Search Results (154)

Food security is crucial for social stability and economic development. Ensuring food security in the arid region of Northwest China presents unique challenges due to limited water and soil resources. This study addresses these challenges by integrating a comprehensive water and soil resource matching assessment with grain production forecasting. Based on data from 2000 to 2020, this research projects the food security status to 2035 using the GM(1,1) model, incorporating a comprehensive index of soil and water resource matching and regression analysis to inform production forecasts. Key assumptions include continued historical trends in population growth, urbanization, and dietary shifts towards an increased animal protein consumption. The findings revealed a consistent upward trend in grain production from 2000 to 2020, with an average annual growth rate of 3.5%. Corn and wheat emerged as the dominant grain crops. Certain provinces demonstrated comparative advantages for specific crops like rice and wheat. The most significant finding is that despite the projected growth in the total grain output by 2035 compared to 2020, the regional grain self-sufficiency rate is projected to range from 79.6% to 84.1%, falling below critical food security benchmarks set by the FAO and China. This projected shortfall carries significant implications, underscoring a serious challenge to regional food security and highlighting the region’s increasing vulnerability to external food supply fluctuations. The findings strongly signal that current trends are insufficient and necessitate urgent and proactive policy interventions. To address this, practical policy recommendations include promoting water-saving technologies, enhancing regional cooperation, and strategically utilizing the international grain trade to ensure regional food security. Full article

In drought-prone regions without irrigation systems, effective agrotechnologies such as subsoiling are crucial for enhancing soil infiltration and water retention. However, the effects of subsoiling can vary depending on crop type and environmental conditions. Despite previous research, there is limited understanding of the contrasting responses of C3 (sunflower) and C4 (maize) crops to subsoiling under drought stress. This study addresses this knowledge gap by assessing the effectiveness of subsoiling as a drought mitigation practice on Haplic Chernozem in Northern Bulgaria, integrating ground-based and remote sensing data. Soil physical parameters, leaf area index (LAI), canopy temperature, crop water stress index (CWSI), soil moisture, and yield were evaluated under both conventional tillage and subsoiling for the two crops. A variety of optical and radar descriptive remote sensing products derived from Sentinel-1 and Sentinel-2 satellite data were calculated for different crop types. Consequently, the use of machine learning, utilizing all the processed remote sensing products, enabled the reasonable prediction of LAI, achieving a coefficient of determination (R²) after a cross-validation greater than 0.42 and demonstrating good agreement with in situ observations. Results revealed differing responses: subsoiling had a positive effect on sunflower, improving LAI, water status, and slightly increasing yield, while it had no positive effect on maize. These findings highlight the importance of crop-specific responses in evaluating subsoiling practices and demonstrate the added value of integrating unmanned aerial systems (UAS) and satellite-based remote sensing data into agricultural drought monitoring. Full article

Climate change has intensified drought stress, threatening global food security by affecting sensitive crops like maize (Zea mays). This study evaluated the potential of Antarctic fungal endophytes (Penicillium chrysogenum and P. brevicompactum) to enhance maize drought tolerance under field conditions with different irrigation regimes. Drought stress reduced soil moisture to 59% of field capacity. UAV-based multispectral imagery monitored plant physiological status using vegetation indices (NDVI, NDRE, SIPI, GNDVI). Inoculated plants showed up to two-fold higher index values under drought, indicating improved stress resilience. Physiological analysis revealed increased photochemical efficiency (0.775), higher chlorophyll and carotenoid contents (45.54 mg/mL), and nearly 80% lower lipid peroxidation in inoculated plants. Lower proline accumulation suggested better water status and reduced osmotic stress. Secondary metabolites such as phenolics, flavonoids, and anthocyanins were elevated, particularly under well-watered conditions. Antioxidant enzyme activity shifted: SOD, CAT, and APX were suppressed, while POD activity increased, indicating reprogrammed oxidative stress responses. Yield components, including cob weight and length, improved significantly with inoculation under drought. These findings demonstrate the potential of Antarctic endophytes to enhance drought resilience in maize and underscore the value of integrating microbial biotechnology with UAV-based remote sensing for sustainable crop management under climate-induced water scarcity. Full article

The critical nitrogen (N) dilution curve is widely used to diagnose crop N status, but no such model has been developed for sugar beet. This study evaluated the effects of irrigation amount and N rate on sugar yield, N use efficiency, and soil nitrate-N (NO₃-N) residue of drip-fertigated sugar beet in the arid southern Xinjiang of China. A reliable N nutrition index (NNI) for sugar yield was also established based on a critical N dilution curve derived from the dry matter of sugar beet. A three-year field experiment was established with six N rates (25–480 kg N ha⁻¹) and three irrigation levels based on crop evapotranspiration (ET_c) (0.6, 0.8, and 1.0 ET_c in 2019 and 2020, and 0.4, 0.6, and 0.8 ET_c in 2021). Results showed that sugar yield and N uptake increased and then generally stabilized with increasing N rate, while N use efficiency decreased. Most soil NO₃-N was mainly distributed in the 0–60 cm soil layer, but increasing irrigation amount reduced residual NO₃-N in the 0–80 cm soil layer. Additionally, the established critical N dilution curve of sugar beet was considered stable (Normalized RMSE = 16.6%), and can be used to calculate plant N requirements and further N rates during sugar beet growth. The results indicated that the optimal NNI was 0.97 under 0.6 ET_c for sugar yield production of sugar beet in this study. This study provides a basis for efficient water and N management in sugar beet production in arid and semi-arid regions globally. Full article

Early detection of plant water status is crucial for efficient crop management. In this research, proximal sensing tools (i.e., hyperspectral imaging HSI and thermal IR camera) were used to monitor changes in spectral and thermal profiles of a citrus orchard in Sicily (Italy), managed under five irrigation systems. The irrigation systems differ in the amount of water distribution and allow four different strategies of deficit irrigation to be obtained. The physiological traits, stem water potential, net photosynthetic rate, stomatal conductance and the amount of leaf chlorophyll were measured over the crop’s growing season for each treatment. The proximal sensing data consisted of thermal and hyperspectral imagery acquired in June–September during the irrigation seasons 2023–2024 and 2024–2025. Significant variation in physiological traits was observed in relation to the different irrigation strategies, highlighting the highest plant water stress in July, in particular for the partial root-zone drying irrigation system. The water-use efficiency (WUE) values in subsurface drip irrigation were similar to the moderate deficit irrigation treatment and more efficient (up to 50%) as compared to control. Proximal sensing measures confirmed a different plant water status in relation to the five different irrigations strategies. Moreover, four spectral indices (Normalized Difference Vegetation Index NDVI; Water Index WI; Photochemical Reflectance Index PRI; Transformed Chlorophyll Absorption Ratio Index TCARI), calculated from HSI spectra, highlighted strong correlations with physiological traits, especially with stem water potential and the amount of leaf chlorophyll (coefficient of correlation ranged between −0.4 and −0.5). This study demonstrated the effectiveness of using proximal sensing tools in precision agriculture and ecosystem monitoring, helping to ensure optimal plant health and water use efficiency. Full article

Soybean cultivation plays a crucial role in the global food system, providing raw materials for both the food and feed industries. To enhance cultivation efficiency, plant biostimulants are used to improve metabolism and stimulate growth. A key aspect of modern cultivation is the ability to rapidly and non-invasively assess crop status. One such method involves the use of drones equipped with multispectral cameras. This paper presents the results of an experimental study on soybean cultivation involving a natural biostimulant in the form of Epilobium angustifolium extract (commonly known as fireweed) and a commercial seaweed-based biostimulant, Kelpak. The research was conducted at an experimental farm in eastern Poland. The effectiveness of the preparations was evaluated using a drone-mounted multispectral camera. Changes in the values of selected spectral indices were analyzed: the Normalized Difference Red Edge Index (NDRE), the Leaf Chlorophyll Index (LCI), and the Optimized Soil-Adjusted Vegetation Index (OSAVI). The study included a control group treated with pure water. Mathematical and statistical analyses of the mean values and standard deviations of the indices were conducted. The results demonstrated that multispectral scanning allows for the detection of significant differences between the effects of the E. angustifolium extract, the seaweed-based biostimulant, and the water control. These findings confirm the utility of this method for assessing the effectiveness of biostimulant applications in soybean cultivation. Full article

The water status of plants is affected by abiotic and biotic environmental factors and influences the growth and yield formation of crops. Assessment of the leaf water content (LWC) of grapevine using hyperspectral imaging (1000–2500 nm) was investigated under controlled conditions for its potential to study the effects of the downy mildew pathogen Plasmopara viticola on LWC of host tissue in compatible and incompatible interactions. A calibration curve was established for the relationship between LWC and the Normalized Difference Leaf Water Index (NDLWI₁₉₃₇) that uses spectral information from the water absorption band and NIR for normalization. LWC was significantly lower for abaxial than for adaxial leaf sides, irrespective of grapevine genotype and health status. Reflecting details of leaf anatomy, vascular tissue exhibited effects reverse to intercostal areas. Effects of P. viticola on LWC coincided with the appearance of first sporangia on the abaxial side and increased during further pathogenesis. Continuous water loss ultimately resulted in tissue death, which progressed from the margins into central leaf areas. Tiny spots of brown leaf tissue related to the reaction of partial resistant cultivars could be monitored only at the sensor’s highest spatial resolution. Proximal sensing enabled an unprecedented spatial resolution of leaf water content in host–pathogen interactions and confirmed that resistance reactions may produce a combination of dead and still-living cells that enable the development of biotrophic P. viticola. Full article

Despite the abundance of available studies on optical and microwave methods devoted to investigating agricultural crop conditions, there is a lack of research that explores the integration between microwave and optical data and the link between photosynthetic activity, measured by PRI (photochemical reflectance index), and vegetation water content, detected by radar sensors. In particular, there is a lack of vision that links these measures to better understand how plants react and adapt to possible water stress conditions. Most of the existing research tends to treat optical and microwave information separately, without investigating how the integration of these techniques can provide a more complete and accurate understanding of the research topic, corroborated by ground data. In this paper, an integrated approach using microwave and optical satellite data, respectively acquired by Sentinel-1 (S-1) and Sentinel-2 (S-2), was presented for monitoring vegetation status. Experimental data and electromagnetic models have been combined to relate backscattering from S-1 and optical indices from S-2 to plant conditions, which were evaluated by measuring PRI, plant water content (PWC), and soil water content. Field data were collected in two sorghum fields close to Florence in Tuscany (Central Italy) during the summers of 2022 and 2023. The results show significant correlations between microwave and optical data with respect to field measurements, highlighting the potential of remote sensing techniques for agricultural monitoring and management, also in response to climate change. Determination coefficients of R² = 0.51 between PRI and PWC, where PWC is retrieved by S-1, and R² = 0.73 between PSRI (plant senescence reflectance index) and PRI were obtained. Full article

The observation time and water background can affect the remote sensing estimates of the nitrogen (N) content in rice crops. This makes the use of vegetation indices (VIs) for N status monitoring and topdressing recommendations challenging, as the timing of panicle initiation and the water level in bays usually differ between farms even when managed using the same irrigation technique. This study aimed to investigate the influence of standing water levels (from 0 to 20 cm) and the time of image acquisition on a set of N-sensitive VIs to identify those less affected by these factors. The experiment was conducted using a split-plot experimental design with two side-by-side bays (main plots) where rice was grown ponded for most of the growing season and aerobically (not permanently ponded), each with four fertilization N rates. The SCCCI and SCCCI² were the only indices that did not vary depending on the time of the day when the multispectral images were collected. These indices showed the lowest variation among water layer treatments (5%), while the Clg index showed the highest (20%). All VIs were significantly correlated with N uptake (average R² = 0.73). However, the SCCCI² was the index that showed the lowest variation in N-uptake estimates resulting in equal N-fertilizer recommendations across water level treatments. The consistent performance of SCCCI² across different water levels makes this index of interest for different irrigation strategies, including aerobic management, which is gaining increasing attention to improve the sustainability of the rice industry. Full article

Field studies and satellite imagery were conducted on an oat cultivation field located on sandy soil with significant spatial heterogeneity in southwestern Poland. Observations and field measurements were carried out during the BBCH growth stages 12, 31, 49, 77, and 99 at 40 points each. Satellite images were acquired at specific intervals, and selected remote sensing indices (NDVI, GNDVI, SAVI, EVI, NDMI, MCARI) were calculated to investigate possibility of early detection of nitrogen demand at the early stage of oat development. The results of this study confirmed that sandy soils, characterized by limited water and nutrient capacity, require a specialized approach to resource management. The selected remote sensing indices provided an effective method for monitoring oat canopy variability in real time. At BBCH 12 growing stage, the highest correlations with plant density were shown by NDVI, SAVI, GNDVI, and EVI. The correlation coefficients ranged from 0.38 to 0.56, with a significance level of ≤0.01, which indicates their usefulness for monitoring crop emergency and early development. At early growing stage (BBCH 31–34), GNDVI was significantly correlated with the final nitrogen uptake (r = 0.44, p < 0.01) and biomass yield of oat (r = 0.39, p = 0.01). This suggests that the GNDVI index is particularly useful for predicting the final nitrogen uptake and biomass yield of oat. It offers a reliable estimation of the plant’s nitrogen status and its potential for nitrogen absorption, allowing for fertilization management at this critical stage. Full article

Water scarcity in the Mediterranean significantly affects the sustainability of citrus cultivation in eastern Sicily, a key production area in Italy. Innovative monitoring approaches are crucial for assessing citrus water status and applying precise irrigation strategies. This study evaluates the potential of low-cost proximal sensors based on thermal infrared (TIR) (e.g., canopy temperature, T_c; ΔT; crop water stress index, CWSI) and visible near-infrared (VNIR) (e.g., normalized difference vegetation index, NDVI) data, combined with stem water potential (SWP), for determining citrus water status proxies across four fields under different water regimes (full irrigation, FI, and deficit irrigation, DI) and cultivar/rootstock combinations. Temporal and spatial differences were detected for most variables during the irrigation season. A 6% decrease in NDVI corresponded to higher T_c values in July (up to 37.6 °C). CWSI highlighted cumulative water deficits, reaching 0.65 ± 0.15 in September. More negative SWP values (−1.91 ± 0.38 MPa) were found under DI compared to FI (−1.70 ± 0.17 MPa) conditions. Microclimatic differences influenced ΔT, with lower values in fields 3–4, despite site-specific SWP, NDVI, and T_c variations. The use of VNIR and TIR tools provided valuable insights for describing the spatial and temporal variability of citrus water status indicators under Mediterranean conditions, supporting their sustainable irrigation management. Full article

Daily water stress reflects the water stress status of crops on a specific day, which is crucial for studying drought progression and guiding precision irrigation. However, accurately monitoring the daily water stress remains challenging, particularly when eliminating the impact of historical stress and normal growth. Recent studies have demonstrated that the diurnal characteristics of the crop canopy obtained via remote sensing techniques can be used to assess daily water stress levels effectively. Remote sensing observations, such as the solar-induced chlorophyll fluorescence (SIF) and reflectance, offer information on the crop canopy structure, physiology, or their combination. However, the sensitivity of different structural, physiological, or combined remote sensing variables to the daily water stress remains unclear. We investigated this issue via continuous measurements of the active fluorescence, leaf rolling, and canopy spectra of maize under different irrigation conditions. The results indicated that with increasing water stress, vegetation exhibited significant coordinated diurnal variations in both structure and physiology. The influence of water stress was minimal in the morning but peaked at noon. The morning-to-noon ratio (NMR) of the apparent SIF yield (SIFy), in which only the effect of the photosynthetically active radiation (PAR) is eliminated and in which both structural and physiological information is incorporated, exhibited the highest sensitivity to water stress variations. This NMR of the SIFy was followed by the NMR of the normalized difference vegetation index ($\mathrm{N}\mathrm{D}\mathrm{V}\mathrm{I}$) and the NMR of the canopy fluorescence emission efficiency ($\mathsf{\Phi }{\mathrm{F}}_{\mathrm{c}\mathrm{a}\mathrm{n}\mathrm{o}\mathrm{p}\mathrm{y}}$) obtained via the fluorescence correction vegetation index (FCVI) method, which primarily reflect structural and physiological information, respectively. This study highlights the advantages of utilizing diurnal vegetation structural and physiological variations for monitoring daily water stress levels. Full article

The color of the plant leaves is a major concern in many areas of agriculture. Pigmentation and its pattern provide the possibility to distinguish genotypes and a basis for annual crop management practices. For example, the nutrient and water status of plants is reflected in the chlorophyll content of leaves that are strongly linked to the lamina coloration. Pests and diseases (virus or bacterial infections) also cause symptoms on the foliage. These symptoms induced by biotic and abiotic stressors often have a specific pattern, which allows for their prediction based on remote sensing. In this report, an RGB (red, green and blue) image processing system is presented to determine leaf lamina color variability based on RGB-based color indices. LeafLaminaMap was developed in Scilab with the Image Processing and Computer Vision toolbox, and the code is available freely at GitHub. The software uses RGB images to visualize 29 color indices and the R, G and B values on the lamina, as well as to calculate the statistical parameters. In this case study, symptomatic (senescence, fungal infection, etc.) and healthy grapevine (Vitis vinifera L.) leaves were collected, digitalized and analyzed with the LeafLaminaMap software according to the mean, standard deviation, contrast, energy and entropy of each channel (R, G and B) and color index. As an output for each original image in the sample set, the program generates 32 images, where each pixel is constructed using index values calculated from the RGB values of the corresponding pixel in the original image. These generated images can subsequently be used to help the end-user identify locally occurring symptoms that may not be visible in the original RGB image. The statistical evaluation of the samples showed significant differences in the color pattern between the healthy and symptomatic samples. According to the F value of the ANOVA analysis, energy and entropy had the largest difference between the healthy and symptomatic samples. Linear discriminant analysis (LDA) and support vector machine (SVM) analysis provided a perfect recognition in calibration and confirmed that energy and entropy have the strongest discriminative power between the healthy and symptomatic samples. The case study showed that the LeafLaminaMap software is an effective environment for the leaf lamina color pattern analysis; moreover, the results underline that energy and entropy are valuable features and could be more effective than the mean and standard deviation of the color properties. Full article

Vegetable production in intensive protected agriculture systems has evolved due to its intensity and economic importance. Sensors are increasingly common for decision-making in crop management and control of environmental variables, obtaining optimal yields, such as estimating vegetation indices. Innovation and technological advances in unmanned vehicle platforms have improved spatial, spectral, and temporal resolution. However, in protected agriculture systems, the use is limited due to the assumption of having controlled environmental conditions for indeterminate vegetable production. Therefore, sequential monitoring of NDVI is proposed during the 2022 and 2023 agricultural cycles using the Green Seeker^® sensor and agronomic variables. This has created a database to generate predictive models of development and yield as a function of nutrient status. The results obtained indicate high significance levels for the development and NDVI curves in all phenological stages; in contrast to the yield predictive models, this is due to the maximum values (close to one) recorded for NDVI inside the greenhouse in comparison to the yield prediction obtained from the 18th week of harvest. Evaluating the models between NDVI and agronomic variables is not an index that offers certainty in predicting yield in indeterminate crops in protected agriculture production systems. This is due to the constant optimal development in response to controlled environmental conditions, nutrient status, and water supply inside the greenhouse, without the sustainability of yield, which decreases in the final stages of production until production becomes economically unprofitable. Full article

Non-thermal plasma technology (NTP) has found widespread applications across several fields, including agriculture. Researchers have explored the use of NTP to improve plant growth and increase agricultural product quality using plasma-activated water (PAW). This technology has shown potential benefits in boosting seed germination, promoting plant growth, as an effective defense against plant pathogens, and increasing systemic plant resistance. An experiment was set up over three different cultivation cycles to investigate the benefits of PAW administration on nursery production. Plasma-activated water was generated using two NTP intensities (PAW-HI = 600 mV; PAW-LI = 450 mV; CTR = tap water control) and manually applied to plants under greenhouse conditions. The species considered in the current study were tomato (Solanum lycopersicum L.), Swiss chard (Beta vulgaris L.), cabbage (Brassica oleracea L.), basil (Ocimum basilicum L.), and lettuce (Lactuca sativa L. var. Longifolia). The following morphological traits were measured at the end of each cycle and for each species: plant height (PH, cm), collar diameter (CD, mm), biomass (g), nutritional status (SPAD index), dry matter (DM, %), and chemical composition. The sturdiness index (SI) was determined by the PH-to-CD ratio. Results indicated a species-specific response to both PAW treatments compared to CTR. The plant height significantly increased in tomato (+11.9%) and cabbage (+5%) under PAW-HI treatment. In contrast, PAW-HI treatment negatively affected the PH in lettuce and basil (−18% and −9%, respectively). Swiss chard showed no significant response to either PAW-LI or PAW-HI treatments. Regarding DM, no significant differences were observed between the PAW treatments and CTR. However, an increase in total N content was detected in plant tissues across all species, except for basil, where no change was observed. The results suggest that PAW treatment has the potential to enhance vegetable nursery production, with species-specific responses observed in crops. Full article