Search Results (225)

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

Quinoa (Chenopodium quinoa Willd.) has emerged as a climate-resilient, nutrient-dense crop with increasing global popularity because of its adaptability under current environmental variations. To address the limited understanding of quinoa’s genotypic performance under local agro-environmental conditions, this study hypothesized that elite genotypes would exhibit significant variation in agronomic, physiological, and biochemical traits. This study aimed to elucidate genotypic variability among 23 elite quinoa lines under field conditions in Faisalabad, Pakistan, using a multidimensional framework that integrated phenological, physiological, biochemical, root developmental, and yield-related attributes. The results revealed that significant variation was observed across all measured parameters, highlighting the diverse adaptive strategies and functional capacities among the tested genotypes. More specifically, genotypes Q4, Q11, Q15, and Q126 demonstrated superior agronomic potential and canopy-level physiological efficiencies, including high biomass accumulation, low infrared canopy temperatures and sustained NDVI values. Moreover, Q9 and Q52 showed enhanced accumulation of antioxidant compounds such as phenolics and anthocyanins, suggesting potential for functional food applications and breeding program for improving these traits in high-yielding varieties. Furthermore, root trait analysis revealed Q15, Q24, and Q82 with well-developed root systems, suggesting efficient resource acquisition and sufficient support for above-ground plant parts. Moreover, principal component analysis further clarified genotype clustering based on trait synergistic effects. These findings support the use of multidimensional phenotyping to identify ideotypes with high yield potential, physiological efficiency and nutritional value. The study provides a foundational basis for quinoa improvement programs targeting climate adaptability and quality enhancement. Full article

Precision irrigation plays a crucial role in managing crop production in a sustainable and environmentally friendly manner. This study builds on the results of the GreenWaterDrone project, aiming to estimate, in real time, the actual water requirements of crop fields using the crop water stress index, integrating infrared canopy temperature, air temperature, relative humidity, and thermal and near-infrared imagery. To achieve this, a state-of-the-art aerial micrometeorological station (AMMS), equipped with an infrared thermal sensor, temperature–humidity sensor, and advanced multispectral and thermal cameras is mounted on an unmanned aerial system (UAS), thus minimizing crop field intervention and permanently installed equipment maintenance. Additionally, data from satellite systems and ground micrometeorological stations (GMMS) are integrated to enhance and upscale system results from the local field to the regional level. The research was conducted over two years of pilot testing in the municipality of Trifilia (Peloponnese, Greece) on pilot potato and watermelon crops, which are primary cultivations in the region. Results revealed that empirical irrigation applied to the rhizosphere significantly exceeded crop water needs, with over-irrigation exceeding by 390% the maximum requirement in the case of potato. Furthermore, correlations between high-resolution remote and proximal sensors were strong, while associations with coarser Landsat 8 satellite data, to upscale the local pilot field experimental results, were moderate. By applying a comprehensive model for upscaling pilot field results, to the overall Trifilia region, project findings proved adequate for supporting sustainable irrigation planning through simulation scenarios. The results of this study, in the context of the overall services introduced by the project, provide valuable insights for farmers, agricultural scientists, and local/regional authorities and stakeholders, facilitating improved regional water management and sustainable agricultural policies. Full article

Transpiration is the dominant process driving water loss in crops, significantly influencing their growth, development, and yield. Efficient monitoring of transpiration rate (Tr) is crucial for evaluating crop physiological status and optimizing water management strategies. The three-temperature (3T) model has potential for rapid estimation of transpiration rates, but its application to low-altitude remote sensing has not yet been further investigated. To evaluate the performance of 3T model based on land surface temperature (LST) and canopy temperature (T_C) in estimating transpiration rate, this study utilized an unmanned aerial vehicle (UAV) equipped with a thermal infrared (TIR) camera to capture TIR images of summer maize during the nodulation-irrigation stage under four different moisture treatments, from which LST was extracted. The Gaussian Hidden Markov Random Field (GHMRF) model was applied to segment the TIR images, facilitating the extraction of T_C. Finally, an improved 3T model incorporating fractional vegetation coverage (FVC) was proposed. The findings of the study demonstrate that: (1) The GHMRF model offers an effective approach for TIR image segmentation. The mechanism of thermal TIR segmentation implemented by the GHMRF model is explored. The results indicate that when the potential energy function parameter β value is 0.1, the optimal performance is provided. (2) The feasibility of utilizing UAV-based TIR remote sensing in conjunction with the 3T model for estimating Tr has been demonstrated, showing a significant correlation between the measured and the estimated transpiration rate (T_r-3T_C), derived from T_C data obtained through the segmentation and processing of TIR imagery. The correlation coefficients (r) were 0.946 in 2022 and 0.872 in 2023. (3) The improved 3T model has demonstrated its ability to enhance the estimation accuracy of crop Tr rapidly and effectively, exhibiting a robust correlation with T_r-3T_C. The correlation coefficients for the two observed years are 0.991 and 0.989, respectively, while the model maintains low RMSE of 0.756 mmol H₂O m⁻² s⁻¹ and 0.555 mmol H₂O m⁻² s⁻¹ for the respective years, indicating strong interannual stability. Full article

Canopy temperature is an important indicator of plants’ water status. The so-called PeriodiCT model was developed to forecast canopy temperature using ambient weather variables, providing a powerful tool for planning crop irrigation scheduling. As this model requires observed data in its parameter training before implementing the forecast, it is important to understand the data requirements in the model training such that accurate forecasts are attained. In this work, we conduct a comprehensive assessment of the PeriodiCT model in terms of sample size requirement and predictabilities across sensors in a field and across seasons for the full model and sub-models. The results show that (1) 5 days’ observations are sufficient for the full model and sub-models to achieve very high predictability, with a minimum coefficient of efficiency of 0.844 for the full model and 0.840 for the sub-model using only air temperature. The predictability decreases in the following order: full model, sub-model without radiation S, with air temperature Ta and vapor pressure VP, and with only Ta. The predictions perform reasonably well even when only one day’s observations are used. (2) The predictability into the future is very stable as the prediction steps increase. (3) The predictabilities of the full and sub-models when using a trained model from one sensor for another sensor perform comparatively well, with a minimum coefficient of efficiency of 0.719 for the full model and 0.635 for the sub-model using only air temperature. (4) The predictabilities of the sub-models without solar radiation when using trained models from one season for another season perform comparatively well, with a minimum coefficient of efficiency of 0.866 for the full model and 0.764 for the sub-model using only air temperature, although the cross-season performances are not as good as the cross-sensor performances. The importance of the predictors is in the order of air temperature, vapor pressure, wind speed, and solar radiation, while vapor pressure and wind speed have similar contributions, and solar radiation has only a marginal contribution. Full article

The increasing global demand for resilient, sustainable agricultural systems has intensified the need for advanced monitoring strategies, particularly for climate-adaptive crops such as Moringa oleifera Lam. This study presents an integrated approach using Unmanned Aerial Vehicles (UAVs) equipped with multispectral and thermal cameras to monitor the vegetative performance and determine the optimal harvest period of four M. oleifera genotypes in a Mediterranean environment. High-resolution data were collected and processed to generate the NDVI, canopy temperature, and height maps, enabling the assessment of plant vigor, stress conditions, and spatial canopy structure. NDVI analysis revealed robust vegetative growth (0.7–0.9), with optimal harvest timing identified on 30 October 2024, when the mean NDVI exceeded 0.85. Thermal imaging effectively discriminated plant crowns from surrounding weeds by capturing cooler canopy zones due to active transpiration. A clear inverse correlation between NDVI and Land Surface Temperature (LST) was observed, reinforcing its relevance for stress diagnostics and environmental monitoring. The results underscore the value of UAV-based multi-sensor systems for precision agriculture, offering scalable tools for phenotyping, harvest optimization, and sustainable management of medicinal and aromatic crops in semiarid regions. Moreover, in this study, to produce M. oleifera leaf powder intended for use as a food ingredient, the leaves of four M. oleifera genotypes were dried, milled, and evaluated for their hygiene and safety characteristics. Plate count analyses confirmed the absence of pathogenic bacterial colonies in the M. oleifera leaf powders, highlighting their potential application as natural and functional additives in food production. Full article

The fig (Ficus carica L.) is one of the oldest fruit crops cultivated in arid and semi-arid regions, valued for both its nutritional and economic importance; thus, ensuring sustainable fig production under climate change conditions is very important, as water scarcity increasingly affects fruit quality and production. Selecting and preserving resilient varieties among traditional varieties, representing centuries of local adaptation, is a vital strategy for addressing the challenges driven by climate change. In this context, this study assessed the physiological and biochemical parameters of the leaves of four fig landrace varieties (Fassi, Ghouddane, Nabout, and Ounq Hmam) grown in three different Mediterranean transitional zones of northern Morocco (Chefchaouen, Taounate, and Taza), during a single timepoint assessment conducted in late August 2023. The combined effects of location, variety, and their interactions on chlorophyll fluorescence (F_v/F_m), Soil Plant Analysis Development (SPAD) index, total chlorophyll content (ChlT), canopy temperature depression (CTD), proline content, protein content, total soluble sugar (TSS), hydrogen peroxide (H₂O₂), and malondialdehyde (MDA) were determined. Significant variation was observed among varieties and locations, with the location effect being observed for proline content, protein content, TSS, CTD, and ChlT, while variety had a stronger influence on SPAD, F_v/F_m, H₂O₂, and MDA. The results showed that Nabout and Ounq Hmam varieties had the greatest photosynthetic efficiency, as indicated by their elevated SPAD index, ChlT, and F_v/F_m values, and showed lower sensitivity to oxidative stress (low proline content, H₂O₂, and MDA levels). In contrast, Ghouddane and Fassi displayed better stress tolerance, presenting higher levels of oxidative stress markers. Among locations, Chefchaouen showed the highest protein, TSS, H₂O₂, and MDA levels, reflecting active stress tolerance mechanisms. These variations were confirmed by principal component analysis, which revealed a clear separation between photosynthetically efficient varieties (Nabout and Ounq Hmam) and stress-tolerant varieties (Ghouddane and Fassi). More than a conventional crop physiology study, this work highlights the adaptive strategies in traditional Mediterranean fig germplasm that could be crucial for climate change adaptation. While our findings are limited to a single season, they offer valuable, practical insights that can inform grower decision-making in the near term, especially when considered alongside local knowledge and additional research. Full article

Cultivating crops in the hot, arid conditions of the Arabian Peninsula often requires irrigation with brackish water, which exposes plants to salinity and heat stress. Timely, cost-effective monitoring of plant health can significantly enhance crop management. In this context, remote sensing techniques offer promising alternatives. This study evaluates several low-cost, ground-level remote sensing methods and compares them with benchmark analytical techniques for assessing salt stress in two economically important woody species, moringa and pomegranate. The species were irrigated under three salinity levels: low (2 dS m⁻¹), medium (5 dS m⁻¹), and high (10 dS m⁻¹). Remote sensing tools included RGB, multispectral, and thermal cameras mounted on selfie sticks for canopy imaging, as well as portable leaf pigment and chlorophyll fluorescence meters. Analytical benchmarks included sodium (Na) accumulation, carbon isotope composition (δ¹³C), and nitrogen (N) concentration in leaf dry matter. As salinity increased from low to medium, canopy temperatures, vegetation indices, and δ¹³C values rose. However, increasing salinity from medium to high levels led to a rise in Na accumulation without further significant changes in other remote sensing and analytical parameters. In moringa and across the three salinity levels, the Normalized Difference Red Edge (NDRE) and leaf chlorophyll content on an area basis showed significant correlations with δ¹³C (r = 0.758, p < 0.001; r = 0.423, p < 0.05) and N (r = 0.482, p < 0.01; r = 0.520, p < 0.01). In pomegranate, the Normalized Difference Vegetation Index (NDVI) and chlorophyll were strongly correlated with δ¹³C (r = 0.633, p < 0.01 and r = 0.767, p < 0.001) and N (r = 0.832, p < 0.001 and r = 0.770, p < 0.001). Remote sensing was particularly effective at detecting plant responses between low and medium salinity, with stronger correlations observed in pomegranate. Full article

Cowpea (Vigna unguiculata (L.) Walp.) is a vital legume crop recognized for its nutritional value and adaptability to various growing conditions. However, exposure of cowpea to drought stress during the early growth stages can significantly restrict growth and yield potential. Therefore, identifying cowpea genotypes tolerant to drought during early growth and development is essential for maintaining yield potential. This study characterized 15 diverse cowpea genotypes for various physiological, pigment, and morphological traits that may contribute to drought tolerance. At the V2 stage, the cowpea genotypes were subjected to two moisture regimes: control (100% irrigation) and drought (50% irrigation) for 22 days to assess trait responses and their relationship to drought tolerance. Drought-stressed plants decreased stomatal conductance by 79%, negatively correlating with a 2.9 °C increase in canopy temperature. Under drought, the photochemical reflectance index (PRI) was strongly associated with the quantum yield of PSII and electron transport rate. Shoot biomass decreased by 51% and root biomass by 32% under drought. Leaf area and shoot weight were correlated with root traits such as total length, surface area, and weight. Among all genotypes, 280785-11 and UCR 1004 demonstrated superior rooting vigor under drought, emphasizing their efficiency in resource utilization. These findings highlight the relevance of utilizing drought-adaptive traits to improve early-season drought tolerance. Full article

Precision irrigation requires reliable estimates of crop evapotranspiration (ET) using site-specific crop and weather data inputs. Such estimates are needed at high resolutions which have been minimally explored for heterogeneous crops such as orchards. In addition, weather information for estimating ET is very often selected from sources that do not represent conditions like heterogeneous site-specific conditions. Therefore, a study was conducted to map geospatial ET and transpiration (T) of a high-density modern apple orchard using high-resolution aerial imagery, as well as to quantify the impact of site-specific weather conditions on the estimates. Five campaigns were conducted in the 2020 growing season to acquire small unmanned aerial system (UAS)-based thermal and multispectral imagery data. The imagery and open-field weather data (solar radiation, air temperature, wind speed, relative humidity, and precipitation) inputs were used in a modified energy balance (UASM-1 approach) extracted from the Mapping ET at High Resolution with Internalized Calibration (METRIC) model. Tree trunk water potential measurements were used as reference to evaluate T estimates mapped using the UASM-1 approach. UASM-1-derived T estimates had very strong correlations (Pearson correlation [r]: 0.85) with the ground-reference measurements. Ground reference measurements also had strong agreement with the reference ET calculated using the Penman–Monteith method and in situ weather data (r: 0.89). UASM-1-based ET and T estimates were also similar to conventional Landsat-METRIC (LM) and the standard crop coefficient approaches, respectively, showing correlation in the range of 0.82–0.95 and normalized root mean square differences [RMSD] of 13–16%. UASM-1 was then modified (termed as UASM-2) to ingest a locally calibrated leaf area index function. This modification deviated the components of the energy balance by ~13.5% but not the final T estimates (r: 1, RMSD: 5%). Next, impacts of representative and non-representative weather information were also evaluated on crop water uses estimates. For this, UASM-2 was used to evaluate the effects of weather data inputs acquired from sources near and within the orchard block on T estimates. Minimal variations in T estimates were observed for weather data inputs from open-field stations at 1 and 3 km where correlation coefficients (r) ranged within 0.85–0.97 and RMSD within 3–13% relative to the station at the orchard-center (5 m above ground level). Overall, the results suggest that weather data from within 5 km radius of orchard site, with similar topography and microclimate attributes, when used in conjunction with high-resolution aerial imagery could be useful for reliable apple canopy transpiration estimation for pertinent site-specific irrigation management. Full article

Frost poses a significant threat to agricultural production, leading to reduced crop yields and deterioration in quality. This review systematically provides an overview of the types and causes of plant frost, and delves into the principles, research progress, and application status of three key active frost protection (FP) technologies: air disturbance, sprinkler irrigation, and heating. It also scrutinizes the challenges faced by current FP equipment, such as high costs, complex maintenance, and noise pollution. Air disturbance technology utilizes fans to mix upper and lower air layers, increasing the canopy temperature, with research focusing on fan optimization and unmanned aerial vehicle (UAV) application. Sprinkler irrigation technology releases latent heat through water freezing, with research centering on water saving and automation. Heating technology directly supplies heat, with attention on heat source optimization and mobile heating strategies. Finally, this review outlines the development trends of plant FP equipment and technologies, highlighting the promising application prospects of agricultural UAVs in FP, which can have multi-purpose use and effectively reduce costs. Full article

Drip irrigation technology, known for its advantages in high water use efficiency and yield increase, has been a focal point of research regarding its combined effects with the plastic film-mulching technique on field water consumption and crop growth. To accurately quantify the water-saving effect of plastic film-mulching techniques and investigate the mechanisms of mulching on evaporation (E) and transpiration (T), this study was conducted on soybean using the Bowen ratio–energy balance system and micro-lysimeters as the observation means and the MSW model as the data partitioning tool, during 2019–2021 in arid northwest China. We compared evapotranspiration (ET) under the film-mulched drip irrigation (FM) and non-mulched drip irrigation (NM) treatments. The results show that ET, E, and T under FM were reduced by 32.6 mm, 76.1 mm, and −43.5 mm, respectively. Moreover, mulching increased the leaf area index (LAI) by 20.7%, soybean yield from 2727.0 kg ha⁻¹ to 3250.5 kg ha⁻¹, and WUE from 0.64 kg m⁻³ to 0.83 kg m⁻³ on average, which means mulching reduced water consumption in the field by decreasing soil evaporation and improved water use efficiency by promoting crop growth. Further analysis indicated that mulching has strengthened the connection between soil temperature and humidity and weakened the effect of soil temperature on soybean leaf growth. Soil water content (SWC) and LAI had a direct negative effect on E, with LAI causing a stronger effect on E under the FM treatment. Mulching has weakened the direct effect of SWC on T, so that only LAI and soil temperature had a significant direct positive effect on T. Following the rapid growth of soybean LAI, the isolating effect of the mulch was gradually replaced by the dense leaf canopy. The results provide a reference for further exploring the water-saving and yield-increasing benefits of plastic film-mulching techniques, and to facilitate wider promotion of the plastic film-mulching techniques and the water–fertilizer integration technology in arid regions. Full article

Alfalfa is a deep-rooted perennial forage crop with diverse drought-tolerant traits. This study evaluated 250 alfalfa half-sib populations over three growing seasons (2021–2023) under irrigated and rainfed conditions in the Mediterranean drought-prone region of Central Chile (Cauquenes), aiming to identify high-yielding, drought-tolerant populations using remote sensing. Specifically, we assessed RGB-derived indices and canopy temperature difference (CTD; Tc − Ta) as proxies for forage yield (FY). The results showed considerable variation in FY across populations. Under rainfed conditions, winter FY ranged from 1.4 to 6.1 Mg ha⁻¹ and total FY from 3.7 to 14.7 Mg ha⁻¹. Under irrigation, winter FY reached up to 8.2 Mg ha⁻¹ and total FY up to 25.1 Mg ha⁻¹. The AlfaL4-5 (SARDI7), AlfaL57-7 (WL903), and AlfaL62-9 (Baldrich350) populations consistently produced the highest yields across regimes. RGB indices such as hue, saturation, b*, v*, GA, and GGA positively correlated with FY, while intensity, lightness, a*, and u* correlated negatively. CTD showed a significant negative correlation with FY across all seasons and water regimes. These findings highlight the potential of RGB imaging and CTD as effective, high-throughput field phenotyping tools for selecting drought-resilient alfalfa genotypes in Mediterranean environments. Full article

In a plant factory, maintaining proper and uniform air/moisture movement above the crop canopy is crucial for aiding plant growth. This research has utilized a three-dimensional computation model to investigate airflow and heat transfer in a plant factory, where airflow, heat, and humidity distributions above plant crops were calculated concerning five categories of crop planting density (P_d) and air change rate (ACH) in the crop area. Spatial uniformities of airflow velocity, temperature, and relative humidity immediately above the crops are evaluated using the objective uniformity parameter (OU), relative standard deviation of temperature (RSD_T) and relative standard deviation of relative humidity (RSD_RH), respectively. Furthermore, a factor of effectiveness (θ) is defined, depending on the uniformity of velocity, temperature, and relative humidity distribution, to comprehensively evaluate the impact of various ACH with P_d on overall effectiveness. Full numerical results show that air velocity, temperature, and relative humidity above the crops are notably influenced by P_d and ACH. As ACH increases, the OU of the air above the indoor crop also expands. Moreover, higher OU values are observed for smaller crop P_d. However, excessively small crop area planting densities and excessively large ACH do not result in a higher OU for the air above the crop. As ACH increases, both RSD_T and RSD_RH decay for the whole range of crop P_d. Moreover, smaller P_d values could achieve the uniformity of thermal fields, while having minimal effects on the relative humidity distributions. Generally, increasing ACH and decreasing P_d could enhance overall value of θ. However, excessively increasing ACH and decreasing P_d does not have a significant effect on θ, which is jointly influenced by OU, RSD_T, and RSD_RH. Therefore, a more suitable combination of ACH and P_d is urgently required to improve the design of agricultural system to enhance crop microclimate uniformity for optimal plant growth and productivity. Full article

Understanding the spatial–temporal distribution of photosynthetically active radiation (PAR) within crop canopies is crucial for optimizing planting structures to enhance resource use efficiency and improve crop yields. While high planting densities are commonly employed to increase yields, this practice can lead to issues such as early leaf senescence and reduced biomass. This study investigates the impact of varying planting densities on PAR dynamics, canopy structure, and yield formation in maize over two years. Key findings include the following: (1) higher planting density significantly increased grain yield, biological yield, and LAI, although HI decreased; (2) canopy light distribution varied with planting density, with the middle layers intercepting the most light, particularly during the grain filling stage; (3) a density of 83,000 plants·ha⁻¹ was the most efficient for maximizing yield and WUE, although high accumulated temperatures negatively impacted yields. These results suggest that adjusting planting density can enhance resource use efficiency in maize farming, particularly in regions with variable water availability and climate challenges. Future research should explore the long-term effects of planting density on soil health, water use efficiency, and crop resilience under varying environmental conditions. Additionally, studies integrating precision agriculture technologies to fine-tune planting density and water management in response to climate change are essential for ensuring sustainable maize production and food security in the future. Full article