Integrated rice management simultaneously improves rice yield and nitrogen use efficiency in various paddy fields

Quantifying the relationships of maize yield and nitrogen use efficiency (NUE) to inherent soil productivity (ISP) could provide a theoretical basis for implementing strategies that concurrently narrow the yield gap, increase NUE, and improve soil quality. A field study under irrigation conditions was conducted at five locations with large differences in ISP (estimated by maize grain yield without using fertilizers) across various ecological regions in Inner Mongolia, China. Our results showed that the changes in maize yield and nitrogen partial factor productivity (PFPN) to ISP followed a linear-plateau model, with a common inflection point. When ISP was below 8.0 t ha–1, maize yield and PFPN were stagnating at a low level, due to a trade-off between the contribution of soil and chemical fertilizers to yield. When ISP exceeded 8.0 t ha–1, the contribution rate of soil to yield stabilized at 80%, resulting in a simultaneous increase in maize yield by 1.2 t ha–1 and PFPN by 4.6 kg kg–1, for increasing ISP at each t ha–1. Our results indicated that while keeping other management strategies unchanged, to increase maize yield and PFPN by 15% or further 30%, it is necessary to increase ISP to 9.3 and 10.4 t ha–1, respectively. In this scenario, N input will be reduced by 33.5 and 47.5%, and apparent N losses will be reduced by 11.7 and 21.5%, respectively. Therefore, enhancing ISP could lead to a simultaneous improvement in yield and NUE as well as strongly support the green development of maize production.

Section: Introductionmentioning

confidence: 71%

Concurrent Improvement in Maize Grain Yield and Nitrogen Use Efficiency by Enhancing Inherent Soil Productivity

Wang

et al. 2022

“…Optimal application rates of N in agriculture production can provide economic and ecological benefits while also benefiting human health and welfare ( Qiao et al., 2018 ). However, excessive use of fertilizer N does not increase yields and poses a significant environmental threat ( Dong et al., 2020 ). Wang et al.…”

Section: Discussionmentioning

confidence: 99%

“…human health and welfare (Qiao et al, 2018). However, excessive use of fertilizer N does not increase yields and poses a significant environmental threat (Dong et al, 2020). Wang et al (2020) previously demonstrated a 52% reduction potential in N inputs for pear orchards.…”

Section: Changes Required For Sustainable Plum Productionmentioning

confidence: 99%

Potential mitigation of environmental impacts of intensive plum production in southeast China with maintenance of high yields: Evaluation using life cycle assessment

Yan

Tang

et al. 2023

IntroductionIntensive plum production usually involves high yields but also high environmental costs due to excessive fertilizer inputs. Quantitative analysis of the environmental effects of plum production is thereby required in the development of optimum strategies to promote sustainable fruit production.MethodsWe collected survey questionnaires from 254 plum production farms in Zhao’an county, Fujian province, southeast China to assess the environmental impacts by life cycle assessment (LCA) methodology. The farms were categorized into four groups based on yield and environmental impacts, i.e., LL (low yield and low environmental impact), LH (low yield but high environmental impact), HL (high yield but low environmental impact), and HH (high yield and high environmental impact).ResultsThe environmental impacts, i.e., average energy depletion, global warming, acidification, and eutrophication potential in plum production were 18.17 GJ ha-1, 3.63 t CO2 eq ha-1, 42.18 kg SO2 eq ha-1, and 25.06 kg PO4 eq ha-1, respectively. Only 19.7% of farmers were in the HL group, with 13.3% in the HH group, 39.0% in LL, and 28.0% LH. Plum yields of the HL group were 109-114% higher than the mean value of all 254 farms. Additionally, the HL group had a lower environmental impact per unit area compared to the overall mean value, with a reduction ranging from 31.9% to 36.7%. Furthermore, on a per tonne of plum production basis, the energy depletion, global warming potential, acidification potential, and eutrophication potential of HL farms were lower by 75.4%, 75.0%, 75.6%, and 75.8%, respectively. Overall, the total environmental impact index of LL, LH, HL, and HH groups were 0.26, 0.42, 0.06, and 0.21, respectively.DiscussionExcessive fertilizer N application was the main source of the environmental impacts, the potential to reduce fertilizer N rate can be achieved without compromising plum yield by studying the HH group. The results provide an important foundation for enhancing the management of plum production, in order to promote ‘green’ agricultural development by reducing environmental impacts.

“…Nearly half of the world’s population depends on rice for food. Globally, Asia, Africa, and Latin America account for 98% of the world’s rice cultivation ( Dong et al, 2020 ). Asia is responsible for 90% of the world’s rice production, and China is the world’s largest rice producer ( Wan et al, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

A Study of Nitrogen Deficiency Inversion in Rice Leaves Based on the Hyperspectral Reflectance Differential

Feng

et al. 2020

To achieve rapid, accurate, and non-destructive diagnoses of nitrogen deficiency in cold land japonica rice, hyperspectral data were collected from field experiments to investigate the relationship between the nitrogen (N) content and the difference in the spectral reflectance relationship and to establish the hyperspectral reflectance difference inversion model of differences in the N content of rice. In this study, the hyperspectral reflectance difference was used to invert the nitrogen deficiency of rice and provide a method for the implementation of precision fertilization without reducing the yield of chemical fertilizer. For the purpose of constructing the standard N content and standard spectral reflectance the principle of minimum fertilizer application at maximum yield was used as a reference standard, and the acquired rice leaf nitrogen content and leaf spectral reflectance were differenced from the standard N content and standard spectral reflectance to obtain N content. The difference and spectral reflectance differential were then subjected to discrete wavelet multiscale decomposition, successive projections algorithm, principal component analysis, and iteratively retaining informative variables (IRIVs); the results were treated as partial least squares (PLSR), extreme learning machine (ELM), and genetic algorithm-extreme learning machine (GA-ELM). The results of hyperspectral dimensionality reduction were used as input to establish the inverse model of N content differential in japonica rice. The results showed that the GA-ELM inversion model established by discrete wavelet multi-scale decomposition obtained the optimal results in data set modeling and training. Both the R2 of the training data set and the validation data set were above 0.68, and the root mean square errors (RMSEs) were <0.6 mg/g and were more predictive, stable, and generalizable than the PLSR and ELM predictive models.