balancing yield and environmental concerns in countries like China, India, and other countries where fertilizers are often overused.

New shoots (including newly formed leaves and twigs) and fruits of citrus tree are key organs for present yield formation and flower differentiation in the next season, but the relationship between yield fluctuation and accumulations of major nutrients in new shoots and fruits of citrus tree is still unclear. Thus, to quantify the biomass and mineral nutrient accumulation in new shoots and fruits of citrus trees under varied yield levels has essential significance for rational fertilization and pruning management for citrus orchards. The purpose of this study was, therefore, to investigate the accumulation of nitrogen (N), phosphorus (P), potassium (K), calcium (Ca) and magnesium (Mg), and the distribution of biomass in new shoots and fruits of citrus trees under low, medium and high-yield levels in each of eight orchards located in Chongqing, China. The results showed that substantial variation of fruit yield was observed in all eight orchards with an average yield of 15.0 (low), 30.9 (medium) and 60.1 (high) kg/plant. The averaged biomass of new shoots ranged from 1.59 to 2.51 kg/plant, which was significantly and positively correlated with fruit yield. Nutrient accumulation in leaves was generally highest among new organs, while more than half of N (52.70–71.4%), P (66.5–80.4%) and K (68.9–85.9%) accumulated in fruit. Fruit yields closely correlated with total amounts of major nutrients in new shoots. Furthermore, the nutrient requirements per unit of newly developed shoots and fruits were gradually decreased with increased yield, but the removed nutrients per ton of fresh fruit were almost stable, indicating that more nutrients were distributed into fruit tissues. Taken together, these findings are valuable for optimizing nutrient and pruning management in citrus orchards in China and other similar countries.

Identifying nitrogen-transforming genes and the microbial community in the lacustrine sedimentary environment is critical for revealing nitrogen cycle processes in eutrophic lakes. In this study, we examined the diversity and abundance of ammonia-oxidizing bacteria (AOB), ammonia-oxidizing archaea (AOA), denitrifying bacteria (DNB), and anammox bacteria (AAOB) in different trophic status regions of Lake Taihu using the amoA, Arch-amoA, nirS, and hzo genes as functional markers. Quantitative Polymerase Chain Reaction (qPCR) results indicated that the abundance of the nirS gene was the highest, while the amoA gene had the lowest abundance in all regions. Except for the primary inflow area of Lake Taihu, Arch-amoA gene abundance was higher than the hzo gene in three lake bays, and the abundance of the nirS gene increased with decreasing trophic status. The opposite pattern was observed for the amoA, Arch-amoA, and hzo genes. Phylogenetic analyses showed that the predominant AOB and AOA were Nitrosomonas and Nitrosopumilus maritimus, respectively, and the proportion of Nitrosomonas in the eutrophic region (87.9%) was higher than that in the mesotrophic region (71.1%). Brocadia and Anammoxoglobus were the two predominant AAOB in Lake Taihu. Five novel unknown phylotypes of AAOB were observed, and Cluster AAOB-B was only observed in the inflow area with a proportion of 32%. In the DNB community, Flavobacterium occurred at a higher proportion (22.6–38.2%) in all regions, the proportion of Arthrobacter in the mesotrophic region (3.6%) was significantly lower than that in the eutrophic region (15.6%), and the proportions of Cluster DNB-E in the inflow area (24.5%) was significantly higher than that in the lake bay (7.3%). The canonical correspondence analysis demonstrated that the substrate concentration in sedimentary environments, such as NO_x^--N in the sediment, NH₄⁺-N in the pore water, and the total organic matter, were the key factors that determined the nitrogen-transforming microbial community. However, the temperature was also a predominant factor affecting the AOA and AAOB communities.

Different cultivars of pear trees are often planted in one orchard to enhance yield for its gametophytic self-incompatibility. Therefore, an accurate and robust modelling method is needed for the non-destructive determination of leaf nitrogen (N) concentration in pear orchards with mixed cultivars. This study proposes a new technique based on in-field visible-near infrared (VIS-NIR) spectroscopy and the Adaboost algorithm initiated with machine learning methods. The performance was evaluated by estimating leaf N concentration for a total of 1285 samples from different cultivars, growth regions, and tree ages and compared with traditional techniques, including vegetation indices, partial least squares regression, singular support vector regression (SVR) and neural networks (NN). The results demonstrated that the leaf reflectance responded to the leaf nitrogen concentration were more sensitive to the types of cultivars than to the different growing regions and tree ages. Moreover, the AdaBoost.RT-BP had the best accuracy in both the training (R² = 0.96, root mean relative error (RMSE) = 1.03 g kg⁻¹) and the test datasets (R² = 0.91, RMSE = 1.29 g kg⁻¹), and was the most robust in repeated experiments. This study provides a new insight for monitoring the status of pear trees by the in-field VIS-NIR spectroscopy for better N managements in heterogeneous pear orchards.