Editor’s Choice Articles

Low-temperature stress has become an important abiotic factor affecting high and stable wheat production. Therefore, it is necessary to take appropriate measures to enhance low-temperature tolerance in wheat. A pot experiment was carried out using Yannong19 (YN19, a cold-tolerant cultivar) and Xinmai26 (XM26, a cold-sensitive cultivar). We employed traditional phosphorus application (TPA, i.e., R1) and optimized phosphorus application (OPA, i.e., R2) methods. Plants undertook chilling (T1 at 4 °C) and freezing treatment (T2 at −4 °C) as well as ambient temperature (CK at 11 °C) during the anther differentiation period to investigate the effects of OPA and TPA on photosynthetic parameters and the accumulation and distribution of dry matter. The net photosynthetic rate (Pn), stomatal conductance (Gs) and transpiration rate (Tr) of flag leaves decreased in low-temperature treatments, whereas intercellular carbon dioxide concentration (Ci) increased. Compared with R1CK, Pn in R1T1 and R1T2 treatments was reduced by 26.8% and 42.2% in YN19 and 34.2% and 54.7% in XM26, respectively. In contrast, it increased by 6.5%, 8.9% and 12.7% in YN19 and 7.7%, 15.6% and 22.6% in XM26 for R2CK, R2T1 and R2T2 treatments, respectively, under OPA compared with TPA at the same temperature treatments. Moreover, low-temperature stress reduced dry matter accumulation at the reproductive growth stage. OPA increased dry matter accumulation of vegetative organs after the flowering stage and promoted the transportation of assimilates to grains. Hence, the grain number per spike (GNPS), 1000-grain weight (TGW) and yield per plant (YPP) increased. The low-temperature treatments of T1 and T2 caused yield losses of 24.1~64.1%, and the yield increased by 8.6~20.5% under OPA treatments among the two wheat cultivars. In brief, OPA enhances low-temperature tolerance in wheat, effectively improves wheat architecture and photosynthesis, increases GNPS and TGW and ultimately lessens yield losses. Full article

This study was conducted to determine the fatty acid (FA) content in pasture grasses, legumes, and non-leguminous forbs in northeast West Virginia. Grass, legume, and forb plant material were collected from rotationally stocked pastures and analyzed for crude protein (CP), linoleic acid (C18:2), α-linolenic acid (C18:3), and total FA content. Species within botanical classes varied in FA content. Forbs had the highest linoleic acid (C18:2) content followed by legume and grass species. Grasses and forbs had the highest α-linolenic acid (C18:3) content. Forbs had the highest total FA content. These field data were combined with FA data from the research literature to evaluate the correlation of CP concentration with fatty acid concentration. Likewise, after accounting for CP, the summer months caused a decrease while forbs caused an increase in α-linolenic acid (C18:3) content. Vegetative growth and leafiness are the major determinants of FA content in pasture forage. Grazing management to benefit vegetative growth and the presence of desirable forbs in tune with seasonal changes are valuable tools to increase desirable FA profiles in milk and meat products that may be of benefit to human health. Full article

Vicia sativa L. (common vetch) is an annual legume species of high economic and ecological importance which is characterized by high nutritive value for animal feeding and its ability to adapt to various edaphic–climatic conditions. However, limited information is available about genotypic and environmental effects on agronomic, nutritional, and antinutritional traits of common vetch genotypes. Thus, in the present study, four advanced breeding lines and three commercial cultivars were evaluated for yield biomass, color assessment, fiber, crude protein (CP), and polyphenols in three locations (Spata, Larissa, and Thessaloniki) for two consecutive growing seasons (2018–2019 and 2019–2020). The effects of genotype, environment and their interaction (GXE) were significant for all the studied traits. The main source of variation for yield, color, CP, and polyphenols was the environment as it explained 71.5–89.7% of the total variation, whereas for the fibers content it was the GXE interaction. On the other hand, genotype had a much smaller effect on all the traits studied (2.9–16.6%). According to GGE biplot analysis, the ‘Alexandros’ cultivar was the most high-yielding and stable, whereas ‘Leonidas’ was the best performing in terms of nutritional and antinutritional traits. However, one advanced line combined high and stable yield biomass with high nutritive value, indicating the possibility for simultaneous improvement of both features. Full article

Halauxifen-methyl is a new auxin herbicide developed by Corteva Agriscience (Wilmington, DE, USA). It has been suggested that ABF5 may be the target of halauxifen-methyl, as AFB5 mutants of Arabidopsis thaliana are resistant to halauxifen-methyl, which preferentially binds to AFB5. However, the mode of action of halauxifen-methyl has not yet been reported. Therefore, the aim of the present study was to reveal the mode of action of halauxifen-methyl by exploring its influence on indole-3-acetic acid (IAA) homeostasis and the biosynthesis of ethylene and Abscisic Acid (ABA) in Galium aparine. The results showed that halauxifen-methyl could disrupt the homeostasis of IAA and stimulate the overproduction of ethylene and ABA by inducing the overexpression of the 1-aminocyclopropane-1-carboxylate synthase (ACS) and 9-cis-epoxycarotenoid dioxygenase (NCED) genes involved in ethylene and ABA biosynthesis, finally leading to senescence and plant death. Full article

Cassava (Manihot esculenta Crantz) is a rainfed, smallholder-produced crop in mainland Southeast Asia, and is currently facing a serious challenge posed by the introduction of cassava mosaic disease (CMD). This study assessed the susceptibility of popular Asian varieties to CMD, yield penalties associated with the disease, and the efficacy of selecting clean or asymptomatic plants as seed for the following season. Field experiments evaluated agronomic management practices (i.e., fertilizer application, use of symptomatic and asymptomatic seed stakes) in Cambodia with six to nine popular varieties over three seasons under natural disease pressure. Popular cassava varieties KU50 and Huaybong60 showed superior CMD tolerance, with consistently fewer symptomatic plants, lower disease progress measures, and higher yields. Plants demonstrating symptoms at early stages of development, i.e., 60 days after planting, yielded significantly less than those developing symptoms later (i.e., 270 DAP) or not at all. Plants grown from clean stems yielded on average 20% to 2.7-fold higher than those grown from symptomatic planting material. A yield decline of ~50% was recorded with symptomatic planting materials of susceptible varieties (e.g., SC8, ~25 t ha⁻¹) over successive years. The findings emphasize that farmers could use positive selection by choosing asymptomatic plants to significantly reduce yield losses. Full article

Salvia hispanica L., commonly known as chia, is a plant-based alternative to seafood and is rich in heart-healthy omega-3 fatty acid, protein, fiber, and antioxidants. In the Northern Hemisphere, chia flowering is triggered by the fall equinox (12-h light and dark, early October) and the seeds mature after approximately three months. Chia is sensitive to frost and end of season moisture which limits its cultivation to small areas in regions with temperate climate. The U.S. chia import has increased considerably over the years; however, chia is not widely cultivated in the United States. This study used the historical U.S. temperature and precipitation data as a first step to explore the potential of widescale chia cultivation. The 10th percentiles of 25 mm precipitation level as well as soft frost (32 °F: 0 °C) and hard frost (28 °F: −2.2 °C) were tabulated for the months of November and December. The results identified temperature as the main limiting factor for chia cultivation in the United States. The commercial chia variety (harvested in December) can be planted on approximately 10,000 km² cropland (1,000,000 hectare) in the United States. The future development of early flowering variety (harvested in November) was demonstrated to open an additional 44,000 km² (4,400,000 hectares) for chia cultivation in the United States. In conclusion, chia cultivation could provide economic benefits to U.S. farmers both by enriching the diversity within crop rotations aimed at reducing pest and pathogen populations and by its high economic value as an alternative specialty crop. Full article

Adjusting yearly pruning severity is a common vineyard management practice employed to manipulate vegetative and reproductive growth in grapevines. Although the effects of pruning on total vegetative growth are well documented, there is little research on the effects of adjusting shoots meter⁻¹ via dormant season pruning on addressing mid-cordon shoot weakness and developmental delays. Cordon-trained, spur-pruned vines are thought, by many growers, to be especially prone to weaker positions and delayed development at mid-cordon positions. This phenomenon is also thought to become more exaggerated as the vine ages. Therefore, the effects of shoot density manipulation, implemented via dormant pruning practices, to homogenize shoot and cluster development along the length of the cordon were examined. In this research, Cabernet Sauvignon grapevines were pruned to either 5.5 shoots meter⁻¹ (5.5) or 11.1 shoots meter⁻¹ (11.1). To control for variations in light interception into the fruiting zone, a control of 11.1 shoots meter⁻¹ with sensor guided leaf thinning (11.1LT) was implemented at full berry set to match the canopy light of the 5.5 shoots meter⁻¹ treatment. It was found that individual shoot growth and yield were directly impacted by manipulation of pruning severity. Shoot growth response varied primarily by growing season, including shoot length and internode length. Yield components were significantly lower in the 5.5 treatment during the first two years of the study but were not significantly different during the last year of the study. The 5.5 treatment resulted in the highest pH and total soluble solids at harvest in 2016 and 2017. Full article

The maturity level of tomato is a key factor of tomato picking, which directly determines the transportation distance, storage time, and market freshness of postharvest tomato. In view of the lack of studies on tomato maturity classification under nature greenhouse environment, this paper proposes a SE-YOLOv3-MobileNetV1 network to classify four kinds of tomato maturity. The proposed maturity classification model is improved in terms of speed and accuracy: (1) Speed: Depthwise separable convolution is used. (2) Accuracy: Mosaic data augmentation, K-means clustering algorithm, and the Squeeze-and-Excitation attention mechanism module are used. To verify the detection performance, the proposed model is compared with the current mainstream models, such as YOLOv3, YOLOv3-MobileNetV1, and YOLOv5 in terms of accuracy and speed. The SE-YOLOv3-MobileNetV1 model is able to distinguish tomatoes in four kinds of maturity, the mean average precision value of tomato reaches 97.5%. The detection speed of the proposed model is 278.6 and 236.8 ms faster than the YOLOv3 and YOLOv5 model. In addition, the proposed model is considerably lighter than YOLOv3 and YOLOv5, which meets the need of embedded development, and provides a reference for tomato maturity classification of tomato harvesting robot. Full article

Co-application of Aspergillus niger (A. niger) and phosphate rocks (PR) has been practiced by environmentalists for lead (Pb) remediation. The secretion of organic acid by A. niger usually dominates the dissolution of PR and Pb immobilization. In this study, two types of PR (fluorapatite (FAp) and phosphogypsum (PG)) were investigated in Pb remediation by A. niger under three different forms of nitrogen (ammonium, nitrate, and urea). Our results reveal that the formation of pyromorphite and lead oxalate contributed to Pb removal by the combination of A. niger with FAp and PG. PG showed a significant capability for Pb remediation compared with FAP, over 94% of Pb vs. 50%. Compared with nitrate and urea, ammonium significantly decreased Pb cation concentrations from 1500 mg/L to 0.4 mg/L. Due to ammonium containing sulfate, the lead sulfate formed also contributed to Pb removal. However, nitrate stimulated A. niger to secrete more oxalic acid (~1400 mg/L) than ammonium and urea (~200 mg/L), which can form insoluble lead oxalate. These insoluble minerals can reduce the availability of removed Pb. Despite the efficacy of both ammonium and nitrate for Pb remediation, our findings suggest that nitrate is the primary candidate in this regard due to high oxalic acid secretion. Full article

The mechanism of greenhouse tomato growth and yield under the integrated water and fertilizer of moistube irrigation (MI) is not clear. Thus, to fill the research gap, a completely randomized trial design was used to study the effects of different irrigation amounts (I; to realize different I, the tube working pressure was 1 (I1), 2 (I2), 3 (I3) m) and fertilizer amounts (F, N-P-K: 20%-20%-20%; the F at a single time was 100 (F1), 200 (F2) and 300 (F3) kg/ha) on growth and yield of tomato. The results showed that with an increase in I, the photosynthetic rate (Pn) of leaves and total dry matter mass (TDM) first increased and then decreased, while the nutrition and the flavor indexes of fruit decreased. With an increase in F, the Pn of leaves, the TDM of tomato and the fruit quality increased at first and then decreased. The effects of I on the yield of tomato was higher than that of F. With an increase in I, the partial fertilizer productivity (PFP) increased at first and then decreased, and the water use efficiency (WUE) decreased by 13.96%. With an increase in F, the WUE increased at first and then decreased, and the PFP decreased by 148.97%. The conclusion based on a spatial analysis was consistent with the comprehensive evaluation of yield and water use efficiency, which showed that I2F2 was the best. Full article

In recent years, early blight epidemics have been frequently causing important yield loses in potato crop. This fungal disease develops quickly when weather conditions are favorable, forcing the use of fungicides by farmers. A Limia is one of the largest areas for potato production in Spain. Usually, early blight epidemics are controlled using pre-established schedule calendars. This strategy is expensive and can affect the environment of agricultural areas. Decision support systems are not currently in place to be used by farmers for managing early blight. Thus, the objective of this research was to evaluate different early blight forecasting models based on plant or/and pathogen requirements and weather conditions to check their suitability for predicting the first symptoms of early blight, which is necessary to determine the timings of the first fungicide application. For this, weather, phenology and symptomatology of disease were monitored throughout five crop seasons. The first early blight symptoms appeared starting the flowering stage, between 37 and 40 days after emergence of plants. The forecasting models that were based on plants offered the best results. Specifically, the Wang-Engel model, with 1.4 risk units and Growing Degree-Days (361 cumulative units) offeredthe best prediction. The pathogen-based models showed a conservative forecast, whereas the models that integrated both plant and pathogen features forecasted the first early blight attack markedly later. Full article

The productivity of alfalfa is associated with a large amount of nitrogen (N) and phosphorus (P); the addition of exogenous N and P fertilizers can fully exploit the growth potential of alfalfa. However, there is uncertainty about the relationship between changes in alfalfa productivity and photosynthetic physiology and anatomy. We conducted field fertilization experiments on alfalfa in the second and third years under drip irrigation, as well as measurement of the photosynthetic physiology, anatomical structure and agronomic traits of alfalfa at different levels of N (0, 120 kg·ha⁻¹) and different levels of P₂O₅ (0, 50, 100 and 150 kg·ha⁻¹). The results showed that the dry matter yield (DMY), crude protein (CP), net photosynthetic rate (P_n) and specific leaf weight (SLW) were increased by 2.10~11.82%, 4.95~11.93%, 4.71~7.59% and 2.02~7.12% in the N application treatment compared with the non-N application treatment, while the DMY, CP, P_n and SLW were increased by 3.19~17.46%, 1.99~8.42%, 6.15~24.95% and 2.16~11.90% in the P application treatment compared with the non-P application treatment. N and P increase the thickness of the spongy tissue (ST) of alfalfa, which will facilitate the entry and exit of gas and water, and will further affect the photosynthetic indexes, such as stomatal conductance (G_s) and transpiration rate (T_r), of alfalfa leaves. Increased palisade tissue (PT) thickness will also enhance the adaptability of plant leaves to strong sunlight, thereby increasing the maximum net photosynthetic rate (P_max) and light saturation point (LSP). Fertilization treatment showed the highest utilization efficiency for low light and better adaptation to strong light, but the R_d decreased. The comprehensive scores of principal component analysis for anatomical structure, photosynthetic performance and agronomic traits were N₁P₂ > N₀P₂ > N₁P₃ > N₁P₁ > N₀P₃ > N₀P₁ > N₁P₀ > N₀P₀. Therefore, the application of N and P fertilizers contributed to the adaptive changes in alfalfa leaf anatomy and the improvement of photosynthetic capacity, which were beneficial to the improvement of alfalfa dry matter yield, growth traits and nutritional quality, with the most obvious improvement effect obtained with the application of 120 kg·ha⁻¹ of N and 100 kg·ha⁻¹ of P₂O₅. Full article

For the automated robotic picking of bunch-type fruit, the strategy is to roughly determine the location of the bunches, plan the picking route from a remote location, and then locate the picking point precisely at a more appropriate, closer location. The latter can reduce the amount of information to be processed and obtain more precise and detailed features, thus improving the accuracy of the vision system. In this study, a long-close distance coordination control strategy for a litchi picking robot was proposed based on an Intel Realsense D435i camera combined with a point cloud map collected by the camera. The YOLOv5 object detection network and DBSCAN point cloud clustering method were used to determine the location of bunch fruits at a long distance to then deduce the sequence of picking. After reaching the close-distance position, the Mask RCNN instance segmentation method was used to segment the more distinctive bifurcate stems in the field of view. By processing segmentation masks, a dual reference model of “Point + Line” was proposed, which guided picking by the robotic arm. Compared with existing studies, this strategy took into account the advantages and disadvantages of depth cameras. By experimenting with the complete process, the density-clustering approach in long distance was able to classify different bunches at a closer distance, while a success rate of 88.46% was achieved during fruit-bearing branch locating. This was an exploratory work that provided a theoretical and technical reference for future research on fruit-picking robots. Full article

In this study, the effect of different photosynthetic photon flux density (PPFD) provided by LEDs (Light Emitting Diodes) and photoperiod on biomass production, morphological traits, photosynthetic performance, sensory attributes, and image texture parameters of indoor cultivated romaine lettuce was evaluated. Two cultivars of lettuce Lactuca sativa var. longifolium namely ‘Casual’ (Syngenta)—midi romaine lettuce with medium-compact heads—and ‘Elizium’ (Enza Zaden)—a mini type (Little Gem) with compact heavy heads—were used. PPFD of 160 and 240 µmol m⁻² s⁻¹ and photoperiod of 16 and 20 h were applied, and Daily Light Integral (DLI) values were 9.2, 11.5, 13.8, and 17.3 mol m⁻² day⁻¹. The experiment lasted 30 days in the Indoor Controlled Environment Agriculture facility. DLI equal to 17.3 mol m⁻² per day for cv. ‘Casual’ and 11.5–17.3 mol m⁻² per day for cv. ‘Elizium’ allowed to obtain a very high fresh weight, 350 and 240 g, respectively, within 30 days of cultivation in an indoor plant production facility. The application of the lowest PPFD 160 µmol m⁻² s⁻¹ and 16 h photoperiod (9.2 mol m⁻² per day DLI) resulted in the lowest fresh weight, the number of leaves and head circumference. The level of nitrate, even at the lowest DLI, was below the limit imposed by European Community Regulation. The cv. ‘Elizium’ lettuce grown at PPFD 240 µmol m⁻² s⁻¹ and 16 h photoperiod had the highest overall sensory quality. The cv. ‘Casual’ lettuce grown at PPFD 160 µmol m⁻² s⁻¹ and 20 h photoperiod had the lowest sensory quality. The samples subjected to different photoperiod and PPFD were also successively distinguished in an objective and non-destructive way using image features and machine learning algorithms. The average accuracy for the leaf samples of cv. ‘Casual’ lettuce reached 98.75% and for cv. ‘Elizium’ cultivar—86.25%. The obtained relationship between DLI and yield, as well as the quality of romaine lettuce, can be used in practice to improve romaine lettuce production in an Indoor Controlled Environment. Full article

Twenty-four hybrids, obtained from a mating design following 6 line × 4 testers, were evaluated to estimate the heterosis, specific, and general combining ability (SCA and GCA) of parents and hybrids to find out suitable general combiner (GC) parents and cross combinations for utilization in the future breeding program. A randomized complete block design with three replications was followed to set the experiment. Data were recorded on grain yield and 13 yield-related agronomic traits. The analysis of variance of all cross combinations had highly significant differences for most of the characters studied, which indicated a wide variation across the genotypes, parents, lines, testers, and crosses. SCA and GCA variances were significant for all studied traits except for the panicle length, indicating that both non-additive and additive gene actions were involved in these traits. The GCA variance/SCA variance for all the traits was <1, signifying the multitude of dominant and epistatic gene actions. The GCA effects of three lines GAN46A, IR58025A, IR62629A, and a tester IR46R were significant for the majority of the agronomic traits including grain yield and might be used for improving the yield of grains in rice as parents of excellent GC. Based on the yield of grains and agronomic traits, the hybrids IR58025A × IR46R and GAN46A × IR46R might be considered the best hybrids and another nine hybrids could also be considered good hybrids. Similarly, based on the yield of grains and agronomic traits, the positive and significant mid-parent, better parent, and standard heterosis were obtained from 3 F₁s, 1 F₁, and 3 F₁s, respectively. Heterosis and combining ability study revealed that hybrids IR58025A × IR46R and GAN46A × IR46R might be considered preferable hybrid cultivars. Full article

The reduction of copper-based plant-protection products with the final aim of phasing out has a high priority in European policy, as well as in organic agriculture. Our survey aims at providing an overview of the current use of these products in European organic agriculture and the need for alternatives to allow policymakers to develop strategies for a complete phasing out. Due to a lack of centralized databases on pesticide use, our survey combines expert knowledge on permitted and real copper use per crop and country, with statistics on organic area. In the 12 surveyed countries (Belgium, Bulgaria, Denmark, Estonia, France, Germany, Hungary, Italy, Norway, Spain, Switzerland, and the UK), we calculated that approximately 3258 t copper metal per year is consumed by organic agriculture, equaling to 52% of the permitted annual dosage. This amount is split between olives (1263 t y⁻¹, 39%), grapevine (990 t y⁻¹, 30%), and almonds (317 t y⁻¹, 10%), followed by other crops with much smaller annual uses (<80 t y⁻¹). In 56% of the allowed cases (countries × crops), farmers use less than half of the allowed amount, and in 27%, they use less than a quarter. At the time being, completely abandoning copper fungicides would lead to high yield losses in many crops. To successfully reduce or avoid copper use, all preventive strategies have to be fully implemented, breeding programs need to be intensified, and several affordable alternative products need to be brought to the market. Full article

Due to the nonlinear modeling capabilities, deep learning prediction networks have become widely used for smart agriculture. Because the sensing data has noise and complex nonlinearity, it is still an open topic to improve its performance. This paper proposes a Reversible Automatic Selection Normalization (RASN) network, integrating the normalization and renormalization layer to evaluate and select the normalization module of the prediction model. The prediction accuracy has been improved effectively by scaling and translating the input with learnable parameters. The application results of the prediction show that the model has good prediction ability and adaptability for the greenhouse in the Smart Agriculture System. Full article

Reference crop evapotranspiration (ET_o) is an important component of the hydrological cycle that is used for water resource planning, irrigation, and agricultural management, as well as in other hydrological processes. The aim of this study was to estimate the ET_o based on limited meteorological data using an artificial neural network (ANN) method. The daily data of minimum temperature (T_min), maximum temperature (T_max), mean temperature (T_mean), solar radiation (SR), humidity (H), wind speed (WS), sunshine hours (Ssh), maximum global radiation (grad_max), minimum global radiation (grad_min), day length, and ET_o data were obtained over the long-term period from 1969 to 2019. The analysed data were divided into two parts from 1969 to 2007 and from 2008 to 2019 for model training and testing, respectively. The optimal ANN for forecasting ET_o included T_max, T_min, H, and SR at hidden layers (4, 3); grad_min, SR, and WS at (6, 4); SR, day length, Ssh, and T_mean at (3, 2); all collected parameters at hidden layer (5, 4). The results showed different alternative methods for estimation of ET_o in case of a lack of climate data with high performance. Models using ANN can help promote the decision-making for water managers, designers, and development planners. Full article

Severe water stress conditions limit growth and development of floricultural crops which affects flower quality. Hence, development of effective approaches for drought tolerance is crucial to limit recurring water deficit challenges. Foliar application of various plant growth regulators has been evaluated to improve drought tolerance in different floricultural crops; however, reports regarding the role of chitosan (Ci) on seasonal flowers like calendula are still scant. Therefore, we evaluated the role of Ci foliar application on morphological, physiological, biochemical, and anatomical parameters of calendula under water stress conditions. Different doses of Ci (0, 2.5, 5, 7.5, 10 mg L⁻¹) were applied through foliar application to evaluate their impact in enhancing growth and photosynthetic pigments of calendula. The optimized Ci level of 7.5 mg L⁻¹ was further evaluated to study mechanisms of water stress tolerance in calendula. Ci application significantly increased biomass and pigments in calendula. Ci (7.5 mg L⁻¹) resulted in increased photosynthetic rate (72.98%), transpiration rate (62.11%), stomatal conductance (59.54%), sub-stomatal conductance (20.62%), and water use efficiency (84.93%). Furthermore, it improved catalase, guaiacol peroxidase, and superoxide dismutase by 56.70%, 64.94%, and 32.41%, respectively. These results highlighted the significance of Ci in inducing drought tolerance in pot marigold. Full article

The use of nanocarriers (NCs), i.e., nanomaterials capable of encapsulating drugs and releasing them selectively, is an emerging field in agriculture. In this study, the synthesis, characterization, and in vitro and in vivo testing of biodegradable NCs loaded with natural bioactive products was investigated for the control of certain phytopathogens responsible for wood degradation. In particular, NCs based on methacrylated lignin and chitosan oligomers, loaded with extracts from Rubia tinctorum, Silybum marianum, Equisetum arvense, and Urtica dioica, were first assayed in vitro against Neofusicoccum parvum, an aggressive fungus that causes cankers and diebacks in numerous woody hosts around the world. The in vitro antimicrobial activity of the most effective treatment was further explored against another fungal pathogen and two bacteria related to trunk diseases: Diplodia seriata, Xylophilus ampelinus, and Pseudomonas syringae pv. syringae, respectively. Subsequently, it was evaluated in field conditions, in which it was applied by endotherapy for the control of grapevine trunk diseases. In the in vitro mycelial growth inhibition tests, the NCs loaded with R. tinctorum resulted in EC₉₀ concentrations of 65.8 and 91.0 μg·mL⁻¹ against N. parvum and D. seriata, respectively. Concerning their antibacterial activity, a minimum inhibitory concentration of 37.5 μg·mL⁻¹ was obtained for this treatment against both phytopathogens. Upon application via endotherapy on 20-year-old grapevines with clear esca and Botryosphaeria decay symptoms, no phytotoxicity effects were observed (according to SPAD and chlorophyll fluorescence measurements) and the sugar content of the grape juice was not affected either. Nonetheless, the treatment led to a noticeable decrease in foliar symptoms as well as a higher yield in the treated arms as compared to the control arms (3177 vs. 1932 g/arm), suggestive of high efficacy. Given the advantages in terms of controlled release and antimicrobial product savings, these biodegradable NCs loaded with natural extracts may deserve further research in large-scale field tests. Full article

Biofertilizers, such as arbuscular mycorrhiza fungi (AMF) and seaweed extract (SWE), have been effective in environmental and agricultural ecosystems. In this study, the effects of AMF, SWE, and their co-application were assayed on the growth and antioxidant potential of lettuce plants. The experiment was conducted as a factorial based on a completely randomized design with two factors and four replications under greenhouse conditions. The first factor was AMF (Glomus mosseae) at two levels consisting of AMF application (20 g pot⁻¹), and without using AMF; and the second factor was SWE foliar spraying (Ascophyllum nodosum) at 0.5, 1.5 and 3 g L⁻¹ concentration. The results revealed that the highest root colonization (85%) belonged to AMF and SWE (3 g L⁻¹) × AMF; the lowest colonization rate (65%) was observed for AMF × SWE (0.5 g L⁻¹) treatment. The highest growth parameters (leaf number, shoot and root fresh weight, head diameter), biochemical traits (total soluble proteins, carbohydrates content) and TAA, total antioxidant activity by FRAP method and ascorbic acid, total phenolics, and flavonoids content were obtained with the co-applications. Therefore, the best results of the evaluated traits were achieved with AMF × SWE (3 g L⁻¹). The TAA value was increased three-fold compared to the control. Total phenolics and flavonoids content were 2.24 and 6.59 times higher than the control, respectively. On the other hand, leaf dry weight was decreased with the further growth of the plants. Overall, the co-application of AMF with SWE can be recommended to producers as an alternative and environment-friendly strategy to improve the qualitative and quantitative traits of the lettuce crop. Full article

Germination and seedling development are essential stages in a plant’s life cycle, greatly influenced by temperature and moisture conditions. The aim of this study was to determine maize (Zea mays L.) seeds’ germination and seedling development under various abiotic stresses. Eight different temperature levels, 5, 10, 15, 20, 25, 30, 35, and 40 °C, were used. Drought and waterlogging stresses were tested using 30 water levels based on one-milliliter intervals and as percentages of thousand kernel weight (TKW) at 20 and 25 °C. Seedling density and the use of antifungals were also examined. Temperature significantly affected germination duration and seedling growth, and 20 °C was found to be ideal with an optimal range of less than 30 °C. Germination occurred at 25% of the TKW. The optimal water range for seedling growth was higher and broader than the range for germination. Seed size assisted in defining germination water requirements and providing an accurate basis. The present research established an optimum water supply range of 150–325% of the TKW for maize seedling development. A total of 6 seeds per 9 cm Petri dish may be preferable over greater densities. The technique of priming seeds with an antifungal solution before planting was observed to have a better effect than applying it in the growth media. Full article

The real-time detection of banana bunches and stalks in banana orchards is a key technology in the application of agricultural robots. The complex conditions of the orchard make accurate detection a difficult task, and the light weight of the deep learning network is an application trend. This study proposes and compares two improved YOLOv4 neural network detection models in a banana orchard. One is the YOLO-Banana detection model, which analyzes banana characteristics and network structure to prune the less important network layers; the other is the YOLO-Banana-l4 detection model, which, by adding a YOLO head layer to the pruned network structure, explores the impact of a four-scale prediction structure on the pruning network. The results show that YOLO-Banana and YOLO-Banana-l4 could reduce the network weight and shorten the detection time compared with YOLOv4. Furthermore, YOLO-Banana detection model has the best performance, with good detection accuracy for banana bunches and stalks in the natural environment. The average precision (AP) values of the YOLO-Banana detection model on banana bunches and stalks are 98.4% and 85.98%, and the mean average precision (mAP) of the detection model is 92.19%. The model weight is reduced from 244 to 137 MB, and the detection time is shortened from 44.96 to 35.33 ms. In short, the network is lightweight and has good real-time performance and application prospects in intelligent management and automatic harvesting in the banana orchard. Full article

An integrated method is required for comprehensive assessment of the environmental impacts and economic benefits of rice production systems. Therefore, the objective of this study was to apply different footprinting approaches (carbon footprint (CF), nitrogen footprint (NF), water footprint (WF)) and determine the economic return on organic rice farming (OF) and conventional rice farming (CVF) at the farm scale. Over the 4-year study period (2018–2021), the results showed lower net greenhouse gas (GHG) emissions in OF (3289.1 kg CO₂eq ha⁻¹ year⁻¹) than in CVF (4921.7 kg CO₂eq ha⁻¹ year⁻¹), indicating that the use of OF can mitigate the GHG emissions from soil carbon sequestration. However, there was a higher CF intensity in OF (1.17 kg CO₂eq kg⁻¹ rice yield) than in CVF (0.93 kg CO₂eq kg⁻¹ rice yield) due to the lower yield. The NF intensities of OF and CVF were 0.34 and 11.94 kg Neq kg⁻¹ rice yield, respectively. The total WF of CVF (1470.1 m³ ton⁻¹) was higher than that in OF (1216.3 m³ ton⁻¹). The gray water in CVF was significantly higher than that in OF due to the use of chemical fertilizers, herbicides, and pesticides. Although the rice yield in OF was nearly two times lower than that in CVF, the economic return was higher due to lower production costs and higher rice prices. However, more field studies and long-term monitoring are needed for future research. Full article

The harvesting operation is a recurring task in the production of any crop, thus making it an excellent candidate for automation. In protected horticulture, one of the crops with high added value is tomatoes. However, its robotic harvesting is still far from maturity. That said, the development of an accurate fruit detection system is a crucial step towards achieving fully automated robotic harvesting. Deep Learning (DL) and detection frameworks like Single Shot MultiBox Detector (SSD) or You Only Look Once (YOLO) are more robust and accurate alternatives with better response to highly complex scenarios. The use of DL can be easily used to detect tomatoes, but when their classification is intended, the task becomes harsh, demanding a huge amount of data. Therefore, this paper proposes the use of DL models (SSD MobileNet v2 and YOLOv4) to efficiently detect the tomatoes and compare those systems with a proposed histogram-based HSV colour space model to classify each tomato and determine its ripening stage, through two image datasets acquired. Regarding detection, both models obtained promising results, with the YOLOv4 model standing out with an F1-Score of 85.81%. For classification task the YOLOv4 was again the best model with an Macro F1-Score of 74.16%. The HSV colour space model outperformed the SSD MobileNet v2 model, obtaining results similar to the YOLOv4 model, with a Balanced Accuracy of 68.10%. Full article

To accurately recognize plant diseases under complex natural conditions, an improved plant disease-recognition model based on the original YOLOv5 network model was established. First, a new InvolutionBottleneck module was used to reduce the numbers of parameters and calculations, and to capture long-distance information in the space. Second, an SE module was added to improve the sensitivity of the model to channel features. Finally, the loss function ‘Generalized Intersection over Union’ was changed to ‘Efficient Intersection over Union’ to address the former’s degeneration into ‘Intersection over Union’. These proposed methods were used to improve the target recognition effect of the network model. In the experimental phase, to verify the effectiveness of the model, sample images were randomly selected from the constructed rubber tree disease database to form training and test sets. The test results showed that the mean average precision of the improved YOLOv5 network reached 70%, which is 5.4% higher than that of the original YOLOv5 network. The precision values of this model for powdery mildew and anthracnose detection were 86.5% and 86.8%, respectively. The overall detection performance of the improved YOLOv5 network was significantly better compared with those of the original YOLOv5 and the YOLOX_nano network models. The improved model accurately identified plant diseases under natural conditions, and it provides a technical reference for the prevention and control of plant diseases. Full article

Over the last few years, several Convolutional Neural Networks for object detection have been proposed, characterised by different accuracy and speed. In viticulture, yield estimation and prediction is used for efficient crop management, taking advantage of precision viticulture techniques. Convolutional Neural Networks for object detection represent an alternative methodology for grape yield estimation, which usually relies on manual harvesting of sample plants. In this paper, six versions of the You Only Look Once (YOLO) object detection algorithm (YOLOv3, YOLOv3-tiny, YOLOv4, YOLOv4-tiny, YOLOv5x, and YOLOv5s) were evaluated for real-time bunch detection and counting in grapes. White grape varieties were chosen for this study, as the identification of white berries on a leaf background is trickier than red berries. YOLO models were trained using a heterogeneous dataset populated by images retrieved from open datasets and acquired on the field in several illumination conditions, background, and growth stages. Results have shown that YOLOv5x and YOLOv4 achieved an F1-score of 0.76 and 0.77, respectively, with a detection speed of 31 and 32 FPS. Differently, YOLO5s and YOLOv4-tiny achieved an F1-score of 0.76 and 0.69, respectively, with a detection speed of 61 and 196 FPS. The final YOLOv5x model for bunch number, obtained considering bunch occlusion, was able to estimate the number of bunches per plant with an average error of 13.3% per vine. The best combination of accuracy and speed was achieved by YOLOv4-tiny, which should be considered for real-time grape yield estimation, while YOLOv3 was affected by a False Positive–False Negative compensation, which decreased the RMSE. Full article

Water scarcity is a major challenge to wheat productivity under changing climate conditions, especially in arid and semi-arid regions. During recent years, different agronomic, physiological and molecular approaches have been used to overcome the problems related to drought stress. Breeding approaches, including conventional and modern breeding, are among the most efficient options to overcome drought stress through the development of new varieties adapted to drought. Growing drought-tolerant wheat genotypes may be a sustainable option to boost wheat productivity under drought stress conditions. Therefore, the present study was conducted with the aim to screen different wheat genotypes based on stress tolerance levels. For this purpose, eleven commonly cultivated wheat genotypes (V₁ = Akbar-2019, V₂ = Ghazi-2019, V₃ = Ujala-2016, V₄ = Zincol-2016, V₅ = Anaj-2017, V₆ = Galaxy-2013, V₇ = Pakistan-2013, V₈ = Seher-2006, V₉ = Lasani-2008, V₁₀ = Faisalabad-2008 and V₁₁ = Millat-2011) were grown in pots filled with soil under well-watered (WW, 70% of field capacity) and water stress (WS, 35% of field capacity) conditions. Treatments were arranged under a completely randomized design (CRD) with three replicates. Data on yield and yield-related traits (tillers/plant, spikelets/spike, grains/spike, 100 grain weight, seed and biological yield) and physio-biochemical (chlorophyll contents, relative water content, membrane stability index, leaf nitrogen, phosphorus, and potassium content) attributes were recorded in this experiment. Our results showed that drought stress significantly affected the morpho-physiological, and biochemical attributes in all tested wheat varieties. Among the genotypes, all traits were found to be significantly (p < 0.05) higher in wheat genotype Faisalabad-2008, including biological yield (9.50 g plant⁻¹) and seed yield (3.39 g plant⁻¹), which was also proven to be more drought tolerant than the other tested genotypes. The higher biological and grain yield of genotype Faisalabad-2008 was mainly attributed to greater numbers of tillers/plant and spikelets/spike compared to the other tested genotypes. The wheat genotype Galaxy-2013 had significantly lower biological (7.43 g plant⁻¹) and seed yield (2.11 g plant⁻¹) than all other tested genotypes, and was classified as a drought-sensitive genotype. For the genotypes, under drought stress, biological and grain yield decreased in the order V₁₀ > V₂ > V₁ > V₄ > V₇ > V₁₁ > V₉ > V₈ > V₃ > V₆. These results suggest that screening for drought-tolerant genotypes may be a more viable option to minimize drought-induced effects on wheat in drought-prone regions. Full article

Biostimulators with chemical protection are a challenge in sustainable agriculture to obtain high yield, healthy, and pesticide-free wheat. The aim of this four-year spring wheat field experiment was to assess the effectivity of using herbicide, mixed fungicides protection, and a humic biostimulator. The following treatments were tested: biostimulator (S), sulfosulfuron (H), H + S, H + propiconazole + cyproconazole/spiroxamin + tebuconazole + triadimenol (H + F1 + F2), and H + F1 + F2 + S. Evaluations of wheat yield and fungal diseases (Septoria tritici blotch, eyespot, sharp eyespot, Fusarium spp.) were performed using visual and qPCR methods. Thirteen mycotoxins were analyzed by LC–MS/MS. Infestations of six weeds were examined visually. Temperatures and precipitation data of the vegetative seasons were monitored. Precipitation most affected the occurrence of leaf diseases despite the same chemical/biostimulator treatments (up to 48% Septoria tritici blotch severity for the S treatment). The highest mean yield was obtained for H + F1 + F2 + S (5.27 t ha⁻¹), while the lowest level of mycotoxins was obtained for H + F1 + F2 (221.68 µg kg⁻¹). For H + S, a greater reduction of mycotoxins was determined compared to the H treatment (27.18%), as well as a higher severity of eyespot (18%) and sharp eyespot (24%). In 2017–2020, the most effective reduction of weed infestation and Fusarium spp. DNA on ears was indicated for H + F1 + F2 (16 g and 0.88 pg g⁻¹ DNA, respectively). The greatest saved production value (196.15€) was determined for H + F1 + F2 + S. Full article

In recent decades, increasing human pressure has caused the gradual deterioration of the physical and chemical properties of water and soil. Salinity is an important factor influencing the quality of water. The aim of this comprehensive research was to determine the effect of increasing concentrations of sodium chloride, which is a salinity inducer, on the yield, photosynthesis efficiency (expressed with chlorophyll fluorescence measurement) and content of selected nutrients in the leaves of hydroponically grown lettuce (Lactuca sativa L.). Experiments were conducted at the following concentrations of NaCl: 0 (control treatment), 10, 20, 40, and 60 mmol L⁻¹. Studies were conducted in two independent seasons: spring and autumn. The plants exposed to NaCl stress modified their chemical composition by lowering the uptake of (for 60 mmol L⁻¹ NaCl in relation to control): N (−11%), K (−35.7%), and Mg (−24.5%), while increasing the sodium content (+2400%). The Na:K ratio was significantly narrowed (from 76:1 to 2.6:1). The increase in the Cl level in the lettuce leaves may also have caused a decrease in the content of nitrates. As a result of disturbed ionic balance, the RWC was significantly reduced (−6.2%). As a result of these changes, the yield of the biomass of the aerial parts decreased (more than two-fold for the highest NaCl concentration in relation to control) whereas the dry matter content increased (+32%). The measurement of fluorescence showed significant changes at the PSII level. Salinity modified the energy flow rate (F₀, F_M, F_V, F_V/F_M) as well as the specific energy flows through the reaction centre (ABS/RC, TR₀/RC, ET₀/RC, DI₀/RC). The PSII functioning index, calculated on the basis of energy absorption (PI_Abs), also changed. The salinity induced with NaCl significantly worsened the physiological reactions of the plants in the PSII, changed the ionic balance, which resulted in a significantly lower yield of the plants. Due to increasing water quality problems, it will be necessary to use, in agriculture on a much larger scale than before, saline water treatment systems (e.g., highly effective nanofiltration and/or reverse osmosis). Full article

In the tropics, warm temperatures and high rainfall contribute to acidic soil formation because of the significant leaching of base cations (K⁺, Ca²⁺, Mg²⁺, and Na⁺), followed by the replacement of the base cations with Al³⁺, Fe²⁺, and H⁺ ions at the soil adsorption sites. The pH buffering capacity of highly weathered acid soils is generally low because of their low pH which negatively impacts soil and crop productivity. Thus, there is a need to amend these soils with the right amount of inorganic liming materials which have relatively high neutralizing values and reactivity to overcome the aforementioned problems. Soil leaching and the pH buffering capacity studies were conducted to determine whether the co-application or co-amendment of a calcium carbonate product (Calciprill) and sodium silicate can improve soil nutrient retention and pH buffering capacity of the Bekenu series (Typic Paleudults). A 30 day soil leaching experiment was carried out using a completely randomized design with 16 treatments and 3 replications after which the leached soil samples were used for a pH buffering capacity study. The Calciprill and sodium silicate treatments significantly improved soil pH, exchangeable NH₄⁺, available P, exchangeable base cations, Effective Cation Exchange Capacity (ECEC), and pH buffering capacity in comparison with the untreated soil. The improvements were attributed to the alkalinity of Calciprill and sodium silicate due to their high inherent K⁺, Ca²⁺, Mg²⁺, and Na⁺ contents. The neutralizing effects of the amendments impeded the hydrolysis of Al³⁺ (96.5%), Fe²⁺ (70.4%), and Mn²⁺ (25.3%) ions resulting in fewer H⁺ ions being produced. The co-application of Calciprill and sodium silicate reduced the leaching of Ca²⁺ (58.7%) and NO₃⁻ (74.8%) from the amended soils. This was due to the ability of sodium silicate to reduce soil permeability and protect the Calciprill and available NO₃⁻ from being leached. This also improved the longevity of Calciprill to enhance the soil pH buffering capacity. However, the amounts of NH₄⁺, P, and base cations leached from the amended soils were higher compared with the un-amended soils. This was due to the high solubility of sodium silicate. The most suitable combination amendment was 7.01 g Calciprill and 9.26 g sodium silicate (C2S5) per kilogram soil. It is possible for farmers to adopt the combined use Calciprill and sodium silicate to regulate soil nutrient retention and improve the soil pH buffering capacity of highly weathered acidic soils. This will enhance soil and crop productivity. Full article

The use of stimulation preparations seems to be a promising means for mitigating the effects of abiotic and biotic stressors. Their significance includes plant organism stimulation and metabolism optimisation, water regime, and nutrition during periods of stress. They help bridge it over and create conditions for rapid regeneration. In a field experiment, the effect of the application of stimulation preparations on cultivars Triticum aestivum L. with different genetic composition was evaluated (donor of blue aleurone colour KM-72-18; donor of a multi-row spike (MRS) KM-94-18). Our results show a predominantly positive effect of the application of stimulants on the yield and thousand-grain weight (TKW). The results obtained were influenced by the year, based on different temperatures and precipitation. Higher yields were achieved in 2020 with higher total precipitation during the grain filling period and with a higher maximum quantum yield of the photosystem II (Fv/Fm). In 2019, this period was significantly dry and warm, which was reflected in a lower yield and TKM, higher proline content in the leaves, and lower Fv/Fm values. In both experimental years, there was a higher yield of the cultivar with blue aleurone (KM-72-18). In the case of cultivars with coloured grains, the promising use of the content substances in cultivars as natural means of increasing resistance to abiotic and biotic stressors seems to be promising. Full article

The objective of this study was to assess the yield efficiency of sulphur-enhanced fertilisers, depending on the dose and application method, in a short-lived (three-year) monoculture of winter oilseed rape under the climate and soil conditions of south-eastern Poland. The experiment was carried out between 2010 and 2013 on winter oilseed rape (Brassica napus L. var. napus) of the Orlando variety, fertilised with different sulphur doses—0, 20, 40 or 60 kg S ha⁻¹ applied in different method—soil application sowing, foliar application in the spring, and soil application sowing + foliar application in the spring (combined application). Following the harvest, seed and straw yields and the content of macroelements (N, S, P, K, Ca and Mg) in the seed and straw samples were determined. The harvest indices were also established for each of these elements. The impact of sulphur on winter oilseed rape yield depended significantly on both the dose and the application method. Even at the lowest dose (20 kg·ha⁻¹), sulphur materially increased seed yield, regardless of the application method. With autumn soil application and foliar application, differences between the lowest dose and the higher doses (40 and 60 kg·ha⁻¹) were not significant. However, with combined application, the highest dose (60 kg·ha⁻¹) significantly increased yield compared to the lower doses. In general, all the fertilisation approaches significantly increased the N, P, K, Ca and Mg contents compared to the control sample, but the differences between them were not substantial. Each of the sulphur application approaches decreased the harvest index for sulphur. The foliar application of each of the doses decreased the harvest indices for N, P, K and Ca. The soil application of 20 kg·ha⁻¹, and the mixed application of 40 and 60 kg·ha⁻¹, all increased the harvest indices for P, K and Ca. Full article

Given that an effective combined foliar application of iodine (I), selenium (Se), and zinc (Zn) would be farmer friendly, compared to a separate spray of each micronutrient, for the simultaneous biofortification of grain crops, we compared effectiveness of foliar-applied potassium iodate (KIO₃, 0.05%), sodium selenate (Na₂SeO₄, 0.0024%), and zinc sulfate (ZnSO₄∙7H₂O, 0.5%), separately and in their combination (as cocktail) for the micronutrient biofortification of four Basmati cultivars of rice (Oryza sativa L.). Foliar-applied, each micronutrient or their cocktail did not affect rice grain yield, but grain yield varied significantly among rice cultivars. Irrespective of foliar treatments, the brown rice of cv. Super Basmati and cv. Kisan Basmati had substantially higher concentration of micronutrients than cv. Basmati-515 and cv. Chenab Basmati. With foliar-applied KIO₃, alone or in cocktail, the I concentration in brown rice increased from 12 to 186 µg kg⁻¹. The average I concentration in brown rice with foliar-applied KIO₃ or cocktail was 126 μg kg⁻¹ in cv. Basmati-515, 160 μg kg⁻¹ in cv. Chenab Basmati, 153 μg kg⁻¹ in cv. Kisan Basmati, and 306 μg kg⁻¹ in cv. Super Basmati. Selenium concentration in brown rice increased from 54 to 760 µg kg⁻¹, with foliar-applied Na₂SeO₄ individually and in cocktail, respectively. The inherent Zn concentration in rice cultivars ranged between 14 and 19 mg kg⁻¹ and increased by 5–6 mg Zn per kg grains by foliar application of ZnSO₄∙7H₂O and cocktail. The results also showed the existence of genotypic variation in response to foliar spray of micronutrients and demonstrated that a foliar-applied cocktail of I, Se, and Zn could be an effective strategy for the simultaneous biofortification of rice grains with these micronutrients to address the hidden hunger problem in human populations. Full article

As one of the greatest agricultural challenges, yield prediction is an important issue for producers, stakeholders, and the global trade market. Most of the variation in yield is attributed to environmental factors such as climate conditions, soil type and cultivation practices. Artificial neural networks (ANNs) and random forest regression (RFR) are machine learning tools that are used unambiguously for crop yield prediction. There is limited research regarding the application of these mathematical models for the prediction of rapeseed yield and quality. A four-year study (2015–2018) was carried out in the Republic of Serbia with 40 winter rapeseed genotypes. The field trial was designed as a randomized complete block design in three replications. ANN, based on the Broyden–Fletcher–Goldfarb–Shanno iterative algorithm, and RFR models were used for prediction of seed yield, oil and protein yield, oil and protein content, and 1000 seed weight, based on the year of production and genotype. The best production year for rapeseed cultivation was 2016, when the highest seed and oil yield were achieved, 2994 kg/ha and 1402 kg/ha, respectively. The RFR model showed better prediction capabilities compared to the ANN model (the r² values for prediction of output variables were 0.944, 0.935, 0.912, 0.886, 0.936 and 0.900, for oil and protein content, seed yield, 1000 seed weight, oil and protein yield, respectively). Full article

Lolium spp. are troublesome weeds mainly found in winter cereal crops worldwide, including Europe. In recent years resistant mechanisms have been evolved to several important herbicides. In this study we investigated the mechanisms responsible for conferring glyphosate resistance in some Lolium spp. populations. A holistic approach was used, based on dose-response experiments, determination of shikimic acid concentration in plant leaf tissue, as well as molecular analyses. More specifically, in three Lolium spp. populations the existence of a mutation in the Pro-106 codon of the 5-enolpyruvylshikimate-3 phosphate synthase (EPSPS) gene was investigated as well as the relative transcript levels of four ABC-transporter genes were monitored at three time points after glyphosate application. The results demonstrated that glyphosate resistance is a multifactor phenomenon. Relative transcript levels of the ABC-transporter genes were abundant at very early time points after glyphosate treatments. Dose-response experiments and shikimate analyses were in accordance with the findings of the quantitative PCR (qPCR) analyses. We suggest that relative expression ratio of ABC-transporter genes can be a useful tool to rapidly identify Lolium spp. populations resistant to glyphosate. Full article

Numerous studies reported the positive effect of soil amendment with biochar on plant development. However, little is known about biochar and its interrelation with nitrogen (N) and phosphorous (P) additions and their impact on plant growth. We carried out greenhouse experiments to understand the interactive effects of nitrogen and phosphorus supply, as well as biochar amendment, on the symbiotic performance of soybean (Glycine max L.) with Bradyrhizobium japonicum, and plant growth and nutrient uptake. The biochar was produced from maize by heating at 600 °C for 30 min and used for pot experiments at an application rate of 2%. Plants were fertilized with two different concentrations of P (KH₂PO₄) and N (NH₄NO₃). Biochar application significantly increased the dry weight of soybean root and shoot biomass, by 34% and 42%, under low nitrogen and low phosphorus supply, respectively. Bradyrhizobium japonicum inoculation enhanced the dry weight of shoot biomass significantly, by 41% and 67%, in soil without biochar and with biochar addition, respectively. The nodule number was 19% higher in plants grown under low N combined with low or high P, than in high N combinations, while biochar application increased nodule number in roots. Moreover, biochar application increased N uptake of plants in all soil treatments with N or P supply, compared with B. japonicum-inoculated and uninoculated plants. A statistical difference in P uptake of plants between biochar and nutrient levels was observed with low N and high P supply in the soil. Our results show that the interactions between nitrogen, phosphorus, and biochar affect soybean growth by improving the symbiotic performance of B. japonicum and the growth and nutrition of soybean. We observed strong positive correlations between plant shoot biomass, root biomass, and N and P uptake. These data indicated that the combined use of biochar and low N, P application can be an effective approach in improving soybean growth with minimum nutrient input. Full article

Land use change could significantly affect soil organic carbon (SOC) and other soil chemical properties. However, the responses of soil labile C fractions at different soil depths to land-use change are not still clear. The aim of this study was to investigate the effect of paddy field conversion on woodlands or corn fields on total soil organic C (TOC) and its labile C fractions including particulate organic C (POC), microbial biomass C (MBC), and potassium permanganate-oxidizable C (KMnO₄–C) along a 0–100 cm soil profile. Our results indicate that soil TOC concentrations increased by 3.88 g kg⁻¹ and 3.47 g kg⁻¹ in the 0–5 cm soil layer and 5.33 g kg⁻¹ and 4.68 g kg⁻¹ in the 5–20 cm soil layer during 13 years after the conversion from paddy fields to woodlands and corn fields, respectively. In the 20–40 cm soil layer, the woodlands had the highest TOC concentration (12.3 g kg⁻¹), which was 5.13 g kg⁻¹ and 3.5 g kg⁻¹ higher than that of the paddy and corn fields, respectively. The increase in TOC was probably due to the absence of soil disturbance and greater root residue input into the woodland soil. In corn fields, pig manure addition contributed to the increase in soil organic C concentrations. In addition, the proportion of soil KMnO₄–C increased after conversion from paddy fields to woodlands or corn fields in the 0–40 cm soil layer, ranging from 39.9–56.6% for the woodlands and 24.6–32.9% for the corn fields. The soil POC content was significantly higher in woodland and corn field soils than in paddy field soils at lower soil depths (5–40 cm). However, there were no differences in MBC contents in the whole soil profile between the woodlands and paddy fields. The KMnO₄–C and MBC was the most important factor affecting the CMI values through the whole 0–100 cm soil profile. Overall, converting paddy fields to woodlands or corn fields increased the TOC and labile C fractions in the 0–40 cm soil layer. Future studies should focus on the response of the deeper soil C pool to land-use change. Full article

Erosion has been reported as one of the top degradation processes that negatively affect agricultural soils. The study objective was to identify hydropedological factors controlling soil water dynamics in erosion-affected hillslope vineyard soils. The hydropedological study was conducted at identically-managed Jastrebarsko (location I), and Jazbina (II) and (III) sites with Stagnosol soils. Soil Hydraulic Properties (SHP) were estimated on intact soil cores using Evaporation and WP4C methods; soil hydraulic functions were fitted using HYPROP-FIT software. For Apg and Bg/Btg horizons, uni- and bimodal soil hydraulic models could be well fitted to data; although, the bimodal model performed better in particular cases where data indicated non-uniform pore size distribution. With these SHP estimations, a one-year (2020) water flow scenario was simulated using HYDRUS-1D to compare water balance results obtained with uni- and bimodal hydraulic functions. Simulation results revealed relatively similar flux distribution at each hillslope position between the water balance components infiltration, surface runoff, and drainage. However, at the bottom profile at Jastrebarsko, bimodality of the hydraulic functions led to increased drainage. Soil water storage was reduced, and the vertical movement increased due to modified soil water retention curve shapes. Adequate parameterization of SHP is required to capture the hydropedological response of heterogenous erosion-affected soil systems. Full article

The widespread use of nitrogen chemical fertilizers in modern agricultural practices has raised concerns over hazardous accumulations of nitrogen-based compounds in crop foods and in agricultural soils due to nitrogen overfertilization. Many vegetables accumulate and retain large amounts of nitrites and nitrates due to repeated nitrogen applications or excess use of nitrogen fertilizers. Consequently, the consumption of high-nitrate crop foods may cause health risks to humans. The effects of varying urea–nitrogen fertilizer application rates on VOC emissions from cucumber fruits were investigated using an experimental MOS electronic-nose (e-nose) device based on differences in sensor-array responses to volatile emissions from fruits, recorded following different urea fertilizer treatments. Urea fertilizer was applied to cucumber plants at treatment rates equivalent to 0, 100, 200, 300, and 400 kg/ha. Cucumber fruits were then harvested twice, 4 and 5 months after seed planting, and evaluated for VOC emissions using an e-nose technology to assess differences in smellprint signatures associated with different urea application rates. The electrical signals from the e-nose sensor array data outputs were subjected to four aroma classification methods, including: linear and quadratic discriminant analysis (LDA-QDA), support vector machines (SVM), and artificial neural networks (ANN). The results suggest that combining the MOS e-nose technology with QDA is a promising method for rapidly monitoring urea fertilizer application rates applied to cucumber plants based on changes in VOC emissions from cucumber fruits. This new monitoring tool could be useful in adjusting future urea fertilizer application rates to help prevent nitrogen overfertilization. Full article

Since the registry of common oregano (Origanum vulgare L.) cultivars does not involve regionalization, a comprehensive study of cultivars bred by different institutions in the intended cultivation region is valuable and relevant. The objective of the research was to assess the possibility of using various indices of ecological adaptability originally developed for grain crops for their use in the most adapted genotypes’ selection (breeding samples and cultivars) of Origanum vulgare L. to the temperate climate of the Crimean Peninsula. The research was carried out in the piedmont zone of Crimea from 2016 to 2019. The study material consisted of breeding samples No. 10 and No. 82 from the collection of FSBSI “Research Institute of Agriculture of Crimea”, as well as cultivars Zima, Raduga, and Slavnitsa selected by the All-Russian Scientific Research Institute of Medicinal and Aromatic Plants (ARSRIMAP). Genotype had the greatest influence on yield of fresh oregano material (43%) with the influence of the weather conditions of the year being 2%. On the contrary, meteorological conditions had a much greater effect on the essential oil accumulation and its areal yield, which were 30% and 25%, respectively. In terms of the coefficient of ecological variation of fresh yield, sample No. 82 and Slavnitsa cultivar were the best (11.47–16.7%). The local genotypes No. 10 and No. 82 varied less by the essential oil content and its yield. The genotype effect value was greater than 0 in the Raduga cultivar and local genotype No. 82 for the yield, but only in No. 82 genotype for the other two characteristics. Cultivars Zima and Raduga were classified as intensive (b_i > 1) by the environmental flexibility of fresh yield, while local genotype No. 82 and Slavnitsa cultivar formed the group of intensive ones by essential oil content and essential oil yield. Local genotypes No. 10 and No. 82 were better than the introduced cultivars in terms of essential oil content homeostability and essential oil yield (Hom = 1.91–2.18). Thus, local genotypes proved to be more adapted to the region’s conditions in terms of essential oil accumulation. However, they were inferior to the registered cultivars of ARSRIMAP breeding in terms of fresh yield. Full article

Malting barley requires sensitive methods for N status estimation during the vegetation period, as inadequate N nutrition can significantly limit yield formation, while overfertilization often leads to an increase in grain protein content above the limit for malting barley and also to excessive lodging. We hypothesized that the use of N nutrition index and N uptake combined with red-edge or green reflectance would provide extended linearity and higher accuracy in estimating N status across different years, genotypes, and densities, and the accuracy of N status estimation will be further improved by using artificial neural network based on multiple spectral reflectance wavelengths. Multifactorial field experiments on interactive effects of N nutrition, sowing density, and genotype were conducted in 2011–2013 to develop methods for estimation of N status and to reduce dependency on changing environmental conditions, genotype, or barley management. N nutrition index (NNI) and total N uptake were used to correct the effect of biomass accumulation and N dilution during plant development. We employed an artificial neural network to integrate data from multiple reflectance wavelengths and thereby eliminate the effects of such interfering factors as genotype, sowing density, and year. NNI and N uptake significantly reduced the interannual variation in relationships to vegetation indices documented for N content. The vegetation indices showing the best performance across years were mainly based on red-edge and carotenoid absorption bands. The use of an artificial neural network also significantly improved the estimation of all N status indicators, including N content. The critical reflectance wavelengths for neural network training were in spectral bands 400–490, 530–570, and 710–720 nm. In summary, combining NNI or N uptake and neural network increased the accuracy of N status estimation to up 94%, compared to less than 60% for N concentration. Full article

The rise in the productivity of sweet orange in Brazil has been related to the use of superior rootstocks and higher tree density, among other factors. In order to investigate whether the cropping system and the land use efficiency would benefit from more intensive cultivation, the performance of Valencia sweet orange was evaluated over nine years on four rootstocks, which induced contrasting vigor, at 513, 696 and 1000 trees·ha⁻¹. Agronomic Institute of Campinas (IAC) 1697 and IAC 1710 citrandarins, and diploid and allotetraploid (4×) Swingle citrumelos were classified as semi-dwarfing, super-standard, standard, and dwarfing rootstocks, respectively. The fruit yield per tree was decreased at higher tree densities, notably for more vigorous rootstocks. Conversely, the cumulative productivity was increased over the evaluation period by 27% at 1000 trees·ha⁻¹, irrespective of the rootstock, and the most vigorous rootstock resulted in 2.5 times higher production than the dwarfing one on average. Most fruit quality parameters were seldom influenced by the tree density, while the rootstock was a decisive factor in improving the quality and the soluble solids content. Dwarfing rootstocks allowed for harvesting 17% more fruit per minute by manual pickers. Because the tree row volume per area is lower with such rootstocks, even at higher tree density, spray volume can be reduced, although appropriate equipment should be developed for better spray coverage on smaller trees. Nine years after planting under strict vector control, the cumulative incidence of huanglongbing-symptomatic trees on IAC 1710 was double that on Swingle 4×. Taken together, the results suggested that the land use efficiency in the citrus industry can be further improved by planting vigorous rootstocks at moderate to high tree densities. Nevertheless, obtaining highly productive semi-dwarfing and dwarfing rootstocks is the sine qua non for making high-density pedestrian sweet orange orchards more profitable. Full article

Plants grow better when they are supplied with a combination of ammonium (NH₄⁺) and nitrate (NO₃⁻) than when either one is supplied as the sole N (nitrogen) source. However, the effects of N forms on N metabolism and major N assimilation enzymes in different plants, especially vegetables, are largely neglected. This study was conducted on two plants with distinct NH₄⁺ tolerances to compare the responses of two popular leafy vegetables, Korean cabbage (Brassica campestris L.) ‘Ssamchu’ and lettuce (Lactuca sativa L.) ‘Caesar green’, to the N source. To this end, plant growth and quality, photosynthesis, carbohydrate, N contents (in the forms of NO₃⁻, NO₂⁻, NH₄⁺, total protein), and key N assimilation-related enzyme (NR, NIR, GS, GDH) activities were investigated. When plants were subjected to one of three NH₄⁺:NO₃⁻ regimes, 0:100, 50:50, or 100:0, lettuce was relatively more tolerant while cabbage was extremely sensitive to high NH₄⁺. Both plants benefited more from being grown with 50:50 NH₄⁺:NO₃⁻, as evidenced by the best growth performance, ameliorated photosynthesis, and enriched carbohydrate (C) stock content. In addition, as compared to cabbage, the GS and GDH activities were reinforced in lettuce in response to an increasing external NH₄⁺ level, resulting in low NH₄⁺ accumulation. Our findings suggested that boosting or maintaining high GS and GDH activities is an important strategy for the ammonium tolerance in vegetables. Full article

Maize is one of the most important staple food crops in Mozambique. Its production is country-wise dominated by smallholder farmers (more than 90%) under rain-fed conditions, where the risk of crop failure is high, especially under semi-arid conditions in southern Mozambique. Several maize genotypes have been developed for the broad agro-ecological zone adaptation but lack strong evidence about their productivity and yield stability to support decision-making in farming systems. In order to assess the yield and yield stability of maize genotypes under different environments, five identical on-station trials were implemented in the period 2017 to 2019, covering summer and winter seasons in the semi-arid region of southern Mozambique. The trials were established at the experimental station of the Universidade Eduardo Mondlane (UEM) in Sábie and at the Instituto de Investigação Agrária de Moçambique (IIAM) in Chókwe. A strip-plot design in a randomized complete block arrangement with 15 maize genotypes, and three water application (rainfall plus irrigation) levels in four replications was followed in a line-source irrigation arrangement. The water application levels varied from 151 mm to 804 mm, covering different water regimes. Under well-watered summer conditions, the genotypes G6 and G12 showed high yield and high grain yield stability. In the drier conditions, either in summer or winter, the G2 and G11 genotypes produced higher grain yield but with low stability. Both groups of genotypes have a high potential to be included in technology transfer packages to smallholder farmers to address food security or large-scale commercial farmers differently. Full article

The 17 sustainable development goals proposed in the 2030 sustainable development agenda are the shared vision of all humanity. The core of achieving the sustainable development goals is to ensure grain security. Although financial inclusion is not separately incorporated into the United Nations sustainable development goals, it is an essential basis for supporting all sustainable development goals. Financial inclusion plays a critical role in improving grain security efficiency to ensure sustainable grain security. According to the Financial Access Survey implemented by IMF, this study calculated the financial inclusion index and grain security efficiency of 121 countries from 2015 to 2019. Based on calculating the efficiency of grain security in production and distribution, this study used an econometric model to empirically examine the role of financial inclusion in improving grain security efficiency. The study found that financial inclusion can promote grain security efficiency from the two links of production and distribution. Still, the improvement of grain security efficiency by financial inclusion is mainly reflected in the distribution. Further, the study found that the advancement of financial inclusion promotes the efficiency of grain distribution through the effects of residents’ income distribution, residents’ income growth, and consumption capacity upgrading, which achieves the goal of ensuring grain security and promoting sustainable development. Full article

Grain protein content (GPC) is controlled by complex genetic systems and their interactions and is an important quality determinant for hard spring wheat as it has a positive effect on bread and pasta quality. GPC is variable among genotypes and strongly influenced by the environment. Thus, understanding the genetic control of wheat GPC and identifying genotypes with improved stability is an important breeding goal. The objectives of this research were to identify genetic backgrounds with less variation for GPC across environments and identify quantitative trait loci (QTLs) controlling the stability of GPC. A spring wheat nested association mapping (NAM) population of 650 recombinant inbred lines (RIL) derived from 26 diverse founder parents crossed to one common parent, ‘Berkut’, was phenotyped over three years of field trials (2014–2016). Genomic selection models were developed and compared based on predictions of GPC and GPC stability. After observing variable genetic control of GPC within the NAM population, seven RIL families displaying reduced marker-by-environment interaction were selected based on a stability index derived from a Finlay–Wilkinson regression. A genome-wide association study identified eighteen significant QTLs for GPC stability with a Bonferroni-adjusted p-value < 0.05 using four different models and out of these eighteen QTLs eight were identified by two or more GWAS models simultaneously. This study also demonstrated that genome-wide prediction of GPC with ridge regression best linear unbiased estimates reached up to r = 0.69. Genomic selection can be used to apply selection pressure for GPC and improve genetic gain for GPC. Full article

To meet the demands of different wheat-based food products, traits related to end-use quality become indispensable components in wheat improvement. Thus, markers associated with these traits are valuable for the timely evaluation of protein content, kernel physical characteristics, and rheological properties. Hereunder, we report the mapping results of quantitative trait loci (QTLs) linked to end-use quality traits. We used a dense genetic map with 5199 SNPs from a 90K array based on a recombinant inbred line (RIL) population derived from ‘CO960293-2’/‘TAM 111’. The population was evaluated for flour protein concentration, kernel characteristics, dough rheological properties, and grain mineral concentrations. An inclusive composite interval mapping model for individual and across-environment QTL analyses revealed 22 consistent QTLs identified in two or more environments. Chromosomes 1A, 1B, and 1D had clustered QTLs associated with rheological parameters. Glu-D1 loci from CO960293-2 and either low-molecular-weight glutenin subunits or gliadin loci on 1A, 1B, and 1D influenced dough mixing properties substantially, with up to 34.2% of the total phenotypic variation explained (PVE). A total of five QTLs associated with grain Cd, Co, and Mo concentrations were identified on 3B, 5A, and 7B, explaining up to 11.6% of PVE. The results provide important genetic resources towards understanding the genetic bases of end-use quality traits. Information about the novel and consistent QTLs provided solid foundations for further characterization and marker designing to assist selections for end-use quality improvements. Full article

Phenylalanine ammonia-lyase (PAL) is the first enzyme in the phenylpropanoid pathway and plays a vital role in adoption, growth, and development in plants but in wheat its characterization is still not very clear. Here, we report a genome-wide identification of TaPAL genes and analysis of their transcriptional expression, duplication, and phylogeny in wheat. A total of 37 TaPAL genes that cluster into three subfamilies have been identified based on phylogenetic analysis. These TaPAL genes are distributed on 1A, 1B, 1D, 2A, 2B, 2D, 4A, 5B, 6A, 6B, and 6D chromosomes. Gene structure, conserved domain analysis, and investigation of cis-regulatory elements were systematically carried out. Chromosomal rearrangements and gene loss were observed by evolutionary analysis of the orthologs among Triticum urartu, Aegilops tauschii, and Triticum aestivum during the origin of bread wheat. Gene ontology analysis revealed that PAL genes play a role in plant growth. We also identified 27 putative miRNAs targeting 37 TaPAL genes. The high expression level of PAL genes was detected in roots of drought-tolerant genotypes compared to drought-sensitive genotypes. However, very low expressions of TaPAL10, TaPAL30, TaPAL32, TaPAL3, and TaPAL28 were recorded in all wheat genotypes. Arogenate dehydratase interacts with TaPAL29 and has higher expression in roots. The analysis of all identified genes in RNA-seq data showed that they are expressed in roots and shoots under normal and abiotic stress. Our study offers valuable data on the functioning of PAL genes in wheat. Full article

Data that are required for nutrient management are becoming increasingly available in digital format, leading to a high innovation potential for digital nitrogen (N) management applications. However, it is currently difficult for farmers to analyze, assess, and optimize N flows in their farms using the existing software. To improve digital N management, this study identified, evaluated, and systematized the requirements of stakeholders. Furthermore, digital farm N management tools with varying objectives in terms of system boundaries, data requirements, used methods and algorithms, performance, and practicality were appraised and categorized. According to the identified needs, the concept of a farm N management system (FNMS) software is presented which includes the following modules: (1) management of site and farm data, (2) determination of fertilizer requirements, (3) N balancing and cycles, (4) N turnover and losses, and (5) decision support. The aim of FNMS is to support farmers in their farming practices for increasing N efficiency and reducing environmentally harmful N surpluses. In this study, the conceptual requirements from the agricultural and computer science perspectives were determined as a basis for developing a consistent, scientifically sound, and user-friendly FNMS, especially applicable in European countries. This FNMS enables farmers and their advisors to make knowledge-based decisions based on comprehensive and integrated data. Full article