Denitrification plays a critical role in mitigating anthropogenic nitrate (NO₃^-) accumulation in JIA-2025-1634 Jinke slj ZR.docxecosystems. The isotopic composition of NO₃^- (δ¹⁵N and δ¹⁸O) serves as a powerful tracer for identifying N sources and transformation processes. Denitrification often superimposed on the isotope effects of NO₂^- oxidation, resulting in parallel enrichment of δ¹⁵N- and δ¹⁸O-NO₃^- (Δδ¹⁸O:Δδ¹⁵N trajectory) that causes them to be either below or above 1. This study compared the Δδ¹⁸O:Δδ¹⁵N trajectory during denitrification, functional genes (narG, napA, and nxrA), and carbon sources from metabolites in the Δδ¹⁸O:Δδ¹⁵N trajectories below or above 1 in unsaturated zones. The results revealed that NO₃^- reduction was more important for variation in the Δδ¹⁸O:Δδ¹⁵N trajectory because the difference in isotope effects (¹⁵ε_{NO3 reduction} and ¹⁸ε_{NO3 reduction}) between the two Δδ¹⁸O:Δδ¹⁵N trajectory groups was significant, whereas the difference in isotope effects (¹⁵ε_nxr and ¹⁸ε_nxr) upon NO₂^- oxidation was not. Carbon sources in the group with Δδ¹⁸O:Δδ¹⁵N trajectories below 1 facilitated more efficient electron production to promote NO₃^- reduction because of their low molecular weight and simple structure. Conversely, the lower electron production efficiency due to the high molecular weight and complex structures of carbon sources in the group with Δδ¹⁸O:Δδ¹⁵N trajectories above 1 downregulated the expression of the three functional genes (narG, napA, and nxrA). The group with Δδ¹⁸O:Δδ¹⁵N trajectories below 1 showed significantly higher levels of ¹⁵ε_{NO3 reduction}, ¹⁸ε_{NO3 reduction}, NO₂^- oxidation ratio, and copy numbers of narG, napA, and nxrA genes compared to the other group, revealing that NO₃^- reduction at the cellular level was more active in the former group. This study elucidated the integrated influence of isotope effects, NO₃^- reductase and NO₂^- oxidoreductase activities, and carbon sources from metabolites. These findings are significant for understanding the Δδ¹⁸O:Δδ¹⁵N trajectories of N cycling in terrestrial ecosystems and support groundwater conservation by improving carbon supplementation approaches that stimulate denitrification, with Δδ¹⁸O:Δδ¹⁵N trajectories serving as effective tracers for assessing denitrification performance in terrestrial environments.

Winter wheat is a key staple crop in Northwest China, yet optimizing its productivity and economic returns remains a challenge due to water constraints and suboptimal planting densities.  This study evaluates the combined effects of irrigation strategies and planting density (PD) on winter wheat yield, resource-use efficiency, and net economic benefits (NEB).  A two-year field experiment was conducted under four irrigation treatments (I1, no irrigation; I2, before winter and jointing; I3, jointing; I4, jointing and anthesis) and three PD treatments (PD1, 562.5×10⁴ plants ha^–1; PD2, 375 ×10⁴ plants ha^–1; PD3, 187.5×10⁴ plants ha^–1).  Through field trials, we identified optimal water-saving irrigation regimes and planting densities that maximize grain yield while enhancing water productivity.  Our results demonstrated that lower PD (187.5×10⁴ plants ha^–1) under reduced irrigation significantly improved dry matter accumulation (DMA), SPAD, and leaf area index (LAI), leading to higher grain yield.  Moderate irrigation at the jointing stage (I3) enhanced grain yield in higher planting densities by up to 18.42% compared to other irrigation regimes, while the highest overall yield (6,310 kg ha^–1) was achieved in medium PD under the I3 irrigation.  Water-use efficiency (WUE) was significantly improved by reducing irrigation at specific growth stages, mitigating excessive evapotranspiration.  PD3–I3 achieved the highest NEB, exceeding I1, I2, and I4 by 11.9, 18.4, and 16.4%, respectively, in 2022–2023 and by 15.1, 14.0, and 8.4%, respectively, in 2023–2024.  The findings provide practical insights for sustainable wheat production, ensuring higher profitability while conserving water resources.  Implementing optimized irrigation and PD strategies offers a strategic pathway to improving food security and farm income in the semi-arid regions of Northwest China.

Nitrogen is a key nutrient for wheat (Triticum aestivum L.) growth and yield, particularly during the grain-filling stage, where most nitrogen is redistributed from vegetative organs to the grain, significantly influencing yield.  However, it remains unclear during which period the nitrogen translocation from the vegetative phase to grain maturation occurs and how it correlates with flag leaf senescence.  In this study, a field experiment was conducted using the winter wheat cultivar ‘Xinong 511’ under two nitrogen fertilizer treatments: regular nitrogen supply (240 kg ha^–1 (N₂₄₀)) and no nitrogen supply (0 kg ha^–1 (N₀)).  The results revealed that nitrogen accumulation in wheat flag leaves peaked at 7–14 days, with a nitrogen content 4.55%, after which nitrogen was redistributed to the grains.  Nitrogen content in flag leaves decreased by 56% during 21–35 days, while that in the grains increased by 51%.  The plant analysis development value (relative chlorophyll content), photosynthetic rate, free amino acid concentration, and soluble protein content in flag leaves peaked at 7–14 days, indicating nitrogen transportation from the flag leaves to the grains.  Nitrogen application significantly increased the nitrogen remobilization rate in flag leaves by 20% compared with that of N₀, reduced reactive oxygen species accumulation by 21%, and delayed flag leaf senescence.  Under nitrogen deficiency, autophagy was induced earlier, with a 5–7-fold increase in the expression of autophagy-related genes (TaATG8), suggesting that regulation of the autophagy pathway and enhancement of autophagy activity can optimize nitrogen fertilization.  Our study demonstrates that the remobilization of nitrogen from vegetative parts to grains initiates leaf senescence and is closely correlated with the expression of autophagy-related genes.

The fine-scale characterization of vegetation surface information serves as a fundamental basis for studying the spatial distribution of resources and the dynamic patterns of environmental responses.  Accurately extracting the distributions of different crop species is of critical importance for improving agricultural production efficiency and ensuring food security.  Traditional fine-scale vegetation extraction methods often face significant challenges due to the presence of spectrally similar features and the substantial influence of background interference, which limit their applicability across large areas.  As a key phenological stage of angiosperms, flowering is characterized by distinctive flowering times, floral morphology, and canopy spectral signatures, so it is an effective pathway for fine-scale vegetation extraction using remote sensing.  Using rapeseed as an example, this study developed a spectral index model for precise flowering vegetation extraction (FI-R) based on Landsat OLI imagery.  The model integrates a yellowness index (Blue, Green) and a peak index (Red, Nir and SWIR1) while leveraging the NDVI to mitigate background interference from spectrally similar objects.  This approach successfully enables the rapid and accurate large-scale mapping of flowering vegetation under complex background conditions.  The proposed method was tested in five rapeseed cultivation regions worldwide with diverse backgrounds.  Validation datasets were generated using GF imagery and the U.S. CDL dataset.  The FI-R model demonstrated superior capability in distinguishing flowering rapeseed from other vegetation, and achieved overall accuracies exceeding 94% in all study areas.  Furthermore, FI-R is compatible with other multispectral sensors that have similar band configurations, so it is applicable to rapeseed extraction in broader contexts.  The method also shows strong potential for the fine-scale extraction of other types of flowering angiosperm vegetation.

Dehydrin (DHN) enhances plant resistance to environmental stress by regulating the synthesis of osmotic adjustment substances and scavenging reactive oxygen species.  However, the role of PbDHN3 under salt stress remains unclear.  In this study, salt stress induced high expression of PbDHN3, and the overexpression of PbDHN3 (OE-PbDHN3) enhanced plant growth under salt stress compared to wild-type (WT) plants.  OE-PbDHN3 plants exhibited higher chlorophyll content and root growth capacity than WT plants under salt stress.  Transcriptome analysis revealed that PbDHN3 expression is associated with ethylene signaling pathways.  OE-PbDHN3 transgenic plants substantially influenced ethylene content and the expression of related genes.  Following treatment with exogenous ethephon, the transgenic lines notably inhibited the processes of ethylene synthesis and signaling transduction.  OE-PbDHN3 transgenic lines treated with exogenous ethylene and the ethylene inhibitor 1-MCP demonstrated significant inhibition of ethylene synthesis and signaling transduction, while promoting root development and chlorophyll content.  Under salt stress, OE-PbDHN3 downregulated the expression of ethylene biosynthesis genes PbACO1-like and PbACO2, and signal transduction genes PbEIN3-like during the initial stress phase.  This early regulation mitigated the adverse effects of salt stress on the plants.  These findings demonstrate that PbDHN3 ameliorates the ethylene-mediated plant growth phenotype under salt stress through regulation of ethylene synthesis and signal transduction.

All developing countries experience a process of rural transformation (RT), and the evidence available to date says that economies that transform their rural sectors more rapidly achieve faster growth and are also more inclusive.  However, not all countries are successful.  This special focus contains five papers related to the nature of rural transformation, its components, and consequences.¹

All authors respond to the proposition that understanding the nature and impact of RT is essential for policymakers, because of their role in its success.  A theme of the collection is the regional diversity in the experience of rural transformation and the value of the design of region-specific policies.

Wang et al. (2023) provide a comprehensive analysis of the concept of RT, its measurement, and the various indicators used in related studies.  They adopt a definition of RT that is more extensive than the simple transformation of agriculture but also retains a focus on the rural economy (and, therefore, is a subset of the structural transformation of the whole economy).  They refer to the text of the definition by IFAD (2016, p. 23) as involving ‘rising agricultural productivity, commercialisation and diversification of production patterns and livelihoods,’ which also involves expanded off-farm employment.   The process also has consequences for inclusion and sustainability.

A feature of RT is the greater use of markets for outputs such as high-value crops and inputs such as labour.  The development of markets in complementary services, such as transport and storage, also supports the use of output markets.  The authors note that RT involves significant changes in rural areas, including shifts in farming practices, land use, population movements, and interactions between primary and other sectors.   

Given all its dimensions, the measurement of RT is a challenge. Current methods and indicators are often insufficient for policy decision-making due to a lack of objectivity, feasibility, accountability, comprehensiveness, and comparability. This study reviews various indicators, such as those referring to components including labour productivity, commercialisation, diversification, inclusiveness, and sustainability. The authors argue that future research could develop more effective measures to assess RT, especially in developing countries, and they review a range of options for further attention.

Shi and Huang (2023) examine the dynamic relationship between RT and its consequences.  They concentrate on its impacts on income growth, and poverty reduction across different regions in China over the past 40 years. The study uses provincial-level data to form indicators of RT.  Specifically, they assess how shifts from traditional agriculture to high-value agriculture (RT₁) and the rise of non-farm employment (RT₂) have impacted rural income and poverty.  These indicators are commonly used in papers in the research project from which this collection is drawn.

The study finds: 1) a positive correlation between both aspects of RT and per capita rural income. While both RT₁ and RT₂ have resulted in a significant increase in rural income, RT₁’s impacts occur more in the later stage than early stage of RT, and RT₂’s impacts happen in every stage of RT.  2) RT₁ and RT₂ have significantly contributed to the reduction of rural poverty.  The effect of RT₁ is more dominant in the early stages of RT, while RT₂ becomes more influential in later stages.  3) The level and speed of RT, along with its effects on income and poverty, vary considerably across regions. Eastern provinces, which are more economically developed, show higher levels of RT and better outcomes in terms of income growth and poverty reduction than other regions.

The findings suggest the importance of targeted policies that promote high-value agriculture and expand rural non-farm employment according to the stage of transformation. And the paper also emphasizes the value of region-specific strategies and policies.

Sudaryanto et al. (2023) explore the impact of RT on household income and poverty in Indonesia over the last 20 years.

They report that RT in Indonesia has been characterized by rapid agricultural growth, particularly in the production of horticulture, estate crops, and livestock, which has surpassed the growth in grain production.  This shift reflects farmers’ orientation from subsistence to market-driven production.  Additionally, there has been an increasing engagement of rural laborers in non-farm employment, contributing to the diversification of rural economies.

Then, using data from 34 provinces in Indonesia from 2000 to 2020, they find growth in both RT₁ and RT₂, with each also positively associated with the growth of rural household income and a decrease in poverty rates. However, they find that the speed of this structural transformation and its consequences varies across regions.

However, there are regional disparities: Regions with a higher level of RT, like those centred around major cities such as Medan and Jakarta, show a more significant increase in household income and a more pronounced reduction in poverty. In contrast, less developed regions, especially those in Eastern Indonesia, face more significant challenges in reducing poverty.  

Policies aimed at improving rural income and reducing poverty should integrate, the authors of the Indonesia paper argue, approaches promoting high-value agriculture and expanding rural non-farm employment, but also pay special attention to regional situations and adopt a contextual policy framework that considers regional diversification in natural resources endowments as well as socio-cultural aspects.

Abedullah et al. (2023) explore the role of RT in enhancing rural per capita income and reducing poverty in Pakistan from 1981 to 2019.  The research uses district-level data to measure RT₁ and RT₂.  The authors find that: 1) Both RT₁ and RT₂ positively affect rural per capita income, with RT₁ having a more pronounced impact than RT₂ (as in the case of China).  2) Both RT₁ and RT₂ are effective in reducing rural poverty, but RT₂ becomes more in the later stages of rural development (as also the case in China).  3) Significant regional disparities occur in the process and impact of RT. Districts at advanced stages of RT positively correlate with increased income and reduced poverty rate (as was also found in both China and Indonesia).

We noted RT’s contribution to inclusion generally, but another important element of the work completed to date is attention to RT’s gender inclusiveness.  Rola-Rubzen et al. (2023) systematically review 84 studies from 1960 to 2021 to understand how RT affects gender indicators and how gender inclusiveness influences RT.

RT significantly impacts women’s work, income, empowerment, wellbeing, and time allocation. It leads to positive outcomes for women, such as increased employment and empowerment opportunities, but also introduces challenges, including unstable income sources and increased workloads.

Due to RT, women are working more, including greater involvement in both on-farm labour and off-farm or non-agricultural employment. While this shift can improve women’s livelihoods, it also results in complexities such as wage discrimination and job insecurity.

The stability of the additional income associated with RT is questionable. Shifts towards high-value agricultural commodities, for example, contribute to reduced income stability. Moreover, women’s increased income does not always equate to increased control over household financial resources.

RT can enhance women’s empowerment, mainly through increased educational opportunities for girls and improved income. Nevertheless, shifts to high-value agriculture sometimes reduce women’s control over income and resources, indicating a complex interplay between RT and empowerment.

In summary, these papers find a positive association of the indicators of rural transformation (high-value products and off-farm employment) with rural income growth and poverty reduction, but with differential impacts at different stages of development.  All studies also finds significant regional variation in performance.

These studies recommends targeted policies to enhance high-value agriculture and expand rural non-farm employment but with region-specific strategies.  The research also highlights the challenges in implementing effective RT strategies, such as land distribution inequalities, credit market limitations, and access to modern agricultural techniques.

The work has led to discussion of policy options that might accelerate rural transformation.  The next step is to systematically consider the drivers of the changes in rural transformation indicators.  The framework applied in the project from which these papers are drawn pays attention to institutions, policies, and investments (IPIs).  Each component of the IPIs is likely to contribute to both RT₁ and RT₂ to varying degrees.  The study of the association of elements of the IPIs with the indicator and, thereby, the outcomes of RT is a topic of current research.  However, some work on the relevant components of the IPIs has been published for the case of China (Huang 2022), which provides direction for this research.  

Relevant institutions, for example, include the conditions of access to land (the development of land markets, in other words) and markets for water and labour.  Markets that support the application of mechanisation are also expected to be essential, as will other markets for complementary services.  Various policies will also influence the emergence of new markets (Barrett et al. 2022).  

Other relevant policies include those affecting incentives faced by rural households, including price regulation, trade policy management, and applying taxes and subsidies.  Sometimes, these linkages also operate in both directions (from policy to RT and back again): an example is trends in trade policy (Anderson 2023) and foreign direct investment (Hu et al. 2023).

Examples of investments include those related to infrastructure (such as roads or irrigation) and technology (through systems of research and development applied to agriculture). As noted, these are topics for further work, intending to map the priorities among the IPIs for accelerating RT depending on its current stage and regional context.  

While RT generally leads to positive outcomes for women, it also presents risks and challenges.  Future RT policies and programs should be more gender-inclusive to maximize benefits for women and further RT itself.  These are critical elements of the larger package of IPIs. 

Accurate estimation of Leaf Area Index (LAI) in multi-variety rice using optical remote sensing remains challenging due to spectral saturation under dense canopy conditions and inter-varietal physiological differences. To address this, we developed a multimodal data fusion framework integrating RGB and multispectral imagery acquired by unmanned aerial vehicles (UAVs), combined with features derived from Digital Surface Models (DSM), vegetation indices (VIs), texture, and depth representations. Using field data collected across 60 rice varieties, four machine learning models were evaluated for LAI estimation. Our results demonstrate that multimodal fusion substantially outperforms conventional VI-based approaches. Among them, the Random Forest Regression (RFR) model achieved optimal performance (R²=0.76, RMSE=0.57), representing a 26–58% improvement in R² over baseline models. SHAP-based feature importance analysis identified DSM feature, height-stratified vegetation indices, and depth features as key contributors to model accuracy. This study establishes that incorporating canopy structural information and deep features mitigates saturation effects and enhances generalizability across varieties. The proposed approach offers a robust and efficient solution for high-throughput LAI estimation, supporting applications in precision agriculture and rice breeding programs.

While straw mulching has the potential to reduce fertilizer-nitrogen (N) losses in intensively managed cropland, how soil organic carbon (SOC) regulates this fate of fertilizer-N at soil aggregate or profile scales remains unresolved. Here, micro-plots were nested within a four-year field experiment to assess fertilizer-N fates and their linkages with SOC fractions and stabilization processes via ¹⁵N-tracing and ¹³C natural abundance analyses. Three treatments were included: (i) conventional N application (FN), (ii) reduced N application (RN), and (iii) reduced N with straw mulching (RS). While RN reduced crop yields compared to FN, RS achieved comparable yields and 7.71% higher N recovery efficiency (P<0.05). The δ¹³C fractionation between aggregates and bulk soil was significantly positively correlated with the fertilizer-N content in the >2 mm and <0.053 mm fractions, indicating that N retention was coupled with SOC stabilization processes. Compared with RN, RS resulted in a 2−3.4 times greater SOC conversion probability into the <0.053 mm fraction and a 1.4 times higher aggregate-associated fertilizer-N content. SOC fractions differentially regulated the profile distribution of fertilizer-N, with nonlabile organic carbon (C) correlated positively, while dissolved organic C correlated negatively but increased plant N recovery. Compared with RN, RS increased the SOC stock by 24%, reduced NO₃⁻-N accumulation by 37%, and immobilized 36% more N into the microbial biomass (P<0.05). Our findings demonstrate that straw mulching increases N recovery by mediating SOC fractionation, stabilization, and microbial N immobilization. These results provide new insights into SOC‒N interactions that could aid in the development of optimal soil C and N management strategies.

Premature senescence of crop foliage presents a formidable challenge to agricultural productivity.  Biogas slurry (BS), an organic effluent, enhances the antioxidant defense system during leaf senescence process.  However, the molecular mechanisms governing the effect of BS on maize leaf senescence remain largely unexplored.  Here, we set up BS topdressing and chemical fertilizer (CF) topdressing treatments, and explored the role of BS in changing the senescence process of maize leaf through physiological and transcriptomic analyses.  Our findings indicated that the BS treatment enhanced the energy supply to aging maize leaves by upreglating the expression of the photosynthetic system, promoting chlorophyll synthesis, enhancing the biological carbon fixation process, and optimizing starch and sucrose metabolism and glycolytic pathways.  Furthermore, BS activated the mitogen-activated protein kinase signaling cascade and elevated peroxisomes expressions.  Concurrently, it suppressed the expression of negative regulatory factors in stress-induced senescence-related plant hormones during maize maturation.  Additionally, the BS treatment decreased the degradation rates of transcription factors from the WRKY, MYB, ERF, and bHLH families during maize leaf senescence.  Consequently, the BS treatment reduced the average senescence rate (27.28%) and maximum senescence rate (13.94%) of leaves, and significantly increase the relative green leaf area at the full maturity stage (94.92%).  Moreover, the superoxide anion content represented as a pivotal mediator in the response to leaf senescence.  Thus, this study showed that BS alleviates the leaf senescence process by regulating the physiological and metabolic processes of maize, thus providing novel strategies for combating crop premature senescence. 

Nitrogen (N) not only provides nutritional support for grain development but also lays the foundation for efficient photosynthesis and yield formation by regulating leaf function and delaying senescence.  However, the regulation of leaf function during the reproductive growth stage and its relationship with yield under drip irrigation remain unclear.  Therefore, from 2020–2021, a cultivar with high nitrogen use efficiency (high-NUE) (T-43) and a low-NUE cultivar (LX-3) were used as the study materials and were grown under drip irrigation with four N fertilization levels (0, 150, 300, and 450 kg ha⁻¹); the differences in leaf morphology, photosynthetic characteristics, hormone contents, antioxidant enzyme activities, biomass (mass), and yield were analysed.  The results revealed the following: (1) N application significantly increased the yield of drip-irrigated rice (17.38-74.03%), and with increasing N application rate, the leaf area index (LAI), chlorophyll a+b (Chl a+b) content, maximum net photosynthetic rate (P_nmax) and mass initially increased but then decreased, reaching optimum values under N₃₀₀, whereas the flag leaf area (LA) continued to increase.  (2) Between the cultivars, T-43 presented relatively high LA and N-metabolizing enzyme activities, thereby increasing the Chl a+b content, light saturation point (I_sat), and mass accumulation; LX-3 presented relatively high abscisic acid (ABA) content, and the accelerated degradation of Chl b resulted in an increased Chl a/b ratio, which inhibited P_nmax.  (3) Structural equation modelling (SEM) further revealed that indole-3-acetic acid (IAA) directly increased P_nmax to increase photosynthetic efficiency, whereas the positive promoting effect of IAA and N-metabolizing enzymes on Chl a+b indirectly increased the LAI and N agronomic efficiency (NAE), thus promoting the positive effects of LAI (0.477^***) and P_nmax (0.715^***) on yield.  In summary, under the appropriate N application rate (300 kg ha⁻¹), in the high-NUE cultivar (T-43), the leaf functional period was maintained, and the photosynthetic capacity was increased via increased hormone contents and antioxidant enzyme activities.  The results of this study provide a theoretical basis for the efficient production of drip-irrigated rice in arid areas.