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China's Losses Plateau (LP) is one of the ecologically vulnerable and the most severe soil erosion regions. Thus, knowing spatiotemporal changes in evapotranspiration (ET) and its components (soil evaporation, E; transpiration, T; and vegetation interception evaporation, EI) and revealing the underlying mechanisms are vital for ecosystem and water resources sustainability for this region. Here, we investigate the spatiotemporal changes in ET and its components and then quantify the impacts of climate variables (i.e., precipitation, radiation, temperature, and relative humidity) and vegetation dynamics (e.g., land use/cover changes [LUCC] and changes in leaf area index [LAI]) on their annual trends, by using a process‐based terrestrial ecosystem model and a joint‐solution method with multiple sensitivity numerical experiments. Results show that over 67% of the study region experienced significant (p < 0.05) increases in annual ET, T, and EI, with regional average rises of 4.05, 3.67, and 0.74 mm·year−1, respectively. However, there are significant (p < 0.05) decreases in regional mean E of 0.38 mm·year−1, and the negative trend covers 35.8% of the study area. E, T, and EI changes dominate the annual ET trends over 11.8%, 87.3%, and 0.9% of the study area, respectively. Attribution analyses highlight the increased LAI as the critical factor governing these trends across most of the LP (>58%). At the same time, precipitation and LUCC play a more dominant role in the remaining areas. This study emphasizes the spatial heterogeneity in the drivers of changes in ET and its components and highlights the critical role of vegetation dynamics. These findings provide valuable insights for understanding the ET processes and guiding sustainable water resource management in the LP. [ABSTRACT FROM AUTHOR]

BackgroundAimsMethodsResultsConclusionAbove-ground biomass (AGB) and its temporal dynamics are key parameters of forest ecosystems, related to their resilience and capacity to fix atmospheric carbon. AGB is related to species richness, composition, forest structure, soils and climate. In human-modified landscapes, fragmentation and human pressure may change the nature of these relationships.Our aim was to quantify how species richness and composition, leaf area index, and edge effect were related to biomass stocks and growth in fragmented sub-tropical secondary forests.We tested the relationship between leaf area index, tree species richness and composition, edge effect and AGB stocks in secondary forest stands in the Atlantic Forest using multiple linear models on data from 104 sites.Our results show that biomass stocks were related to species richness, distance to forest edge and the interaction of both (R2 = 0.25; p < 0.05). Biomass change showed positive relationships with pioneer species richness and distance to edge, and negative relationship with the interaction of total species richness and distance to edge (R2 = 0.15; p < 0.05).Edge effect can affect AGB dynamics directly and indirectly, by weakening the positive effects of species richness and composition on biomass. AGB loss at some sites suggests that some fragments are under chronic disturbance or may be experiencing delayed mortality due to fragmentation. [ABSTRACT FROM AUTHOR]

This study aimed to reveal the effects of arbuscular mycorrhizae and K-humate on some of the morphological characteristics and growth of Russian olive (RO; Elaeagnus angustifolia L.) and black locust (BL; Robinia pseudoacacia L.). The indigenous mycorrhizal spores (Claroideoglomus claroideum, Claroideoglomus etunicatum, Claroideoglomus luteum, and Funneliformis mosseae) collected from rhizospheres of RO and BL trees in afforestation sites located in Central Anatolia. In addition, commercial mycorrhizal mixture and K-humate were used as treatments. Five treatments (1—indigenous mycorrhizal fungi, 2—K-humate, 3—indigenous mycorrhizal fungi + K-humate, 4—commercial mycorrhizal fungi, and 5—control) were assigned in a completely randomized design for both tree species. Four months after the treatments, inoculation rate, above-ground seedling height, fresh and dry weight, root collar diameter, length, fresh, and dry weight, leaf area index, shot-to-root dry weight ratio, seedling height to root collar diameter ratio, and Dickson quality index were determined. Plant and soil analyses were carried out to determine the effects of treatments on plant and soil nutrition. Indigenous arbuscular mycorrhizal fungi and K-humate combinations had positive effects on the morphological characteristics and nutritional status of the seedlings. The indigenous mycorrhizal and K-humate interaction showed the most pronounced effects on RO growth and nutrition. [ABSTRACT FROM AUTHOR]

Grape is a staple crop in many parts of Iran which has shown moderate sensitivity to salinity stress. Water and soil salinity is one of the major environmental stresses that strongly affect the production of agricultural products, including grapes. To overcome the harmful effects of salinity, different methods and materials are used, one of which is the use of nitric oxide. In order to explore the impact of nitric oxide on the mitigation of the negative effects of salinity stress on four grape cultivars and rootstocks ('Bidaneh Sefid' (Sultana) and 'Yaghouti' cultivars, and 140Ru and 1103 P rootstocks), a pot experiment was performed in a factorial based on a randomized complete block design with three replications. Plants were subjected to three sodium chloride (NaCl) levels of 0 (control), 25, and 50 mM and three levels of sodium nitroprusside (SNP) of 0 (control), 0.5, and 1 mM. Results indicated that increasing SNP concentration caused an increase in growth indices such as leaf area, shoot and root length, fresh and dry weights of leaves, shoots, and roots, and leaf relative water content (RWC). Furthermore, salinity decreased the concentrations of potassium (K+), calcium (Ca2+), magnesium (Mg2+), and iron (Fe2+) in leaves, while increased the amount of sodium (Na+) and chlorine (Clˉ) as well as the electrolyte leakage (EL). In addition, SNP at 0.5 and 1 mM could increase the growth efficiency and RWC as well as the elements such as K+ and Mg2+ while decreased the absorption of Na+ and Clˉ as well as the EL in plants under salinity. According to the obtained results, SNP at both concentrations (0.5 and 1 mM) had a pronounced effect on reducing the negative effect of salinity in the evaluated grape rootstocks and cultivars. In general, the positive effects of SNP on 'Yaghouti' and 'Bidaneh Sefid' cultivars were higher than those on 140Ru and 1103 P rootstocks. [ABSTRACT FROM AUTHOR]

ABSTRACT Plants in the understory experience climatic conditions affected by the overstory canopy that influence physiological and biochemical processes. Here, we investigate the relationships of leaf lipid molecular abundances to leaf water content, transmitted irradiance, and free‐air temperature (Tair) from deciduous angiosperm (Quercus buckleyi) and evergreen gymnosperm (Juniperus ashei) understory trees across an elevation gradient in a central Texas (USA) woodland. Monthly sampling from 04/2019 to 01/2020 revealed that long‐chain leaf waxes (≥ C27) accumulated with leaf water deficit over the growing season for both tree species. Higher transmitted light during the hottest, driest months was due to a decreased leaf area index (LAI) in the canopy as leaf shedding is a common drought response. Isoprenoids (sesqui‐, di‐terpenoids, phytosterols) in leaves changed by month with changing LAI and transmittance associated with monthly Tair changes. The chain length of n‐alkanols in Q. buckleyi shifted with seasonal LAI at different topographic positions. The unsaturation of fatty acids in both tree species decreased with increased seasonal Tair but showed topography sensitivity. Leaf‐level metabolites responded to understory microclimatic variables that were influenced by seasonality and topography. [ABSTRACT FROM AUTHOR]

Arid and semi-arid regions, which account for more than 30% of the Earth's land area, increasingly dominate the spatiotemporal trends in global carbon fluxes. The Horqin Sandy Land is a typical semi-arid fragile ecosystem in northern China. Understanding the components of the carbon budget in ecosystems under conditions of extreme soil moisture limitations provides a foundation for comprehending the carbon balance in semi-arid ecosystems. The seasonal and diurnal variations in soil respiration (Rs) in semi-mobile dune (SD) and meadow wetland (MW) ecosystems of the Horqin Sandy Land were examined, and the sources of CO2 emissions from Rs were identified using stable carbon isotopes. The responses of Rs and ecosystem respiration (Reco) to environmental temperature, moisture and leaf area index (LAI) were revealed. The results showed that on a seasonal scale, in SD with soil moisture content (Ms) below field capacity (FC), Ms had a greater influence on Rs than soil temperature (Ts) during the growing season. Changes in the LAI during the middle and late growth period affected Rs by altering root carbon supply. In MW, the most favorable Ms for Rs was near FC. The increase in LAI before mowing could effectively promote root and soil microbial respiration, and the decomposition of litter driven by Ts was the main form of Rs at this time. After mowing, root respiration and soil microbial respiration were the main processes contributing to CO2 emissions. On a daily scale, relative humidity (RH) dominated the Rs variation under dry conditions, whereas in other conditions, the Rs was adequately explained by temperature in SD and MW. The overall Reco was larger than Rs, but occasionally Rs was greater than Reco. The effects of temperature, moisture and LAI on Reco and Rs varied with growing season. Adding factors, such as ecosystem type, vegetation growth, water, and heat, to the carbon cycle model can improve predictions of carbon emissions, and aid in further management decisions in arid and semi-arid areas. [ABSTRACT FROM AUTHOR]

Cold stress is a critical factor affecting rice production worldwide. The application of cold-resistant agents may improve the cold resistance and yield of crops. To screen for suitable cold-resistant agents for machine-transplanted early rice, the effects of uniconazole, abscisic acid, and zinc-amino acids chelate and their spraying times (seed soaking stage, one leaf and one heart stage, two leaves and one heart stage, 7 days before the transplanting stage, and regreening stage) on the yield and cold resistance of machine-transplanted early rice were investigated. Moreover, the application method (spraying amount: 750 and 1125 g ha−1; spraying time: 7 days before the transplanting stage, transplanting stage, regreening stage, and transplanting stage and regreening stage) for the most suitable cold-resistant agent was optimized. The zinc-amino acids chelate was better than the other two cold-resistant agents for promoting rice tillering and increasing the leaf area index, dry matter weight, antioxidant enzyme activities (CAT, SOD, POD) and yield (i.e., 9.22% and 7.14% higher than uniconazole and abscisic acid, respectively), especially when it was applied in the regreening stage. The examination of spraying amounts and times indicated that the zinc-amino acids chelate dosage had no significant effect on the yield and cold resistance of early rice. However, the rice yield and antioxidant enzyme activities were highest when samples were sprayed once in the transplanting stage and the regreening stage. On the basis of the study results, 750 g ha−1 zinc-amino acids chelate applications in the transplanting and regreening stages of machine-transplanted early rice plants may be ideal for increasing cold stress resistance and yield. [ABSTRACT FROM AUTHOR]

Ecological environmental protection and poverty alleviation are of great significance for the study of human–land relationship coordination and sustainable development, and they have also been a focus of attention in China in the past few decades. In this study, we chose 13 contiguous poverty-stricken areas in China as the study area. Using MODIS Leaf Area Index (LAI) data from 2000 to 2020, the spatial–temporal changes in greenness were obtained using the Bayesian spatial–temporal model (BYM). Spatial autocorrelation was used to identify the spatial distribution of poverty using socio-economic statistical data. Driving factors, including natural factors, poverty factors, and the Grain for Green Policy (GTGP), and their influence on greenness were analyzed by using the Geodetector model for detecting spatial differentiation and factors' interactions. The results showed the following: (1) In 13 contiguous poverty-stricken areas (CPSAs) in China, 59% of the area presented an increasing trend of greenness. (2) In 2000, the high poverty levels with larger MPI values were widely distributed. After 20 years, the overall MPI value was lower, except in some northwest regions with increased MPI values. The spatial autocorrelation of poverty, which relates to the mutual influence of poverty in adjacent areas, also decreased. (3) In the study area, 65.24% of the regions showed strong synergistic effect between greening progress and poverty reduction in the interaction between poverty status and green development. With the improvement of greenness level, the positive correlation between poverty alleviation and ecological environment improvement has become increasingly close. (4) The impacts of interaction factors with the highest q values changed from temperature interacting with precision to regional division interacting with the Grain for Green Policy. The conclusions are that from 2000 to 2020, the impact of natural factors, geographical division, and poverty status on greenness has shown a decreasing trend; The effect of the Grain for Green Policy is gradually increasing; At the same time, the interaction and overlapping effects between the Grain for Green Policy and poverty were increasing. Taking into account the needs of ecological environment, poverty alleviation, and rural revitalization, this research provides valuable reference for formulating and implementing relevant policies based on the actual situation in different regions to promote harmonious coexistence between human-land relationship. [ABSTRACT FROM AUTHOR]

Accurate evapotranspiration (ET) estimation is crucial for understanding ecosystem dynamics and managing water resources. Existing methodologies, including traditional techniques like the Penman–Monteith model, remote sensing approaches utilizing Solar-Induced Fluorescence (SIF), and machine learning algorithms, have demonstrated varying levels of effectiveness in ET estimation. However, these methods often face significant challenges, such as reliance on empirical coefficients, inadequate representation of canopy dynamics, and limitations due to cloud cover and sensor constraints. These issues can lead to inaccuracies in capturing ET's spatial and temporal variability, highlighting the need for improved estimation techniques. This study introduces a novel approach to enhance ET estimation by integrating SIF partitioning with Photosynthetically Active Radiation (PAR) and leaf area index (LAI) data, utilizing the TL-LUE model (Two-Leaf Light Use Efficiency). Partitioning SIF data into sunlit and shaded components allows for a more detailed representation of the canopy's functional dynamics, significantly improving ET modelling. Our analysis reveals significant advancements in ET modelling through SIF partitioning. At Xiaotangshan Station, the correlation between modelled ET and SIFsu is 0.71, while the correlation between modelled ET and SIFsh is 0.65. The overall correlation (R2) between the modelled ET and the combined SIF partitioning (SIF(P)) is 0.69, indicating a strong positive relationship at Xiaotangshan Station. The correlations between SIFsh and SIFsu with modelled ET show notable patterns, with R2 values of 0.89 and 0.88 at Heihe Daman, respectively. These findings highlight the effectiveness of SIF partitioning in capturing canopy dynamics and its impact on ET estimation. Comparing modelled ET with observed ET and the Penman–Monteith model (PM model) demonstrates substantial improvements. R2 values for modelled ET against observed ET were 0.68, 0.76, and 0.88 across HuaiLai, Shangqiu, and Yunxiao Stations. Modelled ET correlations to the PM model were 0.75, 0.73, and 0.90, respectively, at three stations. These results underscore the model's capability to enhance ET estimations by integrating physiological and remote sensing data. This innovative SIF-partitioning approach offers a more nuanced perspective on canopy photosynthesis, providing a more accurate and comprehensive method for understanding and managing ecosystem water dynamics across diverse environments. [ABSTRACT FROM AUTHOR]

The Leaf Area Index (LAI) is a crucial structural parameter linked to the photosynthetic capacity and biomass of crops. While integrating machine learning algorithms with spectral variables has improved LAI estimation over large areas, excessive input parameters can lead to data redundancy and reduced generalizability across different crop species. To address these challenges, we propose a novel framework based on Bayesian-Optimized Random Forest Regression (Bayes-RFR) for enhanced LAI estimation. This framework employs a tree model-based feature selection method to identify critical features, reducing redundancy and improving model interpretability. A Gaussian process serves as a prior model to optimize the hyperparameters of the Random Forest Regression. The field experiments conducted over two years on maize and wheat involved collecting LAI, hyperspectral, multispectral, and RGB data. The results indicate that the tree model-based feature selection outperformed the traditional correlation analysis and Recursive Feature Elimination (RFE). The Bayes-RFR model demonstrated a superior validation accuracy compared to the standard Random Forest Regression and Pso-optimized models, with the R2 values increasing by 27% for the maize hyperspectral data, 12% for the maize multispectral data, and 47% for the wheat hyperspectral data. These findings suggest that the proposed Bayes-RFR framework significantly enhances the stability and predictive capability of LAI estimation across various crop types, offering valuable insights for precision agriculture and crop monitoring. [ABSTRACT FROM AUTHOR]

We studied the allelopathic effects of mulch and aqueous extracts of Rice (Oryza sativa L.) straw and on growth of 8-weeds: Phalaris minor L., Cynodon dactylon L., Chenopodium album L., Rumex denticulate L., Anagalis arvensis L., Melilotus spp L., Parthenium hysterophorus L. and Cyperus rotundus (L.) was analyzed. Rice straw mulch at 4.0 t/ha and rice aqueous solution (10 g/L) spray significantly reduced these weeds density (Number/m2) (54, 55, 66, 79, 85, 73, 54 and 39 during 2020-21) respectively, than control. Rice straw mulch 4 t/ha followed by (metsulfuron methyl 4 g a.i./ha + clodinafop propargyl 60 g a.i./ha) and clodinafop propargyl 75 % of 60 g a.i./ha + rice aqueous solution (10 g/L) spray significantly reduced the density of test weeds. Density of grass weeds decreased in zero-till, but broad leaved weeds decreases in conventional tillage. Wheat growth (dry matter accumulation: g/plant), number of tillers per m, leaf area index (LAI) and yield) significantly increased by rice straw mulch at 4 t/ha followed by rice aqueous solution (10 g/L) spray than control. [ABSTRACT FROM AUTHOR]

BACKGROUND: The simultaneous prediction of yield and maturity date has an important impact on ensuring food security. However, few studies have focused on simultaneous prediction of yield and maturity date for wheat–maize in the North China Plain (NCP). In this study, we developed the prediction model of maturity date and yield (PMMY) for wheat–maize using multi‐source satellite images, an Agricultural Production Systems sIMulator (APSIM) model and a random forest (RF) algorithm. RESULTS: The results showed that the PMMY model using peak leaf area index (LAI) and accumulated evapotranspiration (ET) has the optimal performance in the prediction of maturity date and yield. The accuracy of the PMMY model using peak LAI and accumulated ET was higher than that of the PMMY model using only peak LAI or accumulated ET. In a single year, the PMMY model had good performance in the prediction of maturity date and yield. The latitude variation in spatial distribution of maturity date for WM was obvious. The spatial heterogeneity for yield of wheat–maize was not prominent. Compared with 2001–2005, the maturity date of the two crops in 2016–2020 advanced 1–2 days, while yield increased 659–706 kg ha−1. The increase in minimum temperature was the main meteorological factor for advance in the maturity date for wheat–maize. Precipitation was mainly positively correlated with maize yield, while the increase in minimum temperature and solar radiation was crucial to the increase in yield. CONCLUSION: The simultaneous prediction of yield and maturity can be used to guide agricultural production and ensure food security. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

BACKGROUND: The integration of inorganic and organic fertilizers is increasingly being recommended to address the demand for sustainable cotton cultivation and to mitigate the ecological impacts of reliance on inorganic fertilizers. However, the nuanced effects of this combined fertilization approach on soil quality, cotton growth, yield, and their interaction mechanisms, remain unclear. METHOD: To elucidate this, a 2‐year field trial (2022–2023) was conducted, incorporating five fertilization treatments: low inorganic fertilizer (BI1), high inorganic fertilizer (BI2), organic fertilizer (BO), combined low inorganic and organic fertilizer (BIO1), and combined high inorganic and organic fertilizer (BIO2). This study aimed to evaluate the influence of these treatments on soil quality, cotton growth, and yield. RESULTS: The results indicate that the BO treatment significantly enhanced plant height growth rate, and BIO1 treatment increased leaf area index and dry matter accumulation growth rate. Critical soil parameters such as alkali‐hydrolyzed nitrogen and available potassium emerged as pivotal determinants of soil quality over the trial period, corresponding to soil quality index (SQI) values of 0.482 and 0.478, and yields of 7506.19 kg ha−1 and 6788.02 kg ha−1, respectively. Water productivity reached optimum levels at SQI values of 0.461 and 0.462, with corresponding efficiencies of 13.31 kg (ha mm)−1 and 12.16 kg (ha mm)−1. Partial least squares path modeling revealed that integrating organic fertilizer with reduced inorganic fertilizer usage significantly boosts cotton yield by enhancing soil quality (path coefficient: 0.842). CONCLUSION: In conclusion, this integrated fertilization strategy not only improves soil health but also increases agricultural productivity. It presents a promising approach for optimizing crop yields while fostering sustainable agricultural practices. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

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Enhancing irrigation and nitrogen fertilizer application has become a vital strategy for ensuring food security in the face of population growth and resource scarcity. A 2-year experiment was conducted to determine to investigate the effects of different irrigation lower limits and nitrogen fertilizer application amounts on millet growth, yield, water use efficiency (WUE), N utilization, and inorganic nitrogen accumulation in the soil in 2021 and 2022. The experiment was designed with four irrigation lower limits, corresponding to 50%, 60%, 70%, and 80% of the field capacity (FC), referred to as I50, I60, I70, and I80. Four nitrogen fertilizer application were also included: 0, 50, 100, and 150 kg·hm−2 (designated as F00, F50, F100, and F150), resulting in a total of 16 treatments. Binary quadratic regression equations were established to optimize the irrigation and nitrogen application. The results demonstrated that the plant height, stem diameter, leaf area index, aboveground biomass, yield, spike diameter, spike length, spike weight, WUE, and nitrogen agronomic efficiency for millet initially increased before subsequently decreasing as the irrigation lower limit and nitrogen fertilizer application increased. Their maximum values were observed in the I70F100. However, the nitrogen partial factor productivity (PFPN) exhibited a gradual decline with increasing nitrogen application, reaching its peak at F50. Additionally, PFPN displayed a pattern of initial increase followed by a decrease with rising irrigation lower limits. The accumulation of NO3−-N and NH4+-N in the 0~60 cm soil layer increased with the increase of nitrogen fertilizer application in both years, while they tended to decrease as the irrigation lower limit increased. An optimal irrigation lower limit of 64% FC to 74% FC and nitrogen fertilizer application of 80 to 100 kg ha−1 was recommended for millet based on the regression equation. The findings of this study offer a theoretical foundation and technical guidance for developing a drip irrigation and fertilizer application for millet cultivation in Northeast China. [ABSTRACT FROM AUTHOR]

It is not known whether both reducing tiller-fertilizer and increasing panicle-fertilizer can significantly increase grain yield under mechanical deep placement. The two-year field experiment was conducted to access the effects of optimal nitrogen fertilization including reducing tiller-fertilizer, increasing panicle-fertilizer, with the method of mechanical deep placement on grain yield and its physiological traits of rice, in 2019 and 2020. The experimental materials were selected with hybrid rice Wufengyou615 (WFY615) and inbred rice Yuxiangyouzhan (YXYZ). There were six experiment treatments, i.e., no any fertilization (H1); traditional surface broadcast fertilization (SB) (90 kg N ha−1 as basal fertilization (BF) and 60 kg N ha−1 as tillering fertilizer (TF), namely, BF 90 kg N (SB) + TF 60 kg N (SB), (H2); BF 90 kg N (SB) + TF 45 kg N (DP, deep placement) + FF (flowering fertilizer) 7.5 kg N (SB), (H3); BF 90 kg (SB) + TF 45 kg N (DF) + FF 15 kg N (SB), (H4); BF 90 kg N(SB) + TF 30 kg N (DP) + FF 7.5 kg N (SB), (H5); BF 90 kg N (SB) + TF 30 kg N (DP) + FF 15 kg N (SB), (H6). The results showed that mean grain yield of WFY615 and YXYZ for H4 was 10.57 t ha−1 and 10.42 t ha−1, which was 14.58% and 7.49% higher than H2, respectively. The main reason was due to the increase of productive panicle per ha, spikelet per panicle and grain filling percentage. The highest total dry matter of WFY615 and YXYZ at heading (HS) and mature stages (MS) was for H4, which was 9.24, 15.97, 11.65, and 14.71 t ha−1, respectively. There was 31.09, 25.96, 41.73, and 20.58% higher total dry matter material of WFY615 and YXYZ for H4 than H2 at HS and MS, respectively. The largest leaf area index of H4 was also found at HS and fifteen days after HS for two rice cultivars, which was 6.24, 8.79, 6.09, and 8.29, respectively. The H4 treatment had the largest net photosynthetic rate, followed by H3 and H2, while the least net photosynthetic rate was recorded for H1. In addition, significant improvements were also founded in chlorophyll content, glutamate synthase, and nitrate reductase activities of sword leaves at HS for H4. Therefore, the fertilizer management can be regarded as one of high-efficiency fertilization method with 90 kg N ha−1 basal fertilizer by surface broadcast plus 45 kg N ha−1 tillering fertilizer under mechanical deep placement and 15 kg N ha−1 flowering fertilizer by surface broadcast. [ABSTRACT FROM AUTHOR]

Damming profoundly affects downstream flow‐sediment regimes, altering channel bar dynamics and thereby affecting floods and riverine biodiversity. Here, we investigate the response of bars to upstream damming by examining patterns, mechanisms, and impacts in the Middle Yangtze River (∼1,000 km). Over a decade of post‐damming observational data reveal substantial increases in bar revegetation and Leaf Area Index. Shorter flood duration and stable bar size collectively drive bar greening. Consequently, denser vegetation has slowed flow velocity by 17% ± 2% and increased flow resistance by 21% ± 5%, offsetting the water‐level decrease from channel expansion due to scouring and even causing a slight rise in floodwater levels. Furthermore, damming has markedly altered river connectivity, thermal regimes, and solute dynamics, detrimentally affecting fish habitats and aquatic life. These findings, along with refined river stage simulation considering flow‐sediment‐vegetation interactions, facilitate sustainable reservoir operation and river management in big river systems. Plain Language Summary: Dams significantly impact downstream river systems, affecting bar dynamics, flood risks, and biodiversity. This study focuses on the Middle Yangtze River, which spans nearly 1,000 km, to show how upstream damming changes vegetation growth on bars. Over a decade, we observe increases in vegetated area and Leaf Area Index, with shorter floods and stable bar area leading to greener bars. Denser vegetation slows flow velocity and increases flow resistance, which balances the water level changes caused by channel deepening. Damming also disrupts river connectivity and water quality, harming fish habitats and aquatic life. These insights can help improve reservoir management and river conservation in the face of global change. Key Points: Damming operation boosts downstream bar greeningShorter flood duration and stable bar size drive plant expansionChannel bar greening increases flow resistance, reshaping flood and ecological patterns [ABSTRACT FROM AUTHOR]

Climate change and large‐scale ecological restoration programs have profoundly influenced vegetation greening and gross primary productivity (GPP) in China's drylands. However, the specific pathways through which climatic factors and vegetation greening influence GPP remain poorly understood. This study examines the spatiotemporal changes in GPP across China's drylands from 2001 to 2020 and investigates the direct and indirect effects of climatic factors and leaf area index (LAI) on GPP. The results reveal that the overall improvement in vegetation cover has positively increased GPP in these regions. Although the direct effects of climatic factors on GPP are minimal, they exert a substantial indirect effect by regulating vegetation growth, highlighting that LAI is a key intermediary in mediating the effects of climatic factors on GPP. Furthermore, these complex interactions vary significantly along the aridity gradient. This study emphasizes the necessity of comprehensively considering the intricate interactions among multiple climate and vegetation factors. Plain Language Summary: China's drylands have undergone significant vegetation greening and ecological restoration, characterized by transitions toward forests, grasslands, and croplands. These changes have greatly enhanced gross primary productivity (GPP), a key indicator of ecosystem health and functionality. This study reveals that the increase in GPP results from the combined effects of climate change and improved vegetation cover. Although climatic factors like temperature, precipitation, and solar radiation directly affect GPP to a lesser extent, they indirectly boost it by altering vegetation growth conditions. Among the various factors, the increase in vegetation cover has the most direct and substantial positive effect on GPP, especially in semi‐arid and dry sub‐humid regions, where ecological restoration efforts are concentrated. Furthermore, the study indicates that the center of gravity for vegetation productivity in China's drylands is gradually shifting westward, and predicts that most areas will maintain the current trend of increasing vegetation productivity. Overall, under the dual impetus of climate change and greening initiatives, the vegetation in China's drylands has exhibited strong vitality. This not only benefits ecological environment improvement but also supports climate change mitigation and contributes to carbon peaking and carbon neutrality goals. Key Points: The trend of gross primary productivity in China's drylands has shown a marked increase, especially after 2011The leaf area index serves as a crucial intermediary in modulating the indirect effects of climatic factors on gross primary productivityThe complex interactions between climatic factors and the leaf area index on gross primary productivity vary along the aridity gradient [ABSTRACT FROM AUTHOR]

Evaluating vegetation restoration sustainability is crucial to avoid conflicts between human water demand and ecosystem consumption, especially with the surge in leaf area index (LAI) due to revegetation projects in China. However, current methods for assessing vegetation sustainability are still limited. Here, we developed a sustainability index for vegetation systems (vegetation sustainability index, VSI) from water demand and supply aspects based on reliability, resilience, and vulnerability in arid and semi‐arid areas. VSI was built upon a vegetation overplanting index (dLAI) which is the difference between the maximum LAI supported by precipitation (LAIp) and the observed LAI (LAIobs). A case study in the mountainous area of the Haihe River basin reveals gradually declining VSI after 2000. Forests are the primary vegetation type in areas with VSI < 0.5, indicating decreased sustainability due to overplanting. The framework of VSI can be a useful tool for planning and implementing vegetation restoration strategies in arid and semi‐arid regions. Plain Language Summary: Vegetation restoration stands out as a highly effective ecological engineering solution for overcoming land degradation and climate change mitigation with widespread implementation. However, overplanting can threaten the sustainability of vegetation systems, leading to increased evaporative water consumption, decreased catchment water yield and soil moisture in arid and semi‐arid regions. Evaluating the sustainability of vegetation restoration is vital for managing conflicts between human water demand and ecosystem needs in the context of increasing vegetation coverage resulting from revegetation efforts in China. Previous studies suggested using numerous variables to evaluate the sustainability, making it difficult to present or explain clearly. This study addresses the limitations of current methods for assessing vegetation sustainability by developing a novel sustainability index for vegetation systems (VSI) in arid and semi‐arid regions. The VSI framework incorporates aspects of water demand and supply based on reliability, resilience, and vulnerability. A case study in the mountainous Haihe River basin indicates a gradual decline in VSI after 2000, which may be attributed to the overplanting. The VSI framework offers a valuable tool for guiding vegetation restoration strategies in arid and semi‐arid regions, improving the management of water resources and ecosystem sustainability. Key Points: A sustainability index for vegetation systems based on water demand and supply aspects was developedWater demand and supply were represented by the observed leaf area index (LAI) and maximum LAI supported by precipitationDecreased sustainability was found due to overplanting in forests [ABSTRACT FROM AUTHOR]

A multi-season research trial entitled 'crop residue management effects on yield and water productivity of summer mung bean (Vigna radiata L.) under different irrigation regimes in Indian Punjab' was conducted at Punjab Agricultural University (PAU), Regional Research Station (RRS), Bathinda, during rabi 2020 and 2021. The field experiment was conducted in a split-plot layout with nine treatment combinations and replicated thrice. The treatments consisted of T1 (no wheat residue along with tillage), T2 (leftover wheat residue with zero tillage), and T3 (incorporated wheat residue along with tillage) in main plots and irrigation regimes viz., I1 (vegetative growth and flowering stage), I2 (vegetative growth, flowering, and pod filling stage) and I3 (vegetative growth, flowering, pod formation and pod filling stage) in sub-plots, respectively. The growth and yield attributing characters were significantly higher under T3 than T1 but statistically at par with T2 during both years. An increase of 24.1% and 19.0% in grain yield was found in residue incorporation (T3) and residue retention (T2) over residue removal (T1), respectively. Maximum crop and irrigation water productivity was observed under T3 due to reduced water use and increased yield. Among the irrigation regimes, the I3 recorded significantly higher grain yield (0.70 and 0.79 t ha− 1) than I1. It was at par with I2 during both years due to higher irrigation frequency at the pod formation and pod filling stage. Crop water productivity (CWP) was higher under I3, whereas irrigation water productivity (IWP) was higher under I1 during both years. Additional irrigation at the pod-filling stage increased the grain yield by 36.5%, and two additional irrigations at the pod-formation and pod-filling stage further increased yield by 46.2% compared to only two irrigations at the vegetative and flowering stages. The treatment combinations of T2I2 and T3I2 outperformed T1I3 in terms of growth and yield attributing characters viz. plant height, dry matter accumulation (DMA), leaf area index (LAI), pods plant− 1, seeds pod− 1, and 1000-seed weight, which resulted in higher grain yield in these treatment combinations over T1I3. Applying crop residue can help minimize water use and increase crop water productivity. So, retaining crop residue in summer mung bean resulted in saving irrigation water due to lesser evapotranspiration from the soil surface. [ABSTRACT FROM AUTHOR]

The ability to access physiologically driven signals, such as surface temperature, photochemical reflectance index (PRI), and sun-induced chlorophyll fluorescence (SIF), through remote sensing (RS) are exciting developments for vegetation studies. Accessing this ecophysiological information requires considering processes operating at scales from the top-of-the-canopy to the photosystems, adding complexity compared to reflectance index-based approaches. To investigate the maturity and knowledge of the growing RS community in this area, COST Action CA17134 SENSECO organized a Spatial Scaling Challenge (SSC). Challenge participants were asked to retrieve four key ecophysiological variables for a field each of maize and wheat from a simulated field campaign: leaf area index (LAI), leaf chlorophyll content (Cab), maximum carboxylation rate (Vcmax,25), and non-photochemical quenching (NPQ). The simulated campaign data included hyperspectral optical, thermal and SIF imagery, together with ground sampling of the four variables. Non-parametric methods that combined multiple spectral domains and field measurements were used most often, thereby indirectly performing the top-of-the-canopy to photosystem scaling. LAI and Cab were reliably retrieved in most cases, whereas Vcmax,25 and NPQ were less accurately estimated and demanded information ancillary to RS imagery. The factors considered least by participants were the biophysical and physiological canopy vertical profiles, the spatial mismatch between RS sensors, the temporal mismatch between field sampling and RS acquisition, and measurement uncertainty. Furthermore, few participants developed NPQ maps into stress maps or provided a deeper analysis of their parameter retrievals. The SSC shows that, despite advances in statistical and physically based models, the vegetation RS community should improve how field and RS data are integrated and scaled in space and time. We expect this work will guide newcomers and support robust advances in this research field. [ABSTRACT FROM AUTHOR]

The neglected use of micronutrient greatly hampered the productivity of oilseed crop. Thus, to determine the optimal dose of S and B on toria under rice-fallow system, an experiment was carried out with five doses of S i.e. 0, 10, 20, 30 and 40 kg/ha and 3 doses of B i.e. 0, 1 and 2 kg/ha for two consecutive years (2018–19 and 2019–20). The plant growth parameters, i.e. plant height, dry matter accumulation, leaf area index and crop growth rate, and yield attributing parameters, i.e. siliqua/plant and seeds/siliqua, were greatly enhanced through application of S @ 30 kg/ha as well as B@ 2 kg/ha. The higher seed yield (10.11q/ha), stover yield (22.00q/ha) and oil yield (3.86 q/ha) were registered with the application of S @ 30 kg/ha + B @ 2 kg/ha, but it was at par with S@ 30 kg/ha + B @ 1 kg/ha. The combined application of S@ 30 kg/ha + B @ 2 kg/ha also noted higher plant nutrient uptake of N, P, K including S and B closely followed by S@ 30 kg/ha + B @ 1 kg/ha. But in terms of economics, conjugate application of S@ 30 kg/ha + B @1 kg/ha showed maximum monetary benefit with highest B:C (2.24). The pearson correlation indicated strong and positive correlation of seed yield with growth and yield parameters as well as nutrient uptake by plants. Regression analysis revealed that each one unit increase in plant dry matter accumulation showed an increase in seed yield by 4.56 unit and each one increase uptake in N, P, K, S and B showed an increase in seed yield by 0.19, 0.63,0.40,0.55 and 0.05, respectively. Henceforth, considering all the factors the combination of S@ 30 kg/ha and B@1 kg/ha may be recommended under the late sown toria in rice-fallow system. [ABSTRACT FROM AUTHOR]

Background: The pervasively imprudent practices of irrigation and nitrogen (N) application within Oasis Cool Irrigation zones have led to significant soil nitrogen loss and a marked decrease in water and nitrogen use efficiency. Methods: To address this concern, a comprehensive field experiment was conducted from April to September in 2023 to investigate the impact of varying degrees of water and fertilization regulation strategies on pivotal parameters including potato yield, quality, nitrogen balance, and water-nitrogen use efficiency. The experimental design incorporated two water deficit degrees at potato seedling (W1, 55%-65% of Field Capacity (FC); W2, 45%-55% of FC), and four distinct nitrogen application gradients (N0, 0 kg ha-1 of N; N1, 130 kg ha-1 of N; N2, 185 kg ha-1 of N; N3, 240 kg ha-1 of N). A control was also included, comprising N0 nitrogen application and full irrigation (W0, 65%-75% of FC), totally eight treatments and one check. Results: The results indicated that the tuber yield, plant dry matter accumulation, plant height, plant stem, and leaf area index increased with higher nitrogen fertilizer application and irrigation volume. However, tuber starch content, vitamin C, and protein content initially increased and then decreased, while reducing sugar content consistently decreased. Except for W1N2 treatment, the irrigation water use efficiency increased as the N application rate rose, while the nitrogen partial factor productivity, crop nitrogen use efficiency and soil nitrogen use efficiency decreased with an increase in N fertilizer application. The W1N2 treatment resulted in a higher yield (43.16 t ha-1), highest crop nitrogen use efficiency (0.95) and systematic nitrogen use efficiency (0.72),while maintaining moderate levels of soil nitrate and ammonium nitrogen. Conclusion: Therefore, through the construction of an integrated evaluation index (IEI), the W1N2 treatment of mild water deficit (55%-65% of FC) at potato seedling combined with the medium nitrogen application (185 kg ha-1 of N) has the highest IEI (0.978), it was recommended as the optimal water-nitrogen regulation and management strategies to facilitate high-yield, high-efficiency, and environmentally sustainable potato production in the cold and arid oasis areas of northwest China. [ABSTRACT FROM AUTHOR]

Recent studies have suggested that drought projections using Palmer drought severity index (PDSI) and standardized evapotranspiration precipitation index (SPEI) may overestimate drought severity. This overestimation occurs because the potential evapotranspiration (PET) calculations fail to consider the interactive effects of vegetation responses such as increased leaf area index (LAI) and constrained stomatal conductance, which are influenced by elevated atmospheric CO2 concentrations ([CO2]). To address this issue, our study replaced the traditional Penman‐Monteith (PM) equation with a recently proposed PET equation that includes the effects of changing [CO2] and LAI to assess droughts at monthly scale in the Upper Yangtze River basin, which experiences the vegetation greening. The findings indicated a consistent increasing trend in drought conditions with minimal discrepancy between the two equations over the historical period (1986–2017). This consistency arises because the water‐saving effects of increased [CO2] and the greening effects of rising LAI largely counterbalance each other. However, for the future period (2018–2100), projections using PM equation predicted an intensification of drought conditions. In contrast, the improved SPEI indicated no significant drought variations, and the improved PDSI suggested a wetting trend. This divergence can be attributed to the water‐saving effects increasingly outweighing the greening effects, as PET shows a decreasing sensitivity to LAI with LAI increasing, but maintains a near‐constant sensitivity to elevated [CO2]. Consequently, the indices based on PM equation tend to overestimate future drought severity. Overall, this study demonstrates that the new PET estimation method is more capable of responding to the changing environment. Plain Language Summary: This study enhances drought projections by incorporating the effects of elevated atmospheric CO2 concentrations ([CO2]) and increased vegetation, as indicated by the leaf area index (LAI), on potential evapotranspiration (PET). Historically, the water‐saving effect of elevated [CO2] and the greening effect due to increased vegetation have counterbalanced each other, resulting in minimal discrepancies between traditional and newly developed methods. However, our innovative approach demonstrates that with rising LAI, the sensitivity of PET to LAI diminishes, while its sensitivity to CO2 remains relatively constant. This finding suggests that in future scenarios with concurrent increases in [CO2] and LAI, the water‐saving effect will potentially dominate the greening effect, leading traditional methods to overestimate future drought severity. Consequently, this new method offers a more accurate framework for predicting changes in future environmental conditions. Key Points: Palmer drought severity index (PDSI) and standardized evapotranspiration precipitation index (SPEI) are improved using a newly proposed potential evapotranspiration equation that considers the effects of atmospheric CO2 concentrations and leaf area index (LAI) changeThe improved PDSI and SPEI are more applicable in a changing environment and characterize the spatial differences of vegetation variationThe impacts of increased atmospheric CO2 concentrations and LAI must be factored into the assessment of meteorological drought trends [ABSTRACT FROM AUTHOR]

Introduction: Due to limited arable land resources, intercropping has emerged as an efficient and sustainable production method for increasing total grain yield per unit land area. Maize-soybean strip intercropping (MSSI) technology is being widely promoted and applied across China. However, the combination of optimal density for achieving higher production efficiency of both soybean and maize remains unclear. The objective of this study was to evaluate the differences in yield, economic benefits, land, and nitrogen (N) efficiency in MSSI systems under different densities. Methods: Five maize/soybean density combinations (67,500/97,500 plants ha-1, D1; 67,500/120,000 plants ha-1, D2; 67,500/142,500 plants ha-1, D3; 60,000/142,500 plants ha-1, D4; 52,500/142,500 plants ha-1, D5) were set under the same N input in the field experiment. Results and discussion: The results demonstrated that optimizing the density in the intercropping system could enhance production efficiency. Increasing the density of soybean and maize significantly increased the total grain yield (D3 > D2 > D1 > D4 > D5). The D3 treatment, exhibiting the best comprehensive performance, also promoted increases in leaf area index, dry matter accumulation, and N absorption and utilization. Path analysis indicated that density had the most substantial impact on maize yield, while grain number had the greatest influence on soybean yield, with contribution rates of 49.7% and 61.0%, respectively. These results provide valuable insights into optimal field density for summer planting in MSSI, facilitating its wider adoption. [ABSTRACT FROM AUTHOR]

Ammonia (NH3) has received considerable attention as a major reduced nitrogen. However, accurate estimates of the deposition amount are difficult due to its complex behavior characterized by bidirectional exchange between the atmosphere and the surface. We observed the vertical profile of NH3 concentration in a deciduous forest in Japan for 1 year to further advance the studies on NH3 bidirectional exchange in Asia, especially focusing on the process near the forest floor. The observation period lasted from September 29, 2020, to September 28, 2021, including leafy and leafless periods. Using the denuder sampling technique, we measured NH3 concentration in the forest at three heights (above the forest canopy, 30 m, and near the forest floor, 2 m and 0.2 m). NH3 concentrations tended to be highest at the top of the canopy (30 m). Focusing on the concentration near the forest floor, the concentrations at 0.2 m were frequently higher than those at 2 m regardless of the leafy and leafless period, thus suggesting NH3 emissions from the forest floor. NH3 concentration near the forest floor showed strong positive correlations with air temperature during the leafy period. The NH3 emissions from the forest floor during the leafy period were possibly due to the decomposition of leaf litter with increased air temperature. The decrease in leaf area index might induced the increase in NH3 concentration and emission. NH3 emission during the leafless period was also possibly dependent on the state of the deposition surface, apart from air temperature, relative humidity, and leaf area index. [ABSTRACT FROM AUTHOR]

Timely estimations of leaf chlorophyll content (LCC) and leaf area index (LAI) can provide critical information for potato field management. We employed a hybrid method that integrated machine learning with a radiative transfer model to estimate potato growth parameters. A look-up table was generated using the PROSAIL model, which was used as an unlabelled sample set. Measurements were taken from a potato field, and the data were labelled according to growth period and variety. Then, training samples for potato LCC, LAI, and canopy chlorophyll content (CCC) were selected from the simulated unlabelled sample set using the Euclidean distance-based diversity algorithm based on different labelled data sets. The training sample size required to accurately estimate the parameters varied considerably depending on the parameter, variety, and growth period, despite using the same labelled data set. Moreover, our results indicate that the growth period has a substantial impact on model accuracy and needs to be considered when constructing the labelled data set. The study results indicate that the hybrid method combined with the radiative transfer model and active learning can effectively select informative training samples from a data pool and improve the accuracy of potato parameter estimation, which provides a valid tool for accurately monitoring crop growth and growth health. [ABSTRACT FROM AUTHOR]

Spectral and texture features play important roles in plantation leaf area index (LAI) estimation, and their combination may enhance LAI inversion accuracy. Furthermore, research on the impact of different machine learning (ML) models on their hyperparameter combinations and splitting ratios remains challenging. In our study, experiments based on spectral and textural features of GF-6 WFV data were conducted on Eucalyptus grandis plantation forests in Huangmian Town, Guangxi, China. ML methods such as multiple stepwise regression (MSR), random forest (RF), back-propagation neural network (BPNN), and support vector regression (SVR) were mainly utilized to perform model hyper-parameter tuning and split-ratio analysis in order to estimate the LAI. The results of the study showed that spectral and gray level co-occurrence matrix (GLCM) texture features were very sensitive to changes in Eucalyptus grandis LAI. The accuracy of combining the two was 10% higher than when they were not combined. Furthermore, it was found that the nonlinear methods (RF, BPNN, and SVR) outperformed the linear method (MSR), with the average 2 Rmax of the nonlinear model being 26% higher than that of the linear model, and the RMSE value being 29% lower than that of the linear model. In addition, by analyzing different combinations of features, model hyperparameter fine-tuning, and splitting ratios in the nonlinear model, it was found that the splitting ratios of different combinations of model hyperparameters have a great impact on the accuracy of the model. A total of 12 out of 21 data sets showed high accuracy and stability at a split ratio of 8.5:1.5 (ratio of 0.85), with the best-performing RF model differing from the lowest by 91% for 2 max R and 39% for 2 Rstd. Combining spectral and texture features provides highly accurate inversion data. Model hyper-parameter fine-tuning and segmental scale tuning can facilitate the application of inversion data to fully utilize the optimal performance of the ML model. [ABSTRACT FROM AUTHOR]

Water scarcity, over-fertilization, and improper crop management practices severely limit the sustainable cultivation of licorice (Glycyrrhiza uralensis Fisch) in the arid regions of Xinjiang. To elucidate the impacts of integrated water and fertilizer management on the growth characteristics and bioactive components (glycyrrhizic acid and liquiritin) of four-year-old licorice plants, a comprehensive four-year field experiment was conducted from 2019 to 2022. The experiment included four irrigation levels (W1: 2500 m³/ha, W2: 4000 m³/ha, W3: 5500 m³/ha, W4: 7000 m³/ha) and four fertilization rates (F1: 305 kg/ha, F2: 610 kg/ha, F3: 915 kg/ha, F4: 1220 kg/ha), following a completely randomized design. Results indicated that both irrigation and fertilization significantly influenced plant height, root length, root weight, root diameter, leaf area index, and root-to-shoot ratio. The optimal growth characteristics were observed under the W2F2 treatment. The contents of glycyrrhizic acid and liquiritin varied significantly among different water and fertilizer treatments, with the highest levels observed under the W2F2 treatment. Excessive irrigation (W4) and over-fertilization (F4) led to a decrease in these bioactive components. A comprehensive evaluation of the growth characteristics and bioactive components revealed that the ideal irrigation and fertilization parameters were 4000 m³/ha and 610 kg/ha, respectively. These parameters optimized plant development and bioactive component accumulation while ensuring efficient resource use. This study provides scientific evidence for optimizing irrigation and fertilization strategies to enhance licorice yield in arid regions, thereby supporting sustainable agricultural practices and improving economic benefits. [ABSTRACT FROM AUTHOR]

Forest biomass estimates are required for many applications, including accounting for the role of forests in the global carbon cycle, supporting sustainable forest management and making informed decisions. For all these applications and others, accurate and reliable forest biomass estimates are required. This study evaluated the potential of Sentinel-2 data for predicting and mapping aboveground biomass (AGB) of a dry Afromontane Forest in Tigray, Northern Ethiopia. Multiple linear regression was employed to model the relationship between AGB and Sentinel-2-derived spectral variables. The evaluation criteria for best-fit model selection were based on the coefficient of determination (R2), root mean square error (RMSE, %), and bias (Bias, %). All the spectral variables evaluated here were significantly correlated with AGB (p < 0.01). The model that includes a fraction of photosynthetically active radiation (FAPAR), leaf area index (LAI), Band 2 and Band 3 as predictor variables provided the best predictive performance for AGB in the study area (R2 = 0.38, RMSE = 13.62 Mg ha−1 and Bias = -3.10 Mg ha−1). The predicted AGB of the study area ranges from 0.1 to 141.8 Mg ha−1, with a mean value of 12.4 Mg ha−1. The results from this study suggested that Sentinel-2 data can be potentially applied for estimating and mapping AGB in dry Afromontane forests. [ABSTRACT FROM AUTHOR]

Urban trees play an important role in mitigating effects of climate change and provide essential ecosystem services. However, the urban environment can stress trees, requiring the use of effective monitoring methods to assess their health and functionality. The objective of this study, which focused on four deciduous tree species in Rennes, France, was to evaluate the ability of hybrid inversion models to estimate leaf chlorophyll content (LCC), leaf area index (LAI), and canopy chlorophyll content (CCC) of urban trees using eight Sentinel-2 (S2) images acquired in 2021. Simulations were performed using the 3D radiative transfer model DART, and the hybrid inversion models were developed using machine-learning regression algorithms (random forest (RF) and gaussian process regression). Model performance was assessed using in situ measurements, and relations between satellite data and in situ measurements were investigated using spatial allocation (SA) methods at the pixel and tree scales. The influence of including environment features (EFs) as model inputs was also assessed. The results indicated that random forest models that included EFs and used the pixel-scale SA method were the most accurate with R2 values of 0.33, 0.29, and 0.46 for LCC, LAI, and CCC, respectively, with notable variability among species. [ABSTRACT FROM AUTHOR]

A three-year multi-location trial on the farmer's field was conducted in West Bengal, India to evaluate the impact of integrated nutrient management (INM) on the performance of kharif rice cv. Swarna Sub 1. The experiment employed a randomized block design (RBD) across eleven farmers' fields with similar soil properties, texture, topography, and historical fertilizer application patterns. Three treatments were tested: T1 – common practice of applying N:P:K @ 55:32:27 kg ha−1, T2 – soil test-based application (STA) of ∼75% recommended N dose through chemical fertilizer + ∼ 25% through Azolla, and T3 – soil test-based application of ∼ 75% recommended N dose through chemical fertilizer + 20% N from vermi-compost + 5% N through Azolla. The result of the experiment revealed that the T3 exhibited significant improvements in plant height, leaf area index (LAI), effective tillers meter−2, number of panicles plant−1, and 1000-grain weight compared to T2 and T1. Among the different nutrient management practices, the T3 recorded maximum rice grain productivity than the T1, and T2, Further, the benefit-cost ratio was noticed significantly higher under T3 (1.29) followed by T2 (1.27) and T1 (1.23). The soil organic carbon (SOC), available nutrient was enhanced by the soil test-based application of ∼75% recommended N dose through chemical fertilizer + 20% N from vermicompost + 5% N through Azolla. Thus, our study indicated that the balanced nutrient application through combined sources (T3) could improve crop growth; and sustain rice productivity, besides enhancing the farm income, and soil different soil properties. [ABSTRACT FROM AUTHOR]

Silicon (Si) is a versatile nutrient that plays an instrumental role in mitigating biotic and abiotic stresses besides improving growth and yield of graminaceous crops. We hypothesized that application Si would significantly improve productivity and resilience of wheat. Hence, the objectives were a) to assess the impact of Si application on wheat growth, productivity, nutrient uptake, water use efficiency, and b) to determine the potentiality of Si in mitigating lodging and aphid density. Therefore, we conducted a field experiment with five levels of Si (0, 2, 4, 6, and 8 g Si liter−1) at three growth stages (crown root initiation, tillering, and jointing stage) using a factorial randomized block design replicated three times. The results showed that increasing Si doses positively influenced plant height, dry matter accumulation (DMA), leaf area index (LAI), yield, and nutrient uptake. The highest grain and straw yield were observed with 8 g Si liter−1, followed by 6 g Si liter−1, while the control had the lowest yields. With 8 g Si liter−1, grain yield, straw yield, and Si uptake increased by 10.5%, 13.5%, and 26.3%, respectively, compared to the control. Additionally, Si application at 8 g Si liter−1 significantly reduced the density of S. avenae (aphids) by 81.4% and lodging by 75.1% compared to the control. Overall, the study demonstrated that increasing Si doses enhanced various growth and yield parameters, with 8 g Si liter−1 and 6 g Si liter−1 showing superior results. Among the growth stages, foliar application of Si during the tillering stage exhibited better performance in terms of growth, yield, and nutrient uptake in wheat. Therefore, the study concludes that Si fertilization at a rate of 8 g Si liter−1 during the tillering stage can effectively improve growth, productivity, and nutrient uptake in wheat in the southern region of Rajasthan. [ABSTRACT FROM AUTHOR]

Research indicates that, owing to the enhanced grain-filling rate of tetraploid rice, its yield has notably improved compared to previous levels. Studies conducted on diploid rice have revealed that optimal planting density and fertilization rates play crucial roles in regulating rice yield. In this study, we investigated the effects of different nitrogen application and planting density treatments on the growth, development, yield, and nitrogen utilization in tetraploid (represented by T7, an indica–japonica conventional allotetraploid rice) and diploid rice (Fengliangyou-4, represented by FLY4, a two-line super hybrid rice used as a reference variety for the approval of super rice with a good grain yield performance). The results indicated that the highest grain-filling rate of T7 could reach 77.8% under field experimental conditions due to advancements in tetraploid rice breeding. This is a significant improvement compared with the rate seen in previous research. Under the same conditions, T7 exhibited a significantly lower grain yield than FLY4, which could be attributed to its lower grain-filling rate, spikelets per panicle, panicle number m−2, and harvest index score. Nitrogen application and planting density displayed little effect on the grain yield of both genotypes. A higher planting density significantly enhanced the leaf area index and biomass accumulation, but decreased the harvest index score. Compared with T7, FLY4 exhibited a significantly higher nitrogen use efficiency (NUEg), which was mainly due to the higher nitrogen content in the straw. Increasing nitrogen application significantly decreased NUEg due to its minimal effect on grain yield combined with its significant enhancement of nitrogen uptake. Our results suggest that the yield and grain-filling rate of T7 have been improved compared with those of previously tested polyploid rice, but are still lower than those of FLY4, and the yield of tetraploid rice can be further improved by enhancing the grain-filling rate, panicle number m−2, and spikelets per panicle via genotype improvement. [ABSTRACT FROM AUTHOR]

Introduction: Long-term application of excessive nitrogen (N) not only leads to low N use efficiency (NUE) but also exacerbates the risk of environmental pollution due to N losses. Substituting partial chemical N with organic fertilizer (SP) is an environmentally friendly and sustainable fertilization practice. However, the appropriate rate of SP in rainfed maize cropping systems in semi-arid regions of China is unknown. Methods: Therefore, we conducted a field experiment between 2021 and 2022 in a semi-arid region of Northern China to investigate the effects of SP on maize growth, carbon and N metabolism (C/NM), and NUE. The following treatments were used in the experiment: no N application (CK), 100% chemical N (SP0, 210 kg N ha-1), and SP substituting 15% (SP1), 30% (SP2), 45% (SP3), and 60% (SP4) of the chemical N. The relationship between these indicators and grain yield (GY) was explored using the Mantel test and structural equation modeling (SEM). Results and discussion: The results found that the SP1 and SP2 treatments improved the assimilates production capacity of the canopy by increasing the leaf area index, total chlorophyll content, and net photosynthetic rate, improving dry matter accumulation (DMA) by 6.2%-10.6%, compared to the SP0 treatment. SP1 and SP2 treatments increased total soluble sugars, starch, free amino acids, and soluble protein contents in ear leaves via increasing the enzymatic reactions related to C/NM in ear leaves during the reproductive growth stage compared with SP0 treatment. The highest plant nitrogen uptake (PNU) and nitrogen recovery efficiency were obtained under the SP2 treatment, and the GY and nitrogen agronomic efficiency were higher than the SP0 treatment by 9.2% and 27.8%. However, SP3 and SP4 treatments reduced DMA and GY by inhibiting C/NM in ear leaves compared to SP0 treatment. Mantel test and SEM results revealed that SP treatments indirectly increased GY and PNU by directly positively regulating C/NM in maize ear leaves. Therefore, in the semi-arid regions, substituting 30% of the chemical N with SP could be considered. This fertilizer regime may avoid GY reduction and improve NUE. This study provides new insights into sustainable cultivation pathways for maize in semi-arid regions. [ABSTRACT FROM AUTHOR]

Cold stress is a critical factor affecting rice production worldwide. The application of cold-resistant agents may improve the cold resistance and yield of crops. To screen for suitable cold-resistant agents for machine-transplanted early rice, the effects of uniconazole, abscisic acid, and zinc-amino acids chelate and their spraying times (seed soaking stage, one leaf and one heart stage, two leaves and one heart stage, 7 days before the transplanting stage, and regreening stage) on the yield and cold resistance of machine-transplanted early rice were investigated. Moreover, the application method (spraying amount: 750 and 1125 g ha-1; spraying time: 7 days before the transplanting stage, transplanting stage, regreening stage, and transplanting stage and regreening stage) for the most suitable cold-resistant agent was optimized. The zinc-amino acids chelate was better than the other two cold-resistant agents for promoting rice tillering and increasing the leaf area index, dry matter weight, antioxidant enzyme activities (CAT, SOD, POD) and yield (i.e., 9.22% and 7.14% higher than uniconazole and abscisic acid, respectively), especially when it was applied in the regreening stage. The examination of spraying amounts and times indicated that the zinc-amino acids chelate dosage had no significant effect on the yield and cold resistance of early rice. However, the rice yield and antioxidant enzyme activities were highest when samples were sprayed once in the transplanting stage and the regreening stage. On the basis of the study results, 750 g ha-1 zincamino acids chelate applications in the transplanting and regreening stages of machine-transplanted early rice plants may be ideal for increasing cold stress resistance and yield. [ABSTRACT FROM AUTHOR]

Forest ecosystems play a decisive role in the global climatic condition, as well as, provides a wide range of societal benefits, including fuel-wood, tourism, and ecosystem services are considered as one of the major sources of livelihood for the local people in the upper Blue Nile Basin. Therefore, rapid and accurate estimation of forest biomass is crucial for greatly reducing the uncertainty in carbon stock assessments, and for designing strategic forest management plans. Because, above-ground biomass (AGB) estimation is important in determining the management, environmental, and economic roles of forests in the Blue Nile basin. The study was aimed at estimating above-ground biomass in the Upper Blue Nile Basin forests by integrating field-measured data with predictors from Sentinel-2 image. The relationship between measured AGB and sentinel-2 derived vegetation indices and biophysical parameters showed a good correlation result (r value ranging from 0.67 to 0.74). A stepwise regression analysis was carried out in order to develop AGB estimation model by identifying the most important variable. The result demonstrated that, green normalized difference vegetation index, leaf area index, fraction of absorbed photosynthetic active radiation and fractional vegetation cover achieved good performance in predicting AGB with R2 value > 0.5. AGB was estimated with a coefficient of determination (R2) of 0.59 adjusted R2 of 0.618 and root mean square error of (RMSE) 38.36 t/ha in comparison to field observations. The maximum AGB value of 268.32 t/ha was estimated in the Alemsaga natural forest, which is a highly protected dense forest stand from any entrance and disturbance. Generally, integrating field data with optical remote sensing data provides more reliable result for AGB estimation. Moreover, it is also recommended to employ RADAR and LiDAR remote sensing data products together in order to attain more precise estimate results of AGB with great potential for forest resource monitoring and management. [ABSTRACT FROM AUTHOR]

Evaporative fraction (EF) is a useful measure for quantifying land surface energy partitioning processes and determining evaporative regimes; however, its influencing factors remain highly uncertain. Here, global data sets were compiled to disentangle the effects of environmental variables on EF variations along climate and land surface gradients. We found that (a) at annual timescales, ecosystem‐level EF could be expressed as a power law function of aridity index. The relationships of mean annual soil water content (SWC) and leaf area index (LAI) with mean annual EF resembled the traditional evaporative regime theory; (b) at daily timescales, the boosted regression tree method quantitatively revealed that the impacts of environmental variables (including meteorological variables) on EF showed equal importance, especially at humid sites, primarily due to the different response direction and magnitude of latent heat (LE) and sensible heat (H) fluxes to environmental changes. Particularly, the contrasting responses of LE (positive) and H (negative) to SWC, LAI, and relative humidity enhanced the positive effects of those influencing variables on EF; whereas, the correlations between EF and energy‐related factors (i.e., net radiation‐Rn and air temperature‐Ta) deteriorated as both LE and H showed positive response patterns to those variables; (c) meteorological factors were also found to have nonlinear effects on daily EF, further modified by climatic conditions. Rn near 150 W/m2 and Ta near 15°C appeared to be important energy‐partitioning thresholds at drier and humid sites, respectively. Moreover, changing interactions among environmental variables with climates were demonstrated to be important for better explaining EF variations. Plain Language Summary: Evaporative fraction (EF; defined as latent heat flux‐LE divided by the sum of LE and sensible heat flux‐H) can vary under different climatic and land surface conditions, but its influencing factors remain poorly understood. To this end, we explored the effects of environmental variables on annual and daily EF variations at different sites around the globe. We found that mean annual EF decreased with increasing aridity index and increased with mean annual soil water content and leaf area index. By comparison, the boosted regression tree method quantitatively showed that environmental variables exerted equally important roles in regulating daily EF, especially at humid sites. The complex interplays of daily EF with environmental variables were mainly due to the different responses of LE and H to surrounding environments and the strong nonlinear and interactive effects of environmental variables. These results are important for understanding the driving mechanisms of EF and land surface energy partitioning processes along climate and land surface gradients. Key Points: Environmental variables exerted equally important roles in regulating daily evaporative fraction, especially at humid sitesSynchronous responses of latent and sensible heat to surroundings determined how environmental variables affected evaporative fractionEnvironmental factors had strong nonlinear and interactive effects on daily evaporative fraction, which was further modified by climates [ABSTRACT FROM AUTHOR]

Carbon Dots (CDs) were synthesized from peanut shells (PNS) through pyrolysis and characterized using FTIR, XRD, HRTEM and BET analysis revealing an average size of 2–5 nm with amorphous nature. Synthesized PNS-CDs was employed both as priming and foliar agent for enhancing seed quality and crop productivity in blackgram (Vigna mungo L. Hepper). Different concentrations ranging from 50, 100, 200, 300, 400, 500 and 1000 ppm was used for seed priming and 5, 10, 15, 20, 25, 50, 75 and 100 ppm were given as foliar spray on 30th and 45th days after sowing (DAS). On accounting the best seed priming and foliar spray concentrations, field trial was conducted to validate the optimistic effect of PNS-CDs on blackgram crop productivity. Results revealed that priming with 200 ppm for 3 h exhibited maximum seed imbibition (54%), germination (88%) and vigour index (3165). Whereas, foliar spray with 50 ppm expressed significant improvement in leaf area index (2.6), total chlorophyll (2.70 mg/g), total soluble protein (71 mg/g), Number of nodules/plant (138), seed yield/plant (8.7 g) and 100 seed weight (5 g). The impact of PNS-CDs treatments resulted in increased photosynthetic rate (12.45 µmol CO2 m−2s−1), transpiration rate (3.13 mmol H2O/m−2s−1), stomatal conductance (0.55 mol H2O/m−2s−1) and internal leaf CO2 concentration (652 µmol CO2 m−2s−1) which ultimately enhanced the photosynthetic efficiency of plants. It has also exhibited a promising effect on the resultant seed in which the combination seed priming (200 ppm) followed by foliar spray (50 ppm) recorded maximum 100 seed weight (4.19 g), germination (97%) and vigour index (3019). Thus, this study highlights the promising role of PNS-CDs as a sustainable and effective agricultural nanomaterial, offering a novel approach to utilize the agricultural waste and also to enhance the crop productivity through advanced non-chemical approach. [ABSTRACT FROM AUTHOR]

AbstractIndigenous fermented liquid formulations (Panchagavya, Jeevamrut and Amritpani) are rich sources of beneficial microflora and fauna, vitamins, minerals and growth-promoting hormones. Small and marginal farmers of North-West Himalayan region popularly grow sweet pepper as commercial crop. Poultry manure and vermicompost are two cheap nutrient sources, popular among these growers. So, inorder to investigate the combined effect of these organic inputs, two-year field experiment with seven treatments and three replications; with randomized block design; comprised of 100, 90, 80, 70, 60, 50, 40 per cent recommended dose of nutrients (RDN) which was applied through vermicompost and poultry manure along with Panchagavya (5%), Jeevamrut (5%), Amritpani (5%) and plant growth promoting rhizobacteria which was isolated from experimental location, applied to all treatments, except T1; was carried out to study the response of these inputs on quality parameters of sweet pepper. Results revealed that treatment T2 that comprised of 90% RDN including Panchagavya, Jeevamrut, Amritpani and PGPR led to noticeably improvement in growth and quality parameters viz. chlorophyll-a (0.52 mg g−1), chlorophyll-b (0.73 mg g−1), ascorbic acid (29.47 mg 100 g−1), protein content (28.90%), capsaicin content (0.23 mg g−1), number of primary branches (8.20), average fruit weight (62 g), fruit yield (290.58 quintals ha−1), number of flowers per plant (24.97), leaf area index (4.73) and shelf life (6.03 days). Hence, it is concluded that the combined use of these inputs has the potential to enhance growth and quality characteristics of sweet pepper in achieving sustainable crop productivity. [ABSTRACT FROM AUTHOR]

Evapotranspiration (ET) is responsible for regulating the hydrological cycle, with a relevant impact on air humidity and precipitation that is particularly important in the context of acute drought events in recent years. With the intensification of rainfall deficits and extreme heat events, the Mediterranean region requires regular monitoring to enhance water resource management. Even though remote sensing provides spatially continuous information for estimating ET on large scales, existing global products with spatial resolutions ≥ 0.5 km are insufficient for capturing spatial detail at a local level. In the framework of ESA's 4DMED-Hydrology project, we generate an ET dataset at both high spatial and high temporal resolutions using the Priestley–Taylor Two-Source Energy Balance (TSEB-PT) model driven by Copernicus satellite data. We build an automatic workflow to generate a 100 m ET product by combining data from Sentinel-2 (S2) MultiSpectral Instrument (MSI) and Sentinel-3 (S3) land surface temperature (LST) with ERA5 climate reanalysis derived within the period 2017–2021 over four Mediterranean basins in Italy, Spain, France, and Tunisia (Po, Ebro, Hérault, and Medjerda). First, original S2 data are pre-processed before deriving 100 m inputs for the ET estimation. Next, biophysical variables, like leaf area index and fractional vegetation cover, are generated, and then they are temporally composited within a 10 d window according to S3 acquisitions. Consequently, decadal S2 mosaics are used to derive the remaining TSEB-PT inputs. In parallel, we sharpen 1 km S3 by exploiting the dependency between coarse-resolution LST and 100 m S2 reflectances using a decision tree algorithm. Afterwards, climate forcings are utilized to model energy fluxes and then for daily ET retrieval. The daily ET composites demonstrate reasonable TSEB-PT estimates. Based on the validation results against eight eddy covariance (EC) towers between 2017 and 2021, the model predicts 100 m ET with an average RMSE of 1.38 mm d−1 and a Pearson coefficient equal to 0.60. Regardless of some constraints mostly related to the high complexity of EC sites, TSEB-PT can effectively estimate 100 m ET, which opens up new opportunities for monitoring the hydrological cycle on a regional scale. The full dataset is freely available at https://doi.org/10.48784/b90a02d6-5d13-4acd-b11c-99a0d381ca9a , https://doi.org/10.48784/fb631817-189f-4b57-af6a-38cef217bad3 , https://doi.org/10.48784/70cd192c-0d46-4811-ad1d-51a09734a2e9 , and https://doi.org/10.48784/7abdbd94-ddfe-48df-ab09-341ad2f52e47 for the Ebro, Hérault, Medjerda, and Po catchments, respectively (Bartkowiak et al., 2023a–d). [ABSTRACT FROM AUTHOR]

Background: An experiment was conducted at Nkoranza in the Bono-East Region and Ejura Sekyedumase in the Ashanti Region of Ghana from July to December, 2021 to evaluate the effects of P based fertilizer and plant spacing on leaf area, leaf area index and seed yield and yield components of Bambara groundnut. Methods: The experiment was a 3×3 factorial, arranged in a randomized complete block design with three replicates. The first factor was plant spacing with three levels, including 50 cm × 20 cm, 40 cm × 20 cm and 40 cm × 25 cm, while the second factor was application of P based complex fertilizer (NPK 11:22:20) with three levels, including 0 kg/ha, 30 kg P/ha and 60 kg P/ha. Result: Results of the study revealed that leaf area, leaf area index, yield components and seed yield of Bambara groundnut were significantly affected by plant spacing. Wider spacing of 50 cm row was better than 40 cm. Fertilizer application had mixed responses, with no significant impact when the plant density was 10 m-2, whereas under a plant density of 12.5 m-2 there was a response to 60 kg P based fertilizer application, which needs further study. [ABSTRACT FROM AUTHOR]

Background: Micronutrient deficiency especially of zinc (Zn) impacts wheat growth and yield under both timely sown and late sown conditions. The study investigated the effect of Zn, cytokinin and gibberellic acid (GA) on growth and yield of wheat crop. The design of experiment was split-split plot having three replications. Methods: The main plots were comprised of two varieties i.e., V1 (PBW 725) and V2 (PBW 752) which were divided into three subplots Zn0 (no Zn), Zn1 (62.5 kg/ha soil application of ZnSO4) and Zn2 (31.25 kg/ha Zn in soil+foliar spray of 0.5% of ZnSO4). The subplots were further divided into four sub-sub plots i.e., H0 (no hormone), H1 (10 ppm GA), H2 (10 ppm cytokinin) and H3 (5 ppm GA+5 ppm of cytokinin). Result: The results indicated that the combination of Zn2+H1 resulted in maximum height of plant, accumulation of dry matter and straw yield for both varieties. The reported increment for all three parameters was 8.3%, 15.4% and 18.9% for V1 and 10.1%, 14.9% and 17.4% for V2, respectively. However, for both V1 and V2 a combination of Zn2+H2 improved tiller count, leaf area index and ultimately grain yield. The increment for V1 was 24.4%, 15.2% and 17.1%, while for V2 was 30.1%, 17.2% and 19.3%, respectively. The maximum harvest index value was recorded for variety V2 under Zn2+H2. The correlation analysis showed that leaf area index, number of tillers and dry matter accumulation are strongly correlated with grain yield. In general, the study emphasized that soil+foliar application of Zn alongside 10 mg/L cytokinin was most prominent in improving growth and yield of both wheat varieties. [ABSTRACT FROM AUTHOR]

Background: Soil is important medium of all living beings. Due to the high doses of fertilizers application soil heath is getting deteriorated. Soil fertility variation restricts efficient fertilizer N management when broad based blanket recommendations are used in maize (Zea mays L.). Site-specific fertilizer nitrogen management (SSNM) could be the best management option to avoid excessive and untimely nitrogen (N) applications in maize. Methods: A field experiment was conducted at Agronomy Farm, Lovely Professional University, Phagwara during kharif season of 2020 and 2021 to study the nitrogen management using leaf colour chart (LCC) and chlorophyll meter in maize (Zea mays L.). Colour (of the first top maize leaf with fully exposed collar) as measured by comparison with different shades of green colour on a leaf colour chart (LCC) and also by SPAD meter. Need based fertilizer application at LCC5 and SPAD 50 significantly improved growth and yield attributes viz., plant height, dry matter accumulation plant-1, number of leaves plant-1, stem diameter, number of internodes plant-1, leaf area index, cob length, cob girth, number of cobs plant-1, number of grains cob-1, grain weight plant-1 and 1000-grain weight along with higher grain and stover yields, grain protein content over fixed time application of 1200 kg N ha-1. Result: Evaluation of the established threshold leaf greenness revealed that fertilizer N management using LCC 5 and SPAD 50 starting from six-leaf stage to before silking stage resulted in improved agronomic efficiency in maize. There was no response to fertilizer N application at silking stage. The study concluded that in maize, fertilizer N can be more efficiently managed by applying fertilizer N dose based on leaf colour as measured by LCC than blanket recommendation. [ABSTRACT FROM AUTHOR]

Litter decomposition is not fully explained by the general triangle of climate, litter quality and soil decomposers. Therefore, other theoretical frameworks, such as Home-Field Advantage (HFA), have emerged to explain the remaining variation of decomposition. HFA states that litter decomposes faster in their site of origin (home) than far from it (away). However, there are no consistent patterns of HFA and this can varies depending the ecosystem and plant species analyzed. One of the most variable ecosystems in terms of species biodiversity turnover, topography, and soil conditions are the Upper Andean Tropical Forests (UATF), but to date there is no study testing HFA in this ecosystem. Here, HFA was tested through a reciprocal litterbag translocation field experiment across different UATF. The experiment comprised 2520 litterbags placed in 14 20 x 20 m plots that belonged to four sites to analyze decomposition of 15 plant species for 18 months. Of these 15 species, seven were present at only one site. The mean decomposition was calculated for all 15 species to determine the relative decomposition at each site and the decomposition of the seven species at home and away sites was analyzed through two-way ANOVA (sites x species) and linear mixed models. I contrasted environmental charcteristics between sites including litter depth, slope, leaf area index, canopy openness, and microclimatic variables. The results showed that the pattern of decomposition was always the same, no matter the origin of the species and the decomposition period. Microclimate, litter depth, and slope varied between sites, yet these differences were not enough to influence affinity effects of decomposition, as relative decay rates were similar between home and away sites. Overall, no HFA was found in UATF possibly because: (i) strong environmental filters along montane forests homogenize decomposer communities; (ii) high diversity in litters drive decomposers with high ability to degrade different organic compounds; (iii) little adaptation of decomposers to recurrent litter as they respond mainly to changes in litter quality. These results imply that changes in species composition by current anthropogenic pressures could have profound impacts on carbon cycle and nutrient fluxes depending on the identity of species arriving in UATF. [ABSTRACT FROM AUTHOR]

The prediction of crop growth and nitrogen status is essential for agricultural development and food security under climate change scenarios. Crop models are powerful tools for simulating crop growth and their responses to environmental variables, but accurately capturing the dynamic changes in crop nitrogen remains a considerable challenge. Data assimilation can reduce uncertainties in crop models by integrating observations with model simulations. However, current data assimilation research is primarily focused on a limited number of observational variables, and insufficiently utilizes nitrogen observations. To address these challenges, this study developed a new multivariable data assimilation system, ORYZA-EnKF, that is capable of simultaneously integrating multivariable observations (including development stage, DVS; leaf area index, LAI; total aboveground dry matter, WAGT; and leaf nitrogen concentration, LNC). Then, the system was tested through three consecutive years of field experiments from 2021 to 2023. The results revealed that the ORYZA-EnKF model significantly improved the simulations of crop growth compared to the ORYZA2000 model. The relative root mean squared error (RRMSE) for LAI simulations decreased from 23–101% to 16–47% in the three-year experiment. Moreover, the incorporation of LNC observations enabled more accurate predictions of rice nitrogen dynamics, with RRMSE for LNC simulations reduced from 16–31% to 14–26%. And, the RRMSE decreased from 32–50% to 30–41% in the simulations of LNC under low-nitrogen conditions. The multivariable data assimilation system demonstrated its effectiveness in improving crop growth simulations and nitrogen status predictions, providing valuable insights for precision agriculture. [ABSTRACT FROM AUTHOR]

Breeding high-photosynthetic-efficiency wheat varieties is a crucial link in safeguarding national food security. Traditional identification methods necessitate laborious on-site observation and measurement, consuming time and effort. Leveraging unmanned aerial vehicle (UAV) remote sensing technology to forecast photosynthetic indices opens up the potential for swiftly discerning high-photosynthetic-efficiency wheat varieties. The objective of this research is to develop a multi-stage predictive model encompassing nine photosynthetic indicators at the field scale for wheat breeding. These indices include soil and plant analyzer development (SPAD), leaf area index (LAI), net photosynthetic rate (Pn), transpiration rate (Tr), intercellular CO2 concentration (Ci), stomatal conductance (Gsw), photochemical quantum efficiency (PhiPS2), PSII reaction center excitation energy capture efficiency (Fv'/Fm'), and photochemical quenching coefficient (qP). The ultimate goal is to differentiate high-photosynthetic-efficiency wheat varieties through model-based predictions. This research gathered red, green, and blue spectrum (RGB) and multispectral (MS) images of eleven wheat varieties at the stages of jointing, heading, flowering, and filling. Vegetation indices (VIs) and texture features (TFs) were extracted as input variables. Three machine learning regression models (Support Vector Machine Regression (SVR), Random Forest (RF), and BP Neural Network (BPNN)) were employed to construct predictive models for nine photosynthetic indices across multiple growth stages. Furthermore, the research conducted principal component analysis (PCA) and membership function analysis on the predicted values of the optimal models for each indicator, established a comprehensive evaluation index for high photosynthetic efficiency, and employed cluster analysis to screen the test materials. The cluster analysis categorized the eleven varieties into three groups, with SH06144 and Yannong 188 demonstrating higher photosynthetic efficiency. The moderately efficient group comprises Liangxing 19, SH05604, SH06085, Chaomai 777, SH05292, Jimai 22, and Guigu 820, totaling seven varieties. Xinmai 916 and Jinong 114 fall into the category of lower photosynthetic efficiency, aligning closely with the results of the clustering analysis based on actual measurements. The findings suggest that employing UAV-based multi-source remote sensing technology to identify wheat varieties with high photosynthetic efficiency is feasible. The study results provide a theoretical basis for winter wheat phenotypic monitoring at the breeding field scale using UAV-based multi-source remote sensing, offering valuable insights for the advancement of smart breeding practices for high-photosynthetic-efficiency wheat varieties. [ABSTRACT FROM AUTHOR]

Potato–legume intercropping has been confirmed to increase productivity in modern agricultural systems. However, the physiological and ecological mechanisms of potato–soybean intercropping for promoting tuber yield formation in potato remain unclear. Field experiments were conducted in 2022 and 2023 to explore the responses of tuber yield formation, rhizosphere soil quality, root growth, and plant physiology of potato in potato–soybean intercropping. The soil at the experimental site is Cambisols. The treatments included sole cropping potato, sole cropping soybean, and potato–soybean intercropping. Our results indicated that potato –soybean intercropping decreased the water content, increased the total K content and activities of urease and catalase in rhizosphere soil, and enhanced the root mean diameter, root projected area, and root length density in the 0–5 cm and 15–20 cm soil layers of potato. Moreover, potato–soybean intercropping improved the plant photosynthetically active radiation and light transmittance rate of the middle and lower layers as well as the leaf area index, enhanced the leaf chlorophyll b content and ribulose-1,5-diphosphate carboxylase/oxygenase activity, and increased the leaf net photosynthetic rate and organ dry matter accumulation amounts of potato. The changes in the above parameters resulted in an increased tuber weight per plant (19.4%) and commercial tuber number (42.5%) and then enhanced the equivalent tuber yield of potato (38.2%) and land equivalent ratio (1.31 in 2022 and 1.33 in 2023). Overall, potato–soybean intercropping greatly increased the equivalent tuber yield by improving the rhizosphere soil quality, root growth, and plant physiology of potato and then achieved a higher land equivalent ratio. [ABSTRACT FROM AUTHOR]

To address the prevailing problems of high water and fertilizer input and low productivity in Isatis tinctoria L. production in the Hexi Corridor in China, the effects of different irrigation amounts and nitrogen application rates on growth characteristics, photosynthetic physiology, root yield, and quality of I. tinctoria plants were studied with the aim of obtaining the optimal irrigation level and nitrogen application rate. From 2021 to 2023, we established a two-factor split-plot experiment in the oasis irrigation area with three irrigation amounts (sufficient water, medium water, and low water are 100%, 85%, and 70% of the typical local irrigation quota) for the main zone; three nitrogen application rates (low nitrogen, 150 kg ha−1, medium nitrogen, 200 kg ha−1, and high nitrogen, 250 kg ha−1) for the secondary zone; and three irrigation amounts without nitrogen as the control to explore the response of these different water and nitrogen management patterns for I. tinctoria in terms of growth characteristics, photosynthetic physiology, root yield, and quality. The results showed the following: (1) When the irrigation amount was increased from 75% to 100% of the local typical irrigation quota and the nitrogen application rate was increased from 150 to 250 kg ha−1, while the plant's height, leaf area index, dry matter accumulation in the stem, leaf, and root, as well as the net photosynthetic rate (Pn), the stomatal conductance (Gs), and the transpiration rate (Tr) of I. tinctoria increased gradually, and the root–shoot ratio decreased. (2) When the irrigation amount increased from 75% to 100% of the local typical irrigation quota, the yield and net proceeds of I. tinctoria increased from 43.12% to 53.43% and 55.07% to 71.61%, respectively. However, when the irrigation quota was 100% of the local typical irrigation quota, and the nitrogen application rate increased from 150 to 200 kg ha−1, the yield of I. tinctoria increased from 21.58% to 23.69%, whereas the increase in nitrogen application rate from 200 to 250 kg ha−1 resulted in a decrease in the yield of I. tinctoria from 10.66% to 18.92%. During the 3-year experiment, the maximum yield of I. tinctoria appeared when treated with sufficient water and medium nitrogen, reaching 9054.68, 8066.79, and 8806.15 kg ha−1, respectively. (3) The effect of different water and nitrogen combination treatments on the root quality of I. tinctoria was significant. Under the same irrigation level, increasing the nitrogen application rate from 150 to 250 kg ha−1 could increase the contents of indigo, indirubin, (R,S)–goitrin, total nucleoside, uridine, and adenosine in the root of I. tinctoria from 3.94% to 9.59%, 1.74% to 12.58%, 5.45% to 18.35%, 5.61% to 11.59%, 7.34% to 11.32%, and 14.98% to 54.40%, respectively, while the root quality of I. tinctoria showed a trend of first increasing and then decreasing under the same nitrogen application level. (4) AHP, the entropy weight method, and the TOPSIS method were used for a comprehensive evaluation of multiple indexes of water–nitrogen coupling planting patterns for I. tinctoria, which resulted in the optimal evaluation of the W3N2 combination. Therefore, the irrigation level was 100% of the local typical irrigation quota, the nitrogen application rate should be appropriately reduced, and controlling the nitrogen application rate at the level of 190.30–218.27 kg ha−1 can improve water–nitrogen productivity yields for I. tinctoria and root quality. The results of this study can provide a theoretical basis and technical support for a more reasonable water and fertilizer management model for the I. tinctoria production industry in the Hexi Corridor in China. [ABSTRACT FROM AUTHOR]