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Thanks to several Vitis vinifera backcrosses with an initial V. vinifera L. × V. rotundifolia (previously Muscadinia rotundifolia) interspecific cross, the MrRUN1/MrRPV1 locus (resistance to downy and powdery mildews) was introgressed in genotypes phenotypically close to V. vinifera varieties. To check the consequences of introgressing parts of the V. rotundifolia genome on gene expression during fruit development, we conducted a comparative RNA-seq study on single berries from different V. vinifera cultivars and V. vinifera × V. rotundifolia hybrids, including ‘G5’ and two derivative microvine lines, ‘MV102’ (resistant) and ‘MV32’ (susceptible) segregating for the MrRUN1/RPV1 locus. RNA-Seq profiles were analyzed on a comprehensive set of single berries from the end of the herbaceous plateau to the ripe stage. Pair-end reads were aligned both on V. vinifera PN40024.V4 reference genome, V. rotundifolia cv ‘Trayshed’ and cv ‘Carlos’, and to the few resistance genes from the original V. rotundifolia cv ‘52’ parent available at NCBI. Weighted Gene Co-expression Network Analysis (WGCNA) led to classifying the differentially expressed genes into 15 modules either preferentially correlated with resistance or berry phenology and composition. Resistance positively correlated transcripts predominantly mapped on the 4–5 Mb distal region of V. rotundifolia chromosome 12 beginning with the MrRUN1/MrRPV1 locus, while the negatively correlated ones mapped on the orthologous V. vinifera region, showing this large extremity of LG12 remained recalcitrant to internal recombination during the successive backcrosses. Some constitutively expressed V. rotundifolia genes were also observed at lower densities outside this region. Genes overexpressed in developing berries from resistant accessions, either introgressed from V. rotundifolia or triggered by these in the vinifera genome, spanned various functional groups, encompassing calcium signal transduction, hormone signaling, transcription factors, plant–pathogen-associated interactions, disease resistance proteins, ROS and phenylpropanoid biosynthesis. This transcriptomic insight provides a foundation for understanding the disease resistance inherent in these hybrid cultivars and suggests a constitutive expression of NIR NBS LRR triggering calcium signaling. Moreover, these results illustrate the magnitude of transcriptomic changes caused by the introgressed V. rotundifolia background in backcrossed hybrids, on a large number of functions largely exceeding the ones constitutively expressed in single resistant gene transformants. Full article

In data-scarce regions, prudent planning and precise decision-making for sustainable development, especially in agriculture, remain challenging due to the lack of correct information. Remotely sensed satellite images provide a powerful source for assessing land use and land cover (LULC) classes and crop identification. Applying remote sensing (RS) in conjunction with the Geographical Information System (GIS) and modern tools/algorithms of artificial intelligence (AI) and deep learning has been proven effective for strategic planning and integrated resource management. The study was conducted in the canal command area of the Lower Chenab Canal system in Punjab, Pakistan. Crop features/classes were assessed using the Normalized Difference Vegetation Index (NDVI) algorithm. The Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m and Landsat 5 TM (thematic mapper) images were deployed for NDVI time-series analysis with an unsupervised classification technique to obtain LULC classes that helped to discern cropping pattern, crop rotation, and the area of specific crops, which were then used as key inputs for agricultural mechanization planning and resource management. The accuracy of the LULC map was 78%, as assessed by the error matrix approach. Limitations of high-resolution RS data availability and the accuracy of the results are the concerns observed in this study that could be managed by the availability of good quality local sources and advanced processing techniques, that would make it more useful and applicable for regional agriculture and environmental management. Full article

Previous studies have shown a negative correlation between chilling accumulation (CA) and heat requirements (HRs) in peaches (Prunus persica (L.) Batsch), consistent with findings in other plants in spring events. However, there is a lack of comparative research on the CA–HR relationship in peach varieties with different chilling requirements (CRs), and the specific impact of CA on HR reduction remains poorly described. To address this, we investigated the effects of CA on the days and HR for flowering in 54 peach varieties of differing CRs. Scanning electron microscopy was used to observe the effects of CA on the phenology of floral organ development in a selected peach variety. Our results indicate that, in general, peaches exhibit a reduced HR and accelerated flowering as the CA increases, but that the strength and trend of the CA–HR relationship is influenced by the CR and the variety. Low-CR varieties showed less sensitivity to CA increments, requiring higher relative increases in CA to significantly lower the HR, whereas high-CR varieties appeared to be more sensitive, with even modest changes leading to substantial reductions in HR. However, variations from this generality exist, even within varieties displaying the same rCA (the ratio of CA to CR). Additionally, we provide a summary of the relationship between the rCA and drHR in peaches of differing CRs, and identify several varieties exhibiting a strong response in the CA–HR relationship. This study also highlights the impact of CA on flower bud development, revealing slower progression under lower CA levels and accelerated growth with an increased CA. In particular, we identified the critical period of the enlargement and initiation of green scales as indicative of successful pollen grain formation. Finally, we present a schematic of the CA–HR relationship for flowering in peaches. Full article

A cropping system practice is the sequential cultivation of crops in different crop seasons of a year. Cropping system practices determine the land productivity and sustainability of agriculture in regions and, therefore, information on cropping systems of different regions in the form of maps and statistics form critical inputs in crop planning for optimal use of resources. Although satellite-based crop mapping is widely practiced, deriving cropping systems maps using satellites is less reported. Here, we developed moderate-resolution maps of the major cropping systems of South Asia for the year 2014–2015 using multi-temporal satellite data together with a spectral matching technique (SMT) developed with an extensive set of field observation data supplemented with expert-identified crops in high-resolution satellite images. We identified and mapped 27 major cropping systems of South Asia at 250 m spatial resolution. The rice-wheat cropping system is the dominant system, followed by millet-wheat and soybean-wheat. The map showing the cropping system practices of regions opens up many use cases related to the agriculture performance of the regions. Comparison of such maps of different time periods offers insights on sensitive regions and analysis of such maps in conjunction with resources maps such as climate, soil, etc., enables optimization of resources vis-à-vis enhancing land productivity. Thus, the current study offers new opportunities to revisit the cropping system practices and redesign the same to meet the challenges of food security and climate resilient agriculture. Full article

Polycyclic aromatic hydrocarbons (PAHs) are widespread globally, primarily due to long-term anthropogenic pollution sources. Since PAHs tend to accumulate in soil sediments, liverwort plants, such as Lunularia cruciata, are susceptible to their adverse effects, making them good models for bioindicators. The aim of this study was to probe the impact of anthracene, a three-ring linear PAH, on the growth parameters of L. cruciata and the relationship established with the internalization of the pollutant throughout the phenology of the plant. Intrinsic plant responses, isolated from external factors, were assessed in vitro. L. cruciata absorbed anthracene from the culture medium, and its bioaccumulation was monitored throughout the entire process, from the gemma germination stage to the development of the adult plant, over a total period of 60 days. Consequently, plants exposed to concentrations higher than 50 μM anthracene, decreased the growth area of the thallus, the biomass and number of tips. Moreover, anthracene also impinged on plant symmetry. This concentration represented the maximum limit of bioaccumulation in the tissues. This study provides the first evidence that architectural variables in liverwort plants are suitable parameters for their use as bioindicators of PAHs. Full article

Rapid and accurate mapping of soil properties in farmlands is crucial for guiding agricultural production and maintaining food security. Traditional methods using spectral features from remote sensing prove valuable for estimating soil properties, but are restricted to short periods of bare soil occurrence within agricultural settings. Addressing the challenge of predicting soil properties under crop cover, this study proposed an improved soil modeling framework that integrates dynamic crop growth information with machine learning techniques. The methodology’s robustness was tested on six key soil properties in an agricultural region of China, including soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), dissolved organic carbon (DOC), dissolved organic nitrogen (DON), and pH. Four experimental scenarios were established to assess the impact of crop growth information, represented by the normalized difference vegetation index (NDVI) and phenological parameters. Specifically, Scenario I utilized only natural factors (terrain and climate data); Scenario II added phenological parameters based on Scenario I; Scenario III incorporated time-series NDVI based on Scenario I; and Scenario IV combined all variables (traditional natural factors and crop growth information). These were evaluated using three advanced machine learning models: random forest (RF), Cubist, and Extreme Gradient Boosting (XGBoost). Results demonstrated that incorporating phenological parameters and time-series NDVI significantly improved model accuracy, enhancing predictions by up to 36% over models using only natural factors. Moreover, although both are crop growth factors, the contribution of the time-series NDVI variable to model accuracy surpassed that of the phenological variable for most soil properties. Relative importance analysis suggested that the crop growth information, derived from time-series NDVI and phenology data, collectively explained 14–45% of the spatial variation in soil properties. This study highlights the significant benefits of integrating remote sensing-based crop growth factors into soil property inversion under crop-covered conditions, providing valuable insights for digital soil mapping. Full article

Accurate identification of crop phenology timing is crucial for agriculture. While remote sensing tracks vegetation changes, linking these to ground-measured crop growth stages remains challenging. Existing methods offer broad overviews but fail to capture detailed phenological changes, which can be partially related to the temporal resolution of the remote sensing datasets used. The availability of higher-frequency observations, obtained by combining sensors and gap-filling, offers the possibility to capture more subtle changes in crop development, some of which can be relevant for management decisions. One such dataset is Planet Fusion, daily analysis-ready data obtained by integrating PlanetScope imagery with public satellite sensor sources such as Sentinel-2 and Landsat. This study introduces a novel method utilizing Dynamic Time Warping applied to Planet Fusion imagery for maize phenology detection, to evaluate its effectiveness across 70 micro-stages. Unlike singular template approaches, this method preserves critical data patterns, enhancing prediction accuracy and mitigating labeling issues. During the experiments, eight commonly employed spectral indices were investigated as inputs. The method achieves high prediction accuracy, with 90% of predictions falling within a 10-day error margin, evaluated based on over 3200 observations from 208 fields. To understand the potential advantage of Planet Fusion, a comparative analysis was performed using Harmonized Landsat Sentinel-2 data. Planet Fusion outperforms Harmonized Landsat Sentinel-2, with significant improvements observed in key phenological stages such as V4, R1, and late R5. Finally, this study showcases the method’s transferability across continents and years, although additional field data are required for further validation. Full article

The Normalized Difference Vegetation Index (NDVI) is a crucial remote-sensing metric for assessing land surface vegetation greenness, essential for various studies encompassing phenology, ecology, hydrology, etc. However, effective applications of NDVI data are hindered by data noise due to factors such as cloud contamination, posing challenges for accurate observation. In this study, we proposed a novel approach for employing a Temporal-Difference Graph (TDG) method to reconstruct low-quality pixels in NDVI data. Regarding spatio-temporal NDVI data as a time-varying graph signal, the developed method utilized an optimization algorithm to maximize the spatial smoothness of temporal differences while preserving the spatial NDVI pattern. This approach was further evaluated by reconstructing MODIS/Terra Vegetation Indices 16-Day L3 Global 250 m Grid (MOD13Q1) products over Northwest China. Through quantitative comparison with a previous state-of-the-art method, the Savitzky–Golay (SG) filter method, the obtained results demonstrated the superior performance of the TDG method, and highly accurate results were achieved in both the temporal and spatial domains irrespective of noise types (positively-biased, negatively-biased, or linearly-interpolated noise). In addition, the TDG-based optimization approach shows great robustness to noise intensity within spatio-temporal NDVI data, suggesting promising prospects for its application to similar datasets. Full article

Ratoon rice (RR) has emerged as an active adaptation to climate uncertainty, stabilizing total paddy rice yield and effectively reducing agriculture-related ecological environmental issues. However, identifying key remote sensing parameters for RR under cloudy and foggy conditions is challenging, and existing RR monitoring methods in these regions face significant uncertainties. Here, given the sensitivity of synthetic aperture radar (SAR) backscattering signals to the crop phenological period, this paper introduces a threshold model utilizing Sentinel-1A SAR data and phenological information for mapping RR. The Yongchuan District of Chongqing, which is often cloudy and foggy, was selected as a specific study region where VH-polarized backscatter coefficients of Sentinel-1 images were obtained at 10 m spatial resolution in 2020. Based on the proposed threshold model, the RR extraction overall accuracy was up to 90.24%, F1 score was 0.92, and Kappa coefficient was 0.80. Further analysis showed that the extracted RR boundaries exhibited high consistency with true Sentinel-2 remote sensing images and the RR extracted area was in good agreement with the actual planted area situation. This threshold model demonstrated good applicability in the studied cloudy and foggy region, and successfully distinguished RR from other paddy rice types. The methodological framework established in this study provides a basis for extensive application in China and other significant RR-producing regions globally. Full article

Continuous and dense time series of satellite remote sensing data are needed for several land monitoring applications, including vegetation phenology, in-season crop assessments, and improving land use and land cover classification. Supporting such applications at medium to high spatial resolution may be challenging with a single optical satellite sensor, as the frequency of good-quality observations can be low. To optimize good-quality data availability, some studies propose harmonized databases. This work aims at developing an ‘all-in-one’ Google Earth Engine (GEE) web-based workflow to produce harmonized surface reflectance data from Landsat-7 (L7) ETM+, Landsat-8 (L8) OLI, and Sentinel-2 (S2) MSI top of atmosphere (TOA) reflectance data. Six major processing steps to generate a new source of near-daily Harmonized Landsat and Sentinel (HLS) reflectance observations at 30 m spatial resolution are proposed and described: band adjustment, atmospheric correction, cloud and cloud shadow masking, view and illumination angle adjustment, co-registration, and reprojection and resampling. The HLS is applied to six equivalent spectral bands, resulting in a surface nadir BRDF-adjusted reflectance (NBAR) time series gridded to a common pixel resolution, map projection, and spatial extent. The spectrally corresponding bands and derived Normalized Difference Vegetation Index (NDVI) were compared, and their sensor differences were quantified by regression analyses. Examples of HLS time series are presented for two potential applications: agricultural and forest phenology. The HLS product is also validated against ground measurements of NDVI, achieving very similar temporal trajectories and magnitude of values (R² = 0.98). The workflow and script presented in this work may be useful for the scientific community aiming at taking advantage of multi-sensor harmonized time series of optical data. Full article

In organic farming, clover is an important basis for green manure in crop rotation systems due to its nitrogen-fixing effect. However, clover is often sown in mixtures with grass to achieve a yield-increasing effect. In order to determine the quantity and distribution of clover and its influence on the subsequent crops, clover plants must be identified at the individual plant level and spatially differentiated from grass plants. In practice, this is usually done by visual estimation or extensive field sampling. High-resolution unmanned aerial vehicles (UAVs) offer a more efficient alternative. In the present study, clover and grass plants were classified based on spectral information from high-resolution UAV multispectral images and texture features using a random forest classifier. Three different timestamps were observed in order to depict the phenological development of clover and grass distributions. To reduce data redundancy and processing time, relevant texture features were selected based on a wrapper analysis and combined with the original bands. Including these texture features, a significant improvement in classification accuracy of up to 8% was achieved compared to a classification based on the original bands only. Depending on the phenological stage observed, this resulted in overall accuracies between 86% and 91%. Subsequently, high-resolution UAV imagery data allow for precise management recommendations for precision agriculture with site-specific fertilization measures. Full article

Vegetation phenology is a biological factor that directly or indirectly affects the dynamic equilibrium between water and carbon fluxes in ecosystems. Quantitative evaluations of the regulatory mechanisms of vegetation phenology on water–carbon coupling are of great significance for carbon neutrality and sustainable development. In this study, the interannual variation and partial correlation between vegetation phenology (the start of growing season (SOS), the end of growing season (EOS), and the length of growing season (LOS)) and ET (evapotranspiration), GPP (gross primary productivity), WUE (water use efficiency; water–carbon coupling index) in the Huang-Huai-Hai and Yangtze River Basins in China from 2001 to 2019 were systematically quantified. The response patterns of spring (autumn) and growing season WUE to SOS, EOS, and LOS, as well as the interpretation rate of interannual changes, were evaluated. Further analysis was conducted on the differences in vegetation phenology in response to WUE across different river basins. The results showed that during the vegetation growth season, ET and GPP were greatly influenced by phenology. Due to the different increases in ET and GPP caused by extending LOS, WUE showed differences in different basins. For example, an extended LOS in the Huang-Huai-Hai basins reduced WUE, while in the Yangtze River Basin, it increased WUE. After extending the growing season for 1 day, ET and GPP increased by 3.01–4.79 mm and 4.22–6.07 gC/m², respectively, while WUE decreased by 0.002–0.008 gC/kgH₂O. Further analysis of WUE response patterns indicates that compared to ET, early SOS (longer LOS) in the Yellow River and Hai River basins led to a greater increase in vegetation GPP, therefore weakening WUE. This suggests that phenological changes may increase ineffective water use in arid, semi-arid, and semi-humid areas and may further exacerbate drought. For the humid areas dominated by the Yangtze River Basin, changes in phenology improved local water use efficiency. Full article

Leaf senescence is a complex trait which becomes crucial for grain filling because photoassimilates are translocated to the seeds. Therefore, a correct sync between leaf senescence and phenological stages is necessary to obtain increasing yields. In this study, we evaluated the performance of five deep machine-learning methods for the evaluation of the phenological stages of sunflowers using images taken with cell phones in the field. From the analysis, we found that the method based on the pre-trained network resnet50 outperformed the other methods, both in terms of accuracy and velocity. Finally, the model generated, Sunpheno, was used to evaluate the phenological stages of two contrasting lines, B481_6 and R453, during senescence. We observed clear differences in phenological stages, confirming the results obtained in previous studies. A database with 5000 images was generated and was classified by an expert. This is important to end the subjectivity involved in decision making regarding the progression of this trait in the field and could be correlated with performance and senescence parameters that are highly associated with yield increase. Full article

Mutagenic effectiveness and efficiency are the most important factors determining the success of mutation breeding, a coherent tool for quickly enhancing diversity in crops. This study was carried out at Lovely Professional University’s agricultural research farm in Punjab, India, during the year 2023. The experimental design followed a randomized complete block design (RCBD) with three replications. The experiment aimed to assess the effect of three chemical mutagens, sodium azide (SA), ethyl methyl sulphonates (EMSs), and methyl methane sulfonate (MMS), at three different concentrations (0.2%, 0.4%, and 0.6%), in SL958 and SL744 soybean varieties to select the mutant exhibiting the highest yield. The data were collected and analysed using a two-way ANOVA test through SPSS software (version 22), and the means were separated using Duncan’s multiple range test (DMRT) at the 5% level of significance. Between the two varieties, the highest seed germination percentage (76.0% seedlings/plot) was recorded in SL958 (0.4% SA), while the lowest (30.33% seedlings/plot) was observed in 0.6% MMS as compared to the control (53% and 76% in SL744 and SL958 at 10 days after sowing, respectively). Several weeks after sowing, the average plant height was observed to be higher (37.84 ± 1.32 cm) in SL958 (0.4% SA) and lower (20.58 ± 0.30 cm) in SL744 (0.6% SA), as compared to the controls (SL958: 26.09 ± 0.62 cm and SL744: 27.48 ± 0.74 cm). The average leaf count was the highest (234.33 ± 3.09 tetrafoliate leaves/plant) in SL958 (0.4% SA) while it was the lowest (87 leaves/plant) in 0.6% MMS as compared to the control (SL744 180.00 ± 1.63 and SL958 160.73 ± 1.05). The highest total leaf areas recorded in the SL958 and SL744 M1plants were 3625.8 ± 1.43 cm² and 2311.03 ± 3.65 cm², respectively. Seeds of the SL958 variety treated with 0.4% SA resulted in the development of tetrafoliate leaves with a broad leaf base and the maximum yield (277.55 ± 1.37 pods/plant) compared to the narrow pentafoliate leaves obtained through the treatment with EMS. Meanwhile, in the SL744 variety, the same treatment led to tetrafoliate leaves with a comparatively lower yield of 206.54 ± 23.47 pods/plant as compared to the control (SL744 164.33 ± 8.58 and SL958 229.86 ± 0.96). The highest protein content (47.04 ± 0.87% TSP) was recorded in the SL958 (0.4% SA) M2 seeds followed by a content of 46.14 ± 0.64% TSP in the SL744 (0.4% SA) M2 seeds, whereas the lowest content (38.13 ± 0.81% TSP) was found in SL958 (0.6% MMS). Similar observations were recorded for the lipid and fibre content. The 0.4% SA treatment in SL958 proved to be efficient in generating the highest leaf area (tetrafoliate leaves) and a reasonable yield of M1 (the first generation after mutation) plants. Full article

High solar radiation, combined with high temperature, causes losses in plant production. The application of foliar protector in plants is associated with improvements in photosynthesis, reduction in leaf temperature and, consequently, improved productivity. Two experiments were conducted. The first aimed to assess the efficacy of foliar protector versus artificial shading in mitigating the negative impacts of excessive radiation and temperature on the physiology, growth, and yield of common bean plants. The second experiment focused on comparing the timing in cycle plants (phenological phases) of foliar protector application in two different bean cultivars (BRS Fc 104 and BRS MG Realce) under field conditions. Artificial shading provided better results for photosynthesis, transpiration, growth and production compared to the application of foliar protector. In the field conditions experiment, the application timing of the foliar protector at different phenological phases did not increase productivity in the cultivars. The application of foliar protector under the conditions studied was not effective in mitigating the negative impacts of high solar radiation and temperature on common bean cultivation. However, it is opportune to evaluate the application of foliar protector in bean plants grown under conditions with water deficit, high solar radiation and high temperature. Full article