Detecting banana pseudostems is an indispensable part of the intelligent management of banana cultivation, which can be used in settings such as counting banana pseudostems and smart fertilization. In complex environments, dense and occlusion banana pseudostems pose a significant challenge for detection. This paper proposes an improved YOLOV7 deep learning object detection algorithm, YOLOV7-FM, for detecting banana pseudostems with different growth conditions. In the loss optimization part of the YOLOV7 model, Focal loss is introduced, to optimize the problematic training for banana pseudostems that are dense and sheltered, so as to improve the recognition rate of challenging samples. In the data augmentation part of the YOLOV7 model, the Mixup data augmentation is used, to improve the model’s generalization ability for banana pseudostems with similar features to complex environments. This paper compares the AP (average precision) and inference speed of the YOLOV7-FM algorithm with YOLOX, YOLOV5, YOLOV3, and Faster R-CNN algorithms. The results show that the AP and inference speed of the YOLOV7-FM algorithm is higher than those models that are compared, with an average inference time of 8.0 ms per image containing banana pseudostems and AP of 81.45%. This improved YOLOV7-FM model can achieve fast and accurate detection of banana pseudostems. Full article

The identification of genotypes more tolerant to water deficit is a challenge to breeding programs. In this research, our objectives were to identify and validate traits for tolerance to water deficit in eucalypts. The estimation of genotypic parameters and early selection are proposed based on mixed models, selection indexes and validation schemes. Seedlings with 110 days were grown in a greenhouse for 12 weeks, and two water deficit treatments were conducted (polyethylene glycol and water limitation). A total of 26 biomarkers were evaluated, and 15 of them were significant, exhibited adequate heritability, and used for screening: final plant height, increment in height, increment in diameter, area of mature and fully expanded leaf, nutrient contents of N, K, Ca, Mg, S, Cu, Zn, Mn and B, photosynthesis (A) and stomatal conductance (gs). Both treatments were adequate to discriminate water deficit-tolerant clones. The ranking of tolerant clones according to their phenotype in the field demonstrates the potential for early selection and is consistent with the maintenance of water-deficit-tolerance mechanisms until adulthood. There is evidence that the choice of biomarker depends on the species involved and different strategies contributing to the tolerance trait. Full article

Soybeans often encounter several in-season stressors that can alter retention of reproductive structures. To understand soybean response to structural losses through altered growth parameters—and, ultimately, yield—a field trial was established in Bixby, Oklahoma, in 2019 and 2020 and Perkins, Oklahoma, in 2019. Removal of reproductive structures occurred at full flower (R2), the beginning of pod development (R3), and the beginning of seed development (R5) and at three locations on the plant (top third (T), middle third (M), whole (W)). The impact of flower removal on yield at the R2 and R3 stages did not significantly differ from that in non-treated soybean. Pod removal as late as R5 from the upper fruiting positions (T) had a lesser impact on overall yield, with R5:T showing a reduction in seed number of 860 seeds plant⁻¹, whereas R5:M was 1921 seeds plant⁻¹ below the non-treated soybean. The middle portion of the mainstem was the location where the loss demonstrated was paramount at R5, as this region is a large sink and major contributor to yield. Late-season, stress-negating yield recovery, depending on the severity, may indicate that management practices should anticipate physiological limitations for stress, as well as the potential for relative yield recovery and yield improvement. Full article

On-farm composting of agro-livestock wastes can be considered the most appropriate method for their recycling. Pile turning (PW) is one of the most widely used aeration systems for composting. However, this system has long composting periods and is inefficient at supplying oxygen and controlling the temperature. To minimize these drawbacks, the combination of turnings with forced aeration (PR) is an option; in this work, this combination was compared to PW as an aeration system for the co-composting of vegetable waste with different manures. In this comparative study, the evolution of the process, the compost quality and the economic and environmental impacts of the process were evaluated. The PR system was more appropriate for obtaining sanitized composts (the temperature was ≥55 °C for at least three consecutive days) with an adequate degree of maturity. Furthermore, this system reduced the organic matter and nutrient losses, yielding composts with higher agronomic value and a higher total combined value of the nutrients than those obtained using the PW system. However, the energy consumption and associated CO₂ emissions were lower for the PW system, since this aeration system was based only on turnings without the use of forced aeration, as in the case of the PR system. Agricultural valorization of composts will offset this energy consumption and its impact, since it will contribute to reducing the use of synthetic fertilizers. However, more studies are required on the PR composting system and other agro-livestock wastes for the creation of centralized on-farm composting sites, where all steps of the composting chain are optimized. Full article

When biochars are produced, feedstock is a crucial factor that determines their physicochemical properties. However, the characteristics of tropical crop waste-derived biochar have not been described and limit its availability. In this study, pineapple leaf (PAL), banana stem (BAS), sugarcane bagasse (SCB) and horticultural substrate (HCS), were used to prepare biochar at 300, 500 and 700 °C. Properties of biochars and their applications were analysed. The results indicated that hydrophobicity, nonpolarity and aromaticity of SCB biochar (SCBB) were higher than other biochars due to the loss of H (hydrogen), O (oxygen), and N (nitrogen). The pH of PAL biochar (PALB) and BAS biochar (BASB) ranged from 9.69 to 10.30 higher than that of SCBB and HCS biochar (HCSB) with 7.17–9.77. In PALB and BASB, sylvite was the dominant crystal structure. With temperature rising, C–H stretching, C=C stretching and H–O in alcohol groups decreased, and Si–O stretching in HCSB and SCBB strengthened. Biochars obtained at 500 °C, especially SCBB and HCSB, significantly promoted the growth of maize. The PALB and BASB greatly increased the soil pH/EC to 6.90–7.35 and 0.67–0.95 ms/cm, while those of SCBB and HCSB were 5.97–6.74 and 0.23–0.45 ms/cm. The application of the biochars to the soil increased soil pH, reducing the acidic soil stress in maize growth, especially PAL and BAS biochars prepared at 300 °C. Biochar prepared at lower temperature will greatly reduce energy consumption and increase the utilization efficiency of tropical agricultural waste resources. Full article

Citrus psyllid is the main vector of Huanglongbing, and as such, it is responsible for huge economic losses across the citrus industry. The small size of this pest, difficulties in data acquisition, and the lack of target detection algorithms suitable for complex occlusion environments inhibit detection of the pest. The present paper describes the construction of a standard sample database of citrus psyllid in multi-focal lengths and out-of-focus states in the natural environment. By integrating the attention mechanism and optimizing the key module of BottleneckCSP, YOLOv5s-BC, we have created an accurate detection algorithm for small targets. Based on YOLOv5s, our algorithm incorporates an SE-Net channel attention module into the Backbone network and improves the detection of small targets by guiding the algorithm to the channel characteristics of small-target information. At the same time, the BottleneckCSP module in the neck network is improved, and extraction of multiple features of recognition targets is improved by the addition of a normalization layer and SiLU activation function. Experimental results based on a standard sample database show the recognition accuracy (intersection over union (IoU) = 0.5) of the YOLOv5s-BC algorithm for citrus psyllid to be 93.43%, 2.41% higher than that of traditional YOLOv5s. The accuracy and recall rates are also increased by 1.31% and 4.22%, respectively. These results confirm that the YOLOv5s-BC algorithm has good generalization ability in the natural context of citrus orchards, and it offers a new approach for the control of citrus psyllid. Full article

Tomato is a great source of bioactive compounds, is important for human health, and is cultivated worldwide. However, the high inputs required for its cultivation must be sustainably managed in order to limit yield losses, thus obtaining high-quality and environmentally friendly production. In this perspective, we compared four biostimulant treatments, i.e., Ascophyllum nodosum extract—Bio; microbial biostimulant containing the micro-organism Trichoderma afroharzianum—Mic; a combination of both—M-B; not treated—Control) and three mulch treatments (biodegradable film Ecovio—ECO; biodegradable film MaterBi^®—NOV; bare soil—BS) and evaluated their effects on yield and quality traits in processing tomato. Both biodegradable films elicited a 27.0% yield increase compared to plants grown on bare soil, and biostimulants determined a 23.7% increase over the Control, with the best performance recorded for M-B (+24.8%). Biodegradable MaterBi^® film (NOV) was associated with higher total soluble solids (TSS) and firmness values (average of 4.9 °Brix and 1.30 kg cm⁻², respectively), even if a significant effect of biostimulants was observed only for the second element. Carotenoid content was higher in non-treated plants grown on bare soil as well as hydrophilic antioxidant activity (AA), but in this case, no differences between biostimulant treatments were recorded. The lipophilic AA in NOV-treated plants was about six and four times higher than observed in BS and ECO treatments, respectively; NOV also caused a 38.7% increase in ascorbic acid content over the Control but was not different from ECO. All biostimulant treatments elicited a 30% increase in phenol content compared to Control plants. Our findings highlight that microbial biostimulants based on A. nodosum extract and T. afroharzianum (both applied singularly and combined) can be considered a sustainable tool for increasing yield and improve some quality traits of processing tomato; in addition, we also confirmed the capability of biodegradable mulches, in particular, MaterBi^®, to enhance the agronomic performance of tomato. Full article

Currently, the wine industry has gained great relevance worldwide. At the same time, the sustainability of the business activity has positioned itself as one of the main challenges to be achieved in the coming years. Due to the great impact that agricultural processes can have on the environment, vine and wine production is particularly susceptible to the application of new technologies and processes that improve its sustainability in the medium and long term, while allowing the improvement of product quality. The main objective of this paper is to analyze the academic literature in the field of wine industry sustainability, to determine the main contributions carried out, as well as the most prominent authors, universities, and countries in this field of study. The methodology used is bibliometric analysis, specifically the Bibliometrix^® R package, in its version 4.1.1. The results reveal that research in the field of sustainability in the wine industry has increased considerably in the last three years by several leading researchers, mainly from universities located in wine-producing regions. It is concluded that research shows a trend towards collaboration among stakeholders, especially in terms of innovation, which is postulated as the main tool to improve the sustainability of the sector in the coming years. Full article

Stable hydrogen and oxygen isotopes provide a powerful technique for quantifying the proportion of root water uptake (RWU) from different potential water sources. Although many models coupled with stable isotopes have been developed to estimate plant water source apportionment, inter-comparisons of different methods are still limited, especially their performance under different soil water content (SWC) conditions. In this study, three Bayesian tracer mixing models, which included MixSIAR, MixSIR and SIAR, were tested to evaluate their performances in determining the RWU of winter wheat under various SWC conditions (normal, dry and wet) in the North China Plain (NCP). The proportions of RWU in different soil layers showed significant differences (p < 0.05) among the three Bayesian models, for example, the proportion of 0–20 cm soil layer calculated by MixSIR, MixSIAR and SIAR was 69.7%, 50.1% and 48.3% for the third sampling under the dry condition (p < 0.05), respectively. Furthermore, the average proportion of the 0–20 cm layer under the dry condition was lower than that under normal and wet conditions, being 45.7%, 58.3% and 59.5%, respectively. No significant difference (p > 0.05) was found in the main RWU depth (i.e., 0–20 cm) among the three models, except for individual sampling periods. The performance of three models in determining plant water source allocation varied with SWC conditions: the performance indicators such as coefficient of determination (R²) and Nash-Sutcliffe efficiency coefficient (NS) in MixSIAR were higher than that in MixSIR and SIAR, showing that MixSIAR performed well under normal and wet conditions. The rank of performance under the dry condition was MixSIR, MixSIAR, and then SIAR. Overall, MixSIAR performed relatively better than other models in predicting RWU under the three different soil moisture conditions. Full article

Winter jujube is a popular fresh fruit in China for its high vitamin C nutritional value and delicious taste. In terms of winter jujube object detection, in machine learning research, small size jujube fruits could not be detected with a high accuracy. Moreover, in deep learning research, due to the large model size of the network and slow detection speed, deployment in embedded devices is limited. In this study, an improved Yolov5s (You Only Look Once version 5 small model) algorithm was proposed in order to achieve quick and precise detection. In the improved Yolov5s algorithm, we decreased the model size and network parameters by reducing the backbone network size of Yolov5s to improve the detection speed. Yolov5s’s neck was replaced with slim-neck, which uses Ghost-Shuffle Convolution (GSConv) and one-time aggregation cross stage partial network module (VoV-GSCSP) to lessen computational and network complexity while maintaining adequate accuracy. Finally, knowledge distillation was used to optimize the improved Yolov5s model to increase generalization and boost overall performance. Experimental results showed that the accuracy of the optimized Yolov5s model outperformed Yolov5s in terms of occlusion and small target fruit discrimination, as well as overall performance. Compared to Yolov5s, the Precision, Recall, mAP (mean average Precision), and F₁ values of the optimized Yolov5s model were increased by 4.70%, 1.30%, 1.90%, and 2.90%, respectively. The Model size and Parameters were both reduced significantly by 86.09% and 88.77%, respectively. The experiment results prove that the model that was optimized from Yolov5s can provide a real time and high accuracy small winter jujube fruit detection method for robot harvesting. Full article

Due to the significant quantities of waste generated by the Sri Lankan rice industry, circular bioeconomy methodologies were applied to examine value-adding entrepreneurial activities for rice industry by-products (RIB). The study was conceived after scouring the existing literature on agricultural waste management and interviewing experts in the field and the rice industry. In the first phase, the suitability of valorizing alternatives for RIB was considered via a multi-criteria decision-making method. Valorization options, such as biochar production, energy purposes, composting, and other activities, were evaluated using an analytical hierarchy process (AHP) based on four criteria, namely environmental, social, technical, and economic issues. The results indicated that the highest priority should be given to environmental, social, and economic considerations, with local priority vectors of 0.5887, 0.2552, and 0.0955, respectively. It was found that biochar production is the optimal valorization strategy for managing RIB in Sri Lanka. From these findings, the development of a sustainable business model for making biochar out of RIB was done based on commercial motivations and value addition in biochar manufacturing processes. The Business Model Canvas elements played a vital role in categorizing and interpreting the case study data. Though the RIB seems undervalued at present, it was found that as a direct result of environmental concerns, several stakeholders have developed RIB valorization with an emphasis on bioenergy generation and biochar production. Adequate subsidies (technology and knowledge), standard regulations, more collective actions for creating economies of scale, and marketing strategies (consumer awareness) are all necessary for the successful implementation of sustainable circular business models. Full article

Fusarium head blight (FHB) is a fungal disease of cereals including wheat, which results in significant economic losses and reductions in grain quality. Additionally, the presence of Fusarium spp. results in productions of mycotoxins/metabolites, some of which are toxic in low concentrations. The liquid chromatography with tandem mass spectrometry (LC-MS/MS) method was applied to 216 wheat samples from field conditions diseased with FHB. Data obtained show that out of 28 metabolites detected, deoxynivalenol (DON), deoxynivalenol-3-glucoside (D3G), enniatin B (ENN B), enniatin B1 (ENN B1), culmorin, 15-hydroxyculmorin, and aurofusarin were the most prevalent mycotoxins/metabolites over three years (2014–2016). In 2014–2016, 100, 100 and 96% of the samples were contaminated with zearalenone (ZEN). Of the masked mycotoxins, D3G occurred at a high incidence level of 100% in all three investigated years. Among emerging mycotoxins, moniliformin (MON), beauvericin (BEA) and enniatins (ENNs) showed high occurrences ranging from 27 and 100% during three investigated years. Co-occurrence of Fusarium mycotoxins/metabolites was high and almost all were highly correlated to each other but their possible synergistic, additive, or antagonistic effects of toxicity, should be taken into consideration. Our results demonstrated that modified and emerging mycotoxins/metabolites contributed substantially to the overall contamination of wheat grains. To avoid disparagement, it is necessary to analyse these forms in future mycotoxin monitoring programs and to set their maximum levels. Full article

Both nitrogen and sulphur are important macronutrients necessary for the proper development and yield of soybean. Moreover, sulphur plays a special role in nitrogen metabolism in the plant, and sulphur deficiency leads to a reduction in the utilization of nitrogen from fertilizer. The objective of this study was to assess the effect of nitrogen and sulphur application on the yield and quality traits of soybean seeds. The following factors were analyzed in the experiment: I. Nitrogen application rate: 0, 30 and 60 kg ha⁻¹ applied at different times (before sowing and/or at the start of the seed filling stage); II. Sulphur application rate: 0 and 40 kg ha⁻¹ applied in two portions: half during the development of lateral shoots and half at the start of flowering. Thus the 14 fertilizer combinations were obtained. Result show that the highest seeds yield was obtained in the combinations with 60 kg N applied ½ before sowing + ½ after emergence (BBCH 73-75) and ¾ before sowing +¼ after emergence. In these combinations, sulphur did not significantly affect seed yield. In the remaining nitrogen application, sulphur application significantly increased the seed yield. Taking into account the yield and the chemical composition of the soybean seeds, fertilization with 60 kg N ha⁻¹ in two portions can be recommended—½ or ¾ before sowing and the remainder during the development of pods and seeds—in combination with sulphur application. Full article

Salinity is one of the most devastating environmental factors limiting crop productivity worldwide. Therefore, our study investigates the effect of seed priming with zinc oxide nanoparticles (ZnO NPs: 0, 50, and 100 mg L⁻¹), 24-epibrassinolide (EBL: 0.0, 0.2, and 0.4 µM), and their combined treatments on maize (Zea mays L.) grown with different levels of saline stress (i.e., control, 5, 10 dS m⁻¹) under semi-controlled conditions. Higher saline stress (10 dS m⁻¹) negatively influenced the growth traits, physiological attributes, and elemental (i.e., Zn and K) uptake for both roots and shoots of maize, whereas it increased Na⁺ accumulation and Na⁺/K⁺ ratio in comparison to other treatments. However, seed priming with ZnO NPs and EBL as well as their combinations showed amelioration of the detrimental effects of saline stress on the growth and physiological and biochemical performance of maize. In general, seed priming with combined treatments of ZnO NPs and EBL were significantly more effective than either ZnO NPs or EBL as individual treatments. A combination of 100 mg L⁻¹ ZnO NPS + 0.2 µM EBL resulted in the highest values of root length, root surface area, stem diameter, relative leaf water contents, total chlorophyll, net rate of photosynthesis, zinc accumulation, and K⁺ uptake, while it resulted in the lowest Na⁺ and Na⁺/K⁺ ratio, especially under the highest saline-stress treatment. Thus, we concluded that seed priming with combined ZnO NPs and EBL can effectively mitigate the saline-stress-mediated decline in the morphological, physiological, and biochemical traits of maize. Full article

The low digestibility of fiber in alfalfa (Medicago sativa L.) limits dry matter intake and energy availability in ruminant animal production systems. Previously, alfalfa plants were identified for low or high rapid (16 h) and low or high potential (96 h) in vitro neutral detergent fiber digestibility (IVNDFD) of plant stems. Here, two cycles of bidirectional selection for 16 h and 96 h IVNDFD were carried out. The resulting populations were evaluated for total herbage, percentage of stems to total biomass, IVNDFD, neutral detergent fiber (NDF), and acid detergent lignin as a proportion of NDF (ADL/NDF) at three maturity stages. Within these populations, 96 h IVNDFD was highly heritable (h² = 0.71), while 16 h IVNDFD had lower heritability (h² = 0.46). Selection for high IVNDFD reduced NDF and ADL/NDF in plant stems at the late flowering and green pod maturity stages and reduced seasonal variability in stem digestibility but did not alter the percentage of stems. Stability analyses across 12 harvest environments found that selection for high IVNDFD had little effect on environmental stability of the trait compared to the unselected population. Thus, selection for stem IVNDFD was a highly effective strategy for developing alfalfa populations with improved nutritional quality without changing the percentage of stems to total biomass. Full article

Ornamental containerized transplant production needs high doses of controlled release fertilizers (CFR), but it is known that there is an environmental risk caused by inadequate fertilization management. To the best of our knowledge, amino acid-(AaB) and seaweed extract-(SeB) based biostimulant application, in ornamental transplant production, is still poorly studied. Therefore, the aim of this work was to assess the hypothesis that, under reduced nutrient supply, SeB and AaB applications, via foliar spray, can promote quality and sustainability in the production of high-quality ornamental seedlings with a 90-day growing cycle. The CRF incorporated into the peat-growing medium was Osmocote Exact Mini in formulation N:P:K = 15 + 9 + 11 (3 months). Six treatments were compared in two economically important potted (0.3 L in volume) ornamentals: Abelia × grandiflora and Lantana camara: T1 = conventional full CRF dose: 4 gL⁻¹ per pot; T2 = limited CRF dose: 50% of T1; T3 = T2 + MC-Extra^® [SeB 0.5 gL⁻¹]; T4 = T2 + MC-Extra^® [SeB 1.0 gL⁻¹]; T5 = T2 + Megafol^® [AaB 1.5 mL L⁻¹]; T6 = T2 + Megafol^® [AaB 2.5 mL L⁻¹]. The research results showed that the application of 50% CRF plus biostimulant application resulted in plant performance greater than or equal to those raised under the conventional CRF full dose. In particular, S1 (Abelia × grandiflora ‘Edward Goucher’) and S2 (Lantana camara ‘Little Lucky’) behaved differently concerning the Megafol^® dose under 50% CRF; compared to T1, in A. × grandiflora young transplants, T5 increased root morphological characteristics, as well as number of leaves, leaf area, and dry biomass accumulation; in L. camara, T6 achieved higher performance. The application of biostimulants under 50% CRF also improved, in both A. × grandiflora and L. camara, the physiological and agronomical Nitrogen Use Efficiency, compared to a full CRF dose. This study can support decision-making in terms of agronomic technique choices in line with the sustainable development of high-quality ornamental transplant production. Full article

One of the main causes of climate change is the emission of GHGs, and one of the sources for the generation of such gasses is agriculture via plant production. Considering the foregoing, a study was conducted to assess PGPRs in strawberry cultivation which were able to limit GHG emissions. The first experimental factor was the inoculation of plant roots with the Bacillus sp. strains DLGB3, DKB26, DKB58, and DKB 84; the Pantoea sp. strains DKB63, DKB64, DKB65, and DKB68; Azotobacter sp. AJ 1.2; and Pseudomonas sp. PJ 1.1. The second experimental factor constituted the different moisture levels of the growth substrate. In the experiment, emissions of NH₃, CO₂, N₂O, and CH₄ were measured. In light of the conducted research, five strains were selected (Azotobacter sp. AJ 1.2; Pantoea sp. DKB64, DKB63, and DKB68; and Pseudomonas sp. strain PJ 1.1) that showed the greatest potential for reducing GHG emissions depending on the prevailing environmental conditions. The application of the tested bacterial strains under different moisture conditions in the substrate either reduced or did not affect GWP. This research on PGPR, which was conducted to select strains of rhizosphere bacteria that would be able to reduce GHG emissions, may form the basis for creating an inoculum and can be employed as an effective strategy for mitigating certain abiotic stresses. Full article

The implementation of soil conservation measures is essential to promote sustainable crop production in the Mediterranean region. In an organic rainfed almond orchard located in Lanjarón (SE, Spain), a study carried out during 2016–2021 analyzed the influence of different soil management strategies (SMSs) (TT, traditional tillage; NT, no tillage; VF, cover of Vicia faba; VS, cover of Vicia sativa; VS-VE, cover of Vicia sativa and Vicia ervilia) on some selected physical (bulk density, available water content, and aggregate stability), chemical (pH, electrical conductivity, soil-organic content, N, P, K, and micronutrients), and biological (microbial activity) soil properties, relevant to soil health, and their implications for yield and almond quality (physical and chemical). Our results showed that the SMS with legume cover improves soil properties, which had a favorable effect on soil health. The mean almond yield was not significantly affected by the SMS applied, being 315.9, 256.4, 229.1, 212.5, and 176.6 kg ha⁻¹ year⁻¹ for TT, VF, VS-VE, VS, and NT, respectively. Regarding the almond nut quality, the strategy based on implementation of legume cover increased the almond antioxidant activity and the total polyphenol content, which would improve their nutritional value. Here we showed how the use of sustainable SMSs improved the soil properties compared to traditional tillage in rainfed organic almonds, allowing the long-term sustainability of agroecosystems while at the same time obtaining higher nutritional quality almonds. Full article

Anaerobic soil disinfestation (ASD) consists of the application of labile organic materials to soil, flooding, and covering the soil surface with plastic film. Anaerobic soil disinfestation is a widely used ecofriendly alternative to chemical fumigation for eliminating soil-borne plant pathogens. However, the exact mode of action of ASD has not been elucidated. In particular, the potential role of anaerobic soil bacteria in disinfestation is unclear. In this study, we isolated a predominant bacterium designated as strain E801 from cocopeat medium after laboratory-scale ASD with ethanol as the carbon source. The strain was closely related with Clostridium kluyveri, and fermentatively produced butyrate and caproate from ethanol and acetate. Interestingly, the culture supernatant of strain E801 strongly suppressed the growth of Fusarium oxysporum f. sp. lycopersici (Fol) in a pH-dependent manner. Among the volatile fatty acids produced by E801, only caproate showed significant growth suppression at pHs below 5.5. In addition, caproate eliminated Fol conidia completely at pHs 5.5 and 5.0 and suppressed Fol growth even at a low temperature (15 °C). Furthermore, cocopeat medium amended with caproate eliminated Fol conidia completely within 6 days. These results suggest that caproate is one of the key disinfestation factors in ethanol-based ASD and that the direct application of caproate to soil could be a promising strategy for rapid and stable soil disinfestation. Full article

Soil degradation and high environmental costs impede agricultural production in North China. A 6-year field experiment was conducted to determine the effects of tillage practice and nitrogen application rate on changes in soil fertility and wheat yield. Four tillage systems (rotary tillage without maize straw return through 6 years, RT; rotary tillage with maize straw return through 6 years, RS; deep tillage with maize straw return through 6 years, DS; and rotary tillage through 2 years followed by deep tillage next year with maize straw applied for two cycles, RS/DS) and three N levels (HN, 300 kg N ha⁻¹, refers to traditional farming practice; MN, 0.75 × HN, 225 kg N ha⁻¹, to recommended N rate; and LN, 0.5 × HN, 150 kg N ha⁻¹, to reduced N rate) were tested. The soil organic carbon, labile organic carbon, inorganic N, available phosphorus, and available potassium under straw return treatments were significantly higher than RT in the 0–30 cm soil layer (p < 0.05). The microbial diversity, invertase, urease, and alkaline phosphatase activities also increased when maize straw was returned. Tillage practices could distribute maize straw in different depths of the soil and then affect soil nutrients, enzyme activity, and microbial diversity. The RS treatment presented the greatest effects in the 0–10 cm layer, while more significant impacts were observed in DS and RS/DS treatments at the 10–30 cm depths. The levels of soil nutrients and enzyme activity increased with an increased N rate. Compared to that under LN, wheat yields increased under HN and MN treatments, whereas there were no significant differences between HN and MN (p > 0.05). An increasing tendency of grain yield was observed in DS and RS/DS, while conversely so in RS. RS/DS had lower farm costs than DS during the study duration. Thus, RS/DS at 225 kg N ha⁻¹ is the best method for improving soil fertility and wheat yield. Full article

Permanent grasslands represent the main terrestrial ecosystem and serve as an important global reservoir of biodiversity, providing a wide range of benefits to humans and ecosystems. The effects of environment on permanent meadows (in our survey, they were centuries-old meadows that had not been plowed, mowed, or fertilized with manure) production have been adequately investigated in literature. However, plant species composition impact on potential feed value of first cut has still to be understood, in particular regarding different agronomic management. Our field trial was carried out in five farms, in a territory involved in the value chain of the Parmigiano Reggiano PDO (Val d’Enza, Northern Italy), over a two-year period (2017–2018). Differences in botanical composition, biomass, and Pastoral Value index (PV), which synthesizes grassland yield and nutritional parameters, were investigated in depth. The herbage dry matter (DM) yield was affected by year, farm, and their interaction factors. Its highest value across the two years was recorded in farm 5 (11.7 tons of DM ha⁻¹), which applied the highest rate of nitrogen fertilization. The botanical composition of the first cut has favored the presence of both Poaceae and ‘other species’ (each one around 40 plants per transect) compared to Fabaceae (seven plants per transect). However, higher numbers of Fabaceae plants (13 and 10) plausibly determined increases in PV in farms 3 and 5 (56.4 and 58.7, respectively). Although differences were observed among the most important nutritional parameters of grassland (crude protein, digestible and undigested neutral detergent fiber contents), suitable net energy for lactation (NE_L) values for feeding lactating cows were always recorded during the two years of survey. The present study provides a contribution of knowledge on how the botanical composition of permanent meadows may affect their potential nutritive value as fresh herbage for feeding dairy cows. Considering these results, the agronomic management should seek a level of plant biodiversity that at the same time might guarantee satisfactory yield and feed value, also in a context of climate change. Full article

Genetic dissection of complex traits by quantitative trait locus (QTL) analysis permits the understanding of the genotypic effects of QTL, interactions between QTLs, and QTL-by-environment interactions in wheat. This study aimed to identify the QTL linked to yield, its components, end-use quality traits including kernel, flour, and dough rheology, and related agronomic traits under dryland and irrigated conditions. A mapping population of 179 F_2:6 recombinant inbred lines (RILs) derived from ‘TAM 111’/‘TX05A001822’ was evaluated for these traits to investigate their genetic stability and phenotypic plasticity using 2658 single nucleotide polymorphisms (SNPs) with 35 linkage groups. Traits associated with chromosome regions were detected for individual and across-environment QTL by inclusive composite interval mapping. A total of 30 QTL regions were identified, including 14 consistent QTLs mapped on 11 chromosomes and six pleiotropic QTLs mapped on 5 chromosomes. Three consistent QTLs in chromosomes 1A, 3B, and 6D might be novel. Three major QTLs with both consistent and pleiotropic effects were co-localized with known genes. The first QTL for dough mixing properties was physically clustered around Glu-D1 and had an phenotypic variation explained (PVE) up to 31.3%. The second QTL for kernel-related traits was physically close to the TaCWI-4A (cell wall invertase) gene, which influences the thousand kernel weight, heading date, and harvest index, with a PVE of up to 12.3%. The third QTL, which was colocalized with the TaCWI-5D gene for kernel traits, was identified with a PVE of 6.7%. Epistasis was also detected, but major QTLs were not involved in significant epistasis or interactions with environmental effects. The current study provided new information that is useful for enhanced wheat breeding, which will benefit from the deployment of the favorable alleles for end-use quality, yield, and other agronomic traits in wheat-breeding programs through marker-assisted selection. Full article

Crop growth is often uneven within an agricultural parcel, even if it has been managed evenly. Aerial images are often used to determine the presence of vegetation and its spatial variability in field parcels. However, the reasons for this uneven growth have been less studied, and they might be connected to variations in topography, as well as soil properties and quality. In this study, we evaluated the relationship between drone image data and field and soil quality indicators. In total, 27 multispectral and RGB drone image datasets were collected from four real farm fields in 2016–2020. We analyzed 13 basic soil quality indicators, including penetrometer resistance in top- and subsoil, soil texture (clay, silt, fine sand, and sand content), soil organic carbon (SOC) content, clay/SOC ratio, and soil quality assessment parameters (topsoil biological indicators, subsoil macroporosity, compacted layers in the soil profile, topsoil structure, and subsoil structure). Furthermore, a topography variable describing water flow was used as an indicator. Firstly, we evaluated single pixel-wise linear correlations between the drone datasets and soil/field-related parameters. Correlations varied between datasets and, in the best case, were 0.8. Next, we trained and tested multiparameter non-linear models (random forest algorithm) using all 14 soil-related parameters as features to explain the multispectral (NIR band) and RGB (green band) reflectance values of each drone dataset. The results showed that the soil/field indicators could effectively explain the spatial variability in the drone images in most cases (R² > 0.5), especially for annual crops, and in the best case, the R² value was 0.95. The most important field/soil features for explaining the variability in drone images varied between fields and imaging times. However, it was found that basic soil quality indicators and topography variables could explain the variability observed in the drone orthomosaics in certain conditions. This knowledge about soil quality indicators causing within-field variation could be utilized when planning cultivation operations or evaluating the value of a field parcel. Full article

Chlorophyll is an important component of crop photosynthesis as it is necessary for the material exchange between crops and the atmosphere. The amount of chlorophyll present reflects the growth and health status of crops. Spectral technology is a feasible method for obtaining crop chlorophyll content. The first-order differential spectral index contains sufficient spectral information related to the chlorophyll content and has a high chlorophyll prediction ability. Therefore, in this study, the hyperspectral index data and chlorophyll content of soybean canopy leaves at different growth stages were obtained. The first-order differential transformation of soybean canopy hyperspectral reflectance data was performed, and five indices, highly correlated with soybean chlorophyll content at each growth stage, were selected as the optimal spectral index input. Four groups of model input variables were divided according to the following four growth stages: four-node (V4), full-bloom (R2), full-fruit (R4), and seed-filling stage (R6). Three machine learning methods, support vector machine (SVM), random forest (RF), and back propagation neural network (BPNN) were used to establish an inversion model of chlorophyll content at different soybean growth stages. The model was then verified. The results showed that the correlation coefficient between the optimal spectral index and chlorophyll content of soybean was above 0.5, the R2 period correlation coefficient was above 0.7, and the R4 period correlation coefficient was above 0.8. The optimal estimation model of soybean and chlorophyll content is established through the combination of the first-order differential spectral index and RF during the R4 period. The optimal estimation model validation set determination coefficient (R²) was 0.854, the root mean square error (RMSE) was 2.627, and the mean relative error (MRE) was 4.669, demonstrating high model accuracy. The results of this study can provide a theoretical basis for monitoring the growth and health of soybean crops at different growth stages. Full article

In order to increase water productivity at the Collective Irrigation System (CIS) level it is crucial to adapt the existing irrigation infrastructure, enhancing water intake at the source, as well as its transport and delivery efficiency. Rehabilitation may involve structural changes and thus, a large capital investment. This investment should be proportionate to the increase in climate resilience associated to different rehabilitation alternatives. A methodology framework was developed to evaluate CIS resilience to climate change considering different rehabilitation alternatives. The assessed components were: (i) crop production systems; (ii) on-farm irrigation systems; and (iii) project rehabilitation alternatives for the conveyance and distribution of the irrigation water from the source to the farmer fields. This framework was applied to the Maiorga CIS, in central Portugal, to test the methodology performance in assessing the impacts of climate change on the supply-demand balance of the proposed rehabilitation alternatives and to evaluate their climate resilience, for the representative concentration pathways, RCP4.5 and RCP8.5, and two time periods, 2041–2070 and 2071–2100. For each scenario, period, and rehabilitation alternative, irrigation requirements at the source (demand) and stream flows (supply) were computed and the supply-demand balance was performed. Projected increases in irrigation water demand varied between 5.5% for RCP4.5/2071–2100 and 35.7% for RCP8.5/2071–2100. For RCP4.5, 11% (2050) and 9% (2080) reductions in irrigation water supply were projected, while for RCP8.5 the reduction ranges between 13% (2050) and 30% (2080). The proposed framework determined that the rehabilitation alternatives considering just one type of water source, without flow regularization and with open channel distribution to the farmer’s field, have proved to be unviable due to low resilience to climate change. Full article

The failure of sustainable and agricultural intensifications in saving the ecosystem/public health has caused a paradigm shift to microbiome resource engineering through sustainable approaches. As agricultural intensification systems prioritize synthetic input applications over environmental health, sustainable intensification fails to define the end point of intensification, giving room for the application of “intensification” over “sustainability” to suit farmers’ needs. However, sustainable agricultural practices through microbiome resource services have been well harnessed and appreciated for their significant role in plant health and disease management due to their ability to secret agroactive metabolites with notable functionalities in a cooperative manner or as bioinoculants. The complexity of a cooperative microbiome and the uncontrollable nature of its numerous influencing parameters as well as the non-specificity associated with bioinoculant application, results in the direct utilization of agroactive compounds to obtain greater preventive efficiency. In this regard, the known bacterial trove has been seriously ransacked, yet there exists an inexhaustible bank of unknown compounds, which are conserved in Actinomycetes. However, the rare Actinomycetes group has received less attention than other plant growth-promoting bacteria; thus, the possibility exists that the Actinomycetes may encode novel useful metabolites. To unravel the possible uses of these metabolites for phytoprotection, smart culture-based techniques and genometabolomics technology have been applied. Hence the aim of this review is to express the sustainable nature of agro-antibiotics or biopesticide from these bacterial resources for the resolution of phytopathogenic havoc that reduces crop productivity. Full article

The Persea mite, Oligonychus perseae Tuttle, Baker & Abbatiello (Acari: Tetranychidae), is an economically important foliar pest of avocados in Spain. The effects of this mite on the foliar damage, production losses and economic impact were assessed in two avocado, cv. Hass, orchards located in the main growing areas of Spain (Northern Tenerife and Málaga) for 3 and 5 consecutive years, respectively. The economic injury level (EIL) for the optimization of the use of acaricides to control this mite was also established, considering three spraying strategies: (i) mite-free treatment (<50 mites per leaf), (ii) conventional treatment (50–150 mites per leaf), and (iii) control treatment (the absence of spraying). Persea mite populations were sampled fortnightly and foliar damage was estimated. At the end of each season, fruits were harvested, weighed and production losses were quantified. The cumulate mite-days (CMDs) had a significant effect on the percentage of leaf area damaged (PLAD) and yield reduction. High numbers of the Persea mite caused extensive damage to leaves, so a loss in tree yield was evident. However, for the middle population level, there was no evidence of yield losses. The quantitative EIL was estimated at a PLAD of 17%, equivalent to a CMDs of 178 mites per leaf, which is the amount of damage that should not be exceeded. In Northern Tenerife, with a mild climate, the Persea mite can reach significant populations that are maintained throughout the months. In avocado orchards in Málaga, the summer is hotter and drier, so the presence of the mite exists for a shorter duration in the seasons, with less damage to the leaves. In Tenerife, yield loss can be compensated by chemical treatments that permit pest control. Full article

The nutrient and nutraceutical quality of greenhouse wild rocket is strongly influenced by the light environment and nitrogen fertilization. We investigated the effects of two cover materials, a diffuse light film (Film1) and a traditional clear film (Film2), and three nitrogen regimes, no N supply (N0) and sub-optimal (N1) and optimal (N2) doses, also in combination with a biostimulant (Stimolo Mo), on the mineral composition, antioxidant properties and chlorophyll and carotenoid content of rocket plants grown in the autumn–spring cycle. The leaf concentration of most of the minerals was higher under Film1 compared to Film2. In general, K, Ca, Mg and Na were higher, and S was lower in the presence of N supply, and the addition of the biostimulant promoted the mineral uptake. Under Film1, the hydrophilic antioxidant activity (HAA) was higher in some harvests, and the ABTS antioxidant activity (ABTS AA) in the first one, while always lower afterward, than under Film2. Nitrogen fertilization did not affect the antioxidant activity, while it reduced the content of total phenols and ascorbic acid. The biostimulant application increased ABTS AA at the optimal N dose and reduced total phenols in unfertilized plants. Both the diffuse light and the N supply inhibited the synthesis of ascorbic acid, while N fertilization and the biostimulant promoted the synthesis of chlorophylls. The experimental treatments exerted variable effects over time and significant interactions with the harvest period were found for many of the investigated parameters. Full article

With advances in precision agriculture, autonomous agricultural machines can reduce human labor, optimize workflow, and increase productivity. Accurate and reliable obstacle-detection and avoidance systems are essential for ensuring the safety of automated agricultural machines. Existing LiDAR-based obstacle detection methods for the farmland environment process the point clouds via manually designed features, which is time-consuming, labor-intensive, and weak in terms of generalization. In contrast, deep learning has a powerful ability to learn features autonomously. In this study, we attempted to apply deep learning in LiDAR-based 3D obstacle detection for the farmland environment. In terms of perception hardware, we established a data acquisition platform including LiDAR, a camera, and a GNSS/INS on the agricultural machine. In terms of perception method, considering the different agricultural conditions, we used our datasets to train an effective 3D obstacle detector, known as Focal Voxel R-CNN. We used focal sparse convolution to replace the original 3D sparse convolution because of its adaptable ability to extract effective features from sparse point cloud data. Specifically, a branch of submanifold sparse convolution was added to the upstream of the backbone convolution network; this adds weight to the foreground point and retains more valuable information. In comparison with Voxel R-CNN, the proposed Focal Voxel R-CNN significantly improves the detection performance for small objects, and the AP in the pedestrian class increased from 89.04% to 92.89%. The results show that our model obtains an mAP of 91.43%, which is 3.36% higher than the base model. The detection speed is 28.57 FPS, which is 4.18 FPS faster than the base model. The experiments show the effectiveness of our model, which can provide a more reliable obstacle detection model for autonomous agricultural machines. Full article

Organic farming nowadays is held up as a model of sustainability; however, this is not always an economic advantage for farmers due to the reduced yields compared to the conventional regime. The aim of the study is therefore to provide an environmental and economic analysis of the innovative organic model proposed by the Bresov project to assess its sustainability. The study is therefore based on a Life Cycle Assessment methodology and the economic evaluation, through the calculation of the gross income of innovative organic and conventional broccoli cultivation in Sicily. The impact categories analysed reported a 60–100% reduction in impact in the case of innovative organic compared to conventional. From an economic point of view, although there is a minimal reduction in yield in organic compared to conventional, there is an increase in production costs that translates into a reduction in the gross income of approximately 61%. These gaps are filled when the organic product is granted a premium price and thanks to aid from the Common Agricultural Policy. The innovative organic approach, characterised by new products and soil management methods, confirms it as an alternative to conventional. This approach contrasts with the mere substitution of synthetic products. Full article

Weather is an essential component of natural resources that affects agricultural production and plays a decisive role in deciding the type of agricultural production, planting structure, crop quality, etc. In field agriculture, medium- and long-term predictions of temperature and humidity are vital for guiding agricultural activities and improving crop yield and quality. However, existing intelligent models still have difficulties dealing with big weather data in predicting applications, such as striking a balance between prediction accuracy and learning efficiency. Therefore, a multi-head attention encoder-decoder neural network optimized via Bayesian inference strategy (BMAE-Net) is proposed herein to predict weather time series changes accurately. Firstly, we incorporate Bayesian inference into the gated recurrent unit to construct a Bayesian-gated recurrent units (Bayesian-GRU) module. Then, a multi-head attention mechanism is introduced to design the network structure of each Bayesian layer, improving the prediction applicability to time-length changes. Subsequently, an encoder-decoder framework with Bayesian hyperparameter optimization is designed to infer intrinsic relationships among big time-series data for high prediction accuracy. For example, the R-evaluation metrics for temperature prediction in the three locations are 0.9, 0.804, and 0.892, respectively, while the RMSE is reduced to 2.899, 3.011, and 1.476, as seen in Case 1 of the temperature data. Extensive experiments subsequently demonstrated that the proposed BMAE-Net has overperformed on three location weather datasets, which provides an effective solution for prediction applications in the smart agriculture system. Full article

Understanding the grassland ecosystem is crucial for improving grassland ecosystem functions and services such as climate regulation, water and soil conservation, carbon sequestration, and biodiversity and gene pool maintenance. However, a systematic and comprehensive review of the relevant literature is still unclear and lacking. The VOSviewer software and cluster analysis were used to visually analyze and perform dimension reduction classification on the 27,778 studies related to grassland ecosystem research based on the Web of Science database. The number of publications targeting grassland ecosystem increased rapidly from 2006 to 2021. Ecology, agronomy, and environmental science were the most popular research categories, and the top journal sources were Remote Sensing, Journal of Ecology, and Ecology and Evolution. The leading publishing countries were the United States, China, and Germany. The top three institutions were the Chinese Academy of Sciences, the University of Chinese Academy of Sciences, and Colorado State University. Cooperation between different countries and institutions had increased. Keyword co-occurrence network analysis showed that Biodiversity, Vegetation and Conservation were the most popular study areas, grassland management, climate change, land use pattern, and ecosystem impact were the hot research topics. All studies could be divided into three categories by cluster analysis: grassland ecological characteristics including basic physicochemical properties, vegetation community characteristics, aboveground and belowground biomass, and soil structural quality of grassland; driving mechanisms that demonstrated effects of human activities and climate change on grassland ecosystem function; and grassland ecosystem services that focused the influences of different grassland management strategies on ecological services, animal welfare and human well-being. The three topic categories of reviewed studies were interrelated and consistent with each other, and the performances were progressive. This paper reviewed the trend evolution through keyword hotspots and analyzed the future research directions to provide an important reference for scientists to better respond to the balance of herbage and sustainable utilization of grassland and maintenance of ecological security. Full article

The most devastating abiotic factors worldwide are drought and salinity, causing severe bottlenecks in the agricultural sector. To acclimatize to these harsh ecological conditions, plants have developed complex molecular mechanisms involving diverse gene families. Among them, S-adenosyl-L-methionine synthetase (SAMS) genes initiate the physiological, morphological, and molecular changes to enable plants to adapt appropriately. We identified and characterized 16 upland cotton SAMS genes (GhSAMSs). Phylogenetic analysis classified the GhSAMSs into three major groups closely related to their homologs in soybean. Gene expression analysis under drought and salt stress conditions revealed that GhSAMS2, which has shown the highest interaction with GhCBL10 (a key salt responsive gene), was the one that was most induced. GhSAMS2 expression knockdown via virus-induced gene silencing (VGIS) enhanced transgenic plants’ susceptibility to drought and salt stress. The TRV2:GhSAMS2 plants showed defects in terms of growth and physiological performances, including antioxidative processes, chlorophyll synthesis, and membrane permeability. Our findings provide insights into SAMS genes’ structure, classification, and role in abiotic stress response in upland cotton. Moreover, they show the potential of GhSAMS2 for the targeted improvement of cotton plants’ tolerance to multiple abiotic stresses. Full article

Due to the dense distribution of tomato fruit with similar morphologies and colors, it is difficult to recognize the maturity stages when the tomato fruit is harvested. In this study, a tomato maturity recognition model, YOLOv5s-tomato, is proposed based on improved YOLOv5 to recognize the four types of different tomato maturity stages: mature green, breaker, pink, and red. Tomato maturity datasets were established using tomato fruit images collected at different maturing stages in the greenhouse. The small-target detection performance of the model was improved by Mosaic data enhancement. Focus and Cross Stage Partial Network (CSPNet) were adopted to improve the speed of network training and reasoning. The Efficient IoU (EIoU) loss was used to replace the Complete IoU (CIoU) loss to optimize the regression process of the prediction box. Finally, the improved algorithm was compared with the original YOLOv5 algorithm on the tomato maturity dataset. The experiment results show that the YOLOv5s-tomato reaches a precision of 95.58% and the mean Average Precision (mAP) is 97.42%; they are improved by 0.11% and 0.66%, respectively, compared with the original YOLOv5s model. The per-image detection speed is 9.2 ms, and the size is 23.9 MB. The proposed YOLOv5s-tomato can effectively solve the problem of low recognition accuracy for occluded and small-target tomatoes, and it also can meet the accuracy and speed requirements of tomato maturity recognition in greenhouses, making it suitable for deployment on mobile agricultural devices to provide technical support for the precise operation of tomato-picking machines. Full article

Pendimethalin herbicide toxicity to rice plants and barnyard grass invasion have increasingly affected the productivity of direct-seeded rice (DSR) in the fields. Whether and how to promote DSR productivity and sustain weed management depend on the appropriate pre-emergence herbicide application rate to minimise its toxicity in the rice ecosystem. Pot experiments were conducted to determine the effects of pendimethalin rates (1.5, 1.75, 2.0 kg a.i. ha⁻¹, two control treatments include the untreated control and the treated control with 1.5 kg a.i. ha⁻¹ S-metolachlor) on barnyard grass (Echinochloa crus-galli (L.) Beaux) and their potential toxicity risk to photosynthetic performances of rice (Topaz and Sen pidao). All the pendimethalin treatments provided excellent control of barnyard grass. Among the treatments, 1.5, 1.75, 2.0 kg a.i. ha⁻¹ pendimethalin and 1.5 kg a.i. ha⁻¹ S-metolachlor (treated control) decreased leaf area of barnyard grass significantly by 38.9, 49.6, 49.6 and 46.2%, respectively, compared with the untreated control at 40 days after sowing (DAS). The above-ground biomass of barnyard grass significantly decreased by 40% (1.48 g plant⁻¹) with 2.0 kg a.i. ha⁻¹ pendimethalin and by 46.2% (1.33 g plant⁻¹) when 1.5 kg a.i. ha⁻¹S-metolachlor was applied at 40 DAS compared with the untreated pots. Higher pendimethalin rates increased toxicity in Topaz and Sen pidao varieties, and 2.0 kg a.i. ha⁻¹ pendimethalin significantly reduced effective quantum yield (light-adapted) of photosystem (PS) II by 18% (0.58) and 19% (0.52), respectively, compared with the untreated control. Application of 2.0 kg a.i. ha⁻¹ pendimethalin rate significantly decreased the maximum quantum yield (dark-adapted) of Sen pidao (0.66) compared with 1.5 kg a.i. ha⁻¹ pendimethalin (0.68) including the untreated control. All pendimethalin treatments suppressed above-ground biomass at 55 DAS, but above-ground biomass of barnyard grass significantly decreased by 59.9% when 2.0 kg a.i. ha⁻¹ pendimethalin was applied compared with the untreated control. Although application of 1.5 kg a.i. ha⁻¹ pendimethalin rates reduced the effective quantum yield (light-adapted) of photosystem II of Sen pidao (0.55) by a small percentage (8%) than Topaz (0.65), it was non-toxic for both varieties compared with 2.0 kg a.i. ha⁻¹ pendimethalin. Therefore, the use of 1.5 kg a.i. ha⁻¹ pendimethalin can be used for effective weed management in the direct seeding of rice at an early growth stage. Full article

Satellite imagery is gaining popularity as a valuable tool to lower the impact on natural resources and increase profits for farmers. The purpose of this study is twofold: to mine the scientific literature to reveal the structure of this research domain, and to investigate to what extent scientific results can reach a wider public audience. To meet these two objectives, a Web of Science and a Twitter dataset were retrieved and analysed, respectively. For the academic literature, different performances of various countries were observed: the USA and China resulted as the leading actors, both in terms of published papers and employed researchers. Among the categorised keywords, “resolution”, “Landsat”, “yield”, “wheat” and “multispectral” are the most used. Then, analysing the semantic network of the words used in the various abstracts, the different facets of the research in satellite remote sensing were detected. The importance of retrieving meteorological parameters through remote sensing and the broad use of vegetation indexes emerged from these analyses. As emerging topics, classification tasks for land use assessment and crop recognition stand out, alongside the use of hyperspectral sensors. Regarding the interaction of academia with the public, the analysis showed that it is practically absent on Twitter: most of the activity therein stems from private companies advertising their business. This shows that there is still a communication gap between academia and actors from other societal sectors. Full article

In order to solve the problem of an accurate recognition of tea picking through tea picking robots, an edge device detection method is proposed in this paper based on ShuffleNetv2-YOLOv5-Lite-E for tea with one bud and two leaves. This replaces the original feature extraction network by removing the Focus layer and using the ShuffleNetv2 algorithm, followed by a channel pruning of YOLOv5 at the neck layer head, thus achieving the purpose of reducing the model size. The results show that the size of the improved generated weight file is 27% of that of the original YOLOv5 model, and the mAP value of ShuffleNetv2-YOLOv5-Lite-E is 97.43% and 94.52% on the pc and edge device respectively, which are 1.32% and 1.75% lower compared to that of the original YOLOv5 model. The detection speeds of ShuffleNetv2-YOLOv5-Lite-E, YOLOv5, YOLOv4, and YOLOv3 were 8.6 fps, 2.7 fps, 3.2 fps, and 3.4 fps respectively after importing the models into an edge device, and the improved YOLOv5 detection speed was 3.2 times faster than that of the original YOLOv5 model. Through the detection method, the size of the original YOLOv5 model is effectively reduced while essentially ensuring recognition accuracy. The detection speed is also significantly improved, which is conducive to the realization of intelligent and accurate picking for future tea gardens, laying a solid foundation for the realization of tea picking robots. Full article

Plant nutrition through mineral fertilizers is commonly used in soilless culture systems. Our study aims to replace intensive mineral fertilizers with bio-fertilizers, at least partially. We supplemented 50% of the mineral fertilizers with Chlorella vulgaris microalgae, a mix of beneficial bacteria and mycorrhiza. In addition, we investigated how to enhance spinach quality by implementing a sustainable and eco-friendly production method. Our research focused on analyzing the parameters of leaf quality and nitrate accumulation of baby spinach grown in a floating culture system utilizing biofertilizers. When mycorrhiza, algae, and bacteria supplemented 50% of mineral fertilizers, 17.5%, 20%, and 21.9% fewer leaf yields than 100% mineral fertilizers (5270 g m⁻²) were achieved. However, biofertilizers improved the internal leaves’ quality of hydroponically grown baby spinach. The highest amount of total phenolic (356.88 mg gallic acid 100g⁻¹), vitamin C (73.83 mg 100 g⁻¹), total soluble solids (9.4%), phosphorus (0.68%), and iron (120.07 ppm) content were obtained by using mycorrhiza. Bacteria induced the lowest nitrate content (206 mg kg⁻¹) in spinach leaves, while 100% mineral fertilizers showed the highest nitrate (623 mg kg⁻¹) concentration. Moreover, bacteria provided the highest SPAD-chlorophyll (73.72) and titrable acidity (0.31%). The use of microalgae, Chlorella vulgaris, induced the highest amount of potassium (9.62%), calcium (1.64%), magnesium (0.58%), zinc (75.21 ppm), and manganese (64.33 mg kg⁻¹). In conclusion, our findings demonstrate that the utilization of biofertilizers has the potential to significantly reduce the reliance on mineral fertilizers by up to 50%. Furthermore, an improvement in the quality of baby spinach, as evidenced by an increase in health-beneficial compounds, is possible. Thus, implementing biofertilizers in the cultivation of soilless baby spinach presents a promising approach to achieving both environmental sustainability and improved crop quality. Full article

Cyperus esculentus is an invasive troublesome neophyte in many arable crops across the globe. Analysis of the genetic and morphological profile of local C. esculentus clonal populations may be useful in explaining differential herbicide sensitivity found among distinct clonal populations and spatial distribution patterns. In this study, 35 Belgian C. esculentus clonal populations, evenly spread across the entire infestation area (30,689 km²) and covering a great diversity of farm and soil types, and hydrological and environmental conditions, were genetically characterized using amplified fragment length polymorphism (AFLP) profiling. These clonal populations were also grouped into different morphological clusters using data from shoot, tuber, and inflorescence number, fresh tuber biomass, individual fresh tuber weight, and number of germinable seeds collected in three consecutive years. Of the 271 AFLP markers, 207 were polymorphic. The gene diversity among clonal populations was 0.331 and three genetically distinct clusters were identified. Depending on observation year, clonal populations were grouped in four to five morphologically distinct clusters that closely aligned with the genetic clusters. The genetically distinct clusters differed in their geographical distribution pattern and range as well as in their morphological characteristics. Clonal populations belonging to clusters with broad distribution ranges produced numerous viable seeds. Clusters with clonal populations that produced large tubers were less widespread than the cluster with clonal populations producing many small tubers. The results suggest that tuber size, tuber number, and fecundity may all play an important role in the spread of C. esculentus. Morphotyping may be very useful in designing effective preventive and curative C. esculentus management strategies. Full article

Natural products have been postulated as an alternative to the use of synthetic herbicides in pest control. The latter compounds have caused numerous problems and these include the appearance of resistance to such herbicides. Aminophenoxazinones are natural products that have shown multiple biological activities, such as pharmacological or phytotoxic effects. In the case of phytotoxicity, the mode of action of aminophenoxazinones has not been widely exploited in agriculture and resistant weeds have not been reported to date. This fact makes aminophenoxazinones promising candidates in the development of herbicides. In the study reported here, seven aminophenoxazinone derivatives have been synthesized and their phytotoxicity has been evaluated on wheat coleoptiles and two important agricultural weeds (Lolium rigidum and Portulaca oleracea). Several derivatives have shown phytotoxic activity, which is similar to the positive control pendimethalin, and even higher in some cases at the highest concentrations tested. The most affected parameter in weeds was root length and the most susceptible weed was P. oleracea. Compound 2, in which nitrogen atoms are present in the heterocycles, was the most active and this was followed by compound 1. Modifications at C-8 led to a reduced activity, with the exception of the nitro compound on the root length of P. oleracea. However, the phytotoxicity also increased on introducing an iodo-substituent at C-4. The results highlight aminophenoxazinones as promising candidates in the development of natural herbicides. Full article

Phosphorus (P) is the most important substance in inorganic fertilizers used in the agriculture industry. In this study, a multi-product and multi-objective model is presented considering economic and environmental concerns to design a renewable and sustainable P-fertilizer supply chain management (PFSCM) strategy. To handle the complexities of the model, an ensemble heuristic–metaheuristic algorithm utilizing the heuristic information available in the model, the whale optimization algorithm, and a variable neighborhood search (named H-WOA-VNS) is proposed. First, a problem-dependent heuristic is designed to generate a set of near-optimal feasible solutions. These solutions are fed into a population-based whale optimization algorithm which benefits from exploration and exploitation strategies. Finally, the single-solution variable neighborhood search is applied to further improve the quality of the solution using local search operators. The objective function of the algorithm is formulated as a weighted average function to minimize total economic cost while increasing crop yield and P use efficiency. The experimental results for a real case study of the P-fertilizer supply chain confirm the effectiveness of the proposed method in improving the crop yield and P use efficiency by 33% and 27.8%, respectively. The results demonstrate that the proposed H-WOA-VNS algorithm outperforms the Heuristic, WOA, and VNS models in reducing the total objective function value of the PFSCM model by 9.8%, 2.9%, and 4%, respectively. Full article

‘Candidatus Liberibacter solanacearum’ (Lso) is a phloem-limited bacterial plant pathogen infecting solanaceous plants in the Americas and New Zealand and is associated with diseases of apiaceous crops in Europe, Northern Africa, and the Middle East. This pathogen is also related to other Liberibacter species that infect other crops. In the USA, two haplotypes of Lso, LsoA and LsoB, are predominant and responsible for diseases in potato and tomato. Tobacco, Nicotiana tabacum, a model species to study plant defenses, is a host for Lso; therefore, the interaction between Lso and this host plant could be used to study Liberibacter−plant interactions. In this study, we characterized the infection associated with LsoA and LsoB in tobacco. Under laboratory conditions, LsoB caused more severe symptoms than LsoA, and LsoA and LsoB titers were dynamic during the 7 weeks of the experiment. We also measured SA and other metabolites, including oxylipins, at an early point of infection and found that SA was accumulated in plants infected with LsoB but not with LsoA; whereas ABA levels were reduced in LsoA- but not in LsoB-infected plants. Full article

Positively affecting crop quality and yields, amino acids used as plant stimulants play a special role in ensuring global food security. L-tryptophan (L-Try) and L-glutamic acid (L-Glu) are important biostimulants that increase the yield of field crops and vegetables. Carrot is one of the most important vegetables due to its production volume in the world (sixth most consumed vegetable) and its nutritional value. The response of different plant species to amino acid application varies. The literature mainly deals with the effects of ready-made products containing a mixture of several amino acids, with no exhaustive studies on the effects of individual amino acids on carrot quality and yield. This paper is based on a two-year field experiment (2019–2020), in which the effect of two amino acids, L-Trp (7.5 g·ha⁻¹) and L-Glu (60.0 g·ha⁻¹), on carrot (Daucus carota L.) was investigated. They were applied to the leaves (FA) or both to the soil and to the leaves (S + FA), separately, (L-Trp or L-Glu) or as a mixture (L-Trp + L-Glu). The control plot was treated with mineral fertilizers only. The research was conducted as a field experiment in a split-block design. The yield of carrot storage roots and their content of dry matter, protein, sugars, total soluble solids (TSS), and ascorbic acid were determined. The amino acids positively affected the yield of carrots compared to the control, but only the synergistic action of L-tryptophan and L-glutamic acid increased it significantly. On average, for both amino acids the S + FA application increased the protein content and the marketable yield of storage roots significantly more than in response to FA treatment. A significant increase in marketable yield compared to the control was found after the combined soil and foliar application (S + FA) of all amino acid combinations, but the L-Trp + L-Glu mixture worked best. The storage roots of carrots grown on the plot with L-Trp + L-Glu contained significantly more protein and TSS than those on the control plot. The content of TSS was also positively affected by L-Trp used on its own, while L-Glu increased the content of ascorbic acid. Amino acids applied to the leaves (FA) increased the content of total sugars in the carrot roots more than when applied both to the soil and to the leaves (S + FA). Of all treatment combinations, the synergistic action of L-Trp and L-Glu made it possible to obtain the highest yields of carrot storage roots, containing the most protein and total soluble solids. Full article

In this study, a Box-Behnken Design (BBD) has been used to optimize the recovery of bioactive compounds from jabuticaba (Myrciaria cauliflora) by-products through microwave-assisted extraction (MAE). Thus, the Box-Behnken (BBD) experimental design was followed by a response surface methodology (RSM) that would allow investigating the influence of the four independent variables (temperature, solvent composition, pH, and sample-solvent ratio) that have been considered. The results revealed that temperature and solvent composition (%MeOH) were the most significant factors regarding the yields of anthocyanins obtained from the studied plant matrix. The established optimized conditions and 10-min-extraction time resulted in yields of 9.70 ± 0.28 mg g⁻¹ of total anthocyanins. The method exhibited good repeatability and intermediate precision, with RSD variations lower than 5% for both parameters. The developed process was also able to extract and identify anthocyanins in commercial samples (jabuticaba pulp and jam). The results obtained from the optimized MAE method suggest that this technique is not only efficient for the recovery of anthocyanins from jabuticaba by-products, but it is also beneficial for a circular economy approach. Full article

Rapid and accurate crop chlorophyll content estimation and the leaf area index (LAI) are both crucial for guiding field management and improving crop yields. This paper proposes an accurate monitoring method for LAI and soil plant analytical development (SPAD) values (which are closely related to leaf chlorophyll content; we use the SPAD instead of chlorophyll relative content) based on the fusion of ground–air multi-source data. Firstly, in 2020 and 2021, we collected unmanned aerial vehicle (UAV) multispectral data, ground hyperspectral data, UAV visible-light data, and environmental cumulative temperature data for multiple growth stages of summer maize, respectively. Secondly, the effective plant height (canopy height model (CHM)), effective accumulation temperature (growing degree days (GDD)), canopy vegetation index (mainly spectral vegetation index) and canopy hyperspectral features of maize were extracted, and sensitive features were screened by correlation analysis. Then, based on single-source and multi-source data, multiple linear regression (MLR), partial least-squares regression (PLSR) and random forest (RF) regression were used to construct LAI and SPAD inversion models. Finally, the distribution of LAI and SPAD prescription plots was generated and the trend for the two was analyzed. The results were as follows: (1) The correlations between the position of the hyperspectral red edge and the first-order differential value in the red edge with LAI and SPAD were all greater than 0.5. The correlation between the vegetation index, including a red and near-infrared band, with LAI and SPAD was above 0.75. The correlation between crop height and effective accumulated temperature with LAI and SPAD was above 0.7. (2) The inversion models based on multi-source data were more effective than the models made with single-source data. The RF model with multi-source data fusion achieved the highest accuracy of all models. In the testing set, the LAI and SPAD models’ R² was 0.9315 and 0.7767; the RMSE was 0.4895 and 2.8387. (3) The absolute error between the extraction result of each model prescription map and the measured value was small. The error between the predicted value and the measured value of the LAI prescription map generated by the RF model was less than 0.4895. The difference between the predicted value and the measured value of the SPAD prescription map was less than 2.8387. The LAI and SPAD of summer maize first increased and then decreased with the advancement of the growth period, which was in line with the actual growth conditions. The research results indicate that the proposed method could effectively monitor maize growth parameters and provide a scientific basis for summer maize field management. Full article

Accurate information on the spatial distribution of weeds is the key to effective site-specific weed management and the efficient and sustainable use of weed control measures. This work focuses on the early detection of johnsongrass, common cocklebur and velvetleaf present in a corn field using high resolution airborne hyperspectral imagery acquired when corn plants were in a four to six leaf growth stage. Following the appropriate radiometric and geometric corrections, two supervised classification techniques, such as spectral angle mapper (SAM) and spectral mixture analysis (SMA) were applied. Two different procedures were compared for endmember selections: field spectral measurements and automatic methods to identify pure pixels in the image. Maps for both, overall weeds and for each of the three weed species, were obtained with the different classification methods and endmember sources. The best results were achieved by defining the endmembers through spectral information collected with a field spectroradiometer. Overall accuracies ranged between 60% and 80% using SAM for maps that do not differentiate the weed species while it decreased to 52% when the three weed species were individually classified. In this case, the SMA classification technique clearly improved the SAM results. The proposed methodology shows it to be a promising prospect to be applicable to low cost images acquired by the new generation of hyperspectral sensors onboard unmanned aerial vehicles (UAVs). Full article

Maize diseases are reported to occur often, and are complicated and difficult to control, which seriously affects the yield and quality of maize. This paper proposes an improved YOLOv5n model incorporating a CA (Coordinate Attention) mechanism and STR (Swin Transformer) detection head, CTR_YOLOv5n, to identify common maize leaf spot, gray spot, and rust diseases in mobile applications. Based on the lightweight model YOLOv5n, the accuracy of the model is improved by adding a CA attention module, and the global information acquisition capability is enhanced by using TR2 as the detection head. The average recognition accuracy of the algorithm model can reach 95.2%, which is 2.8 percent higher than the original model, and the memory size is reduced to 5.1MB compared to 92.9MB of YOLOv5l, which is 94.5% smaller and meets the requirement of being light weight. Compared with SE, CBAM, and ECA, which are the mainstream attention mechanisms, the recognition effect we used is better and the accuracy is higher, achieving fast and accurate recognition of maize leaf diseases with fewer computational resources, providing new ideas and methods for real-time recognition of maize and other crop spots in mobile applications. Full article

A three-year study was conducted to analyze agronomic management in the production of winter oilseed rape (WOSR) under different tillage systems. A field experiment was conducted at the University’s Agricultural Experiment Station in Bałcyny (north-eastern Poland), in three growing seasons (2016/2017, 2017/2018, and 2018/2019). The experiment had a 3^5–2 resolution III fractional factorial design with five fixed factors that were tested at three levels of intensity. The experimental factors were: A—tillage: (A0) strip-till, (A1) low-till, (A2) conventional tillage; B—weed control: (B0) pre-emergent, (B1) foliar, (B2) sequential; C—growth regulation: (C0) none, (C1) in fall, (C2)—in fall and spring; D—rate of nitrogen (N) fertilizer applied in spring: (D0) 160, (D1) 200, (D2) 240 kg ha⁻¹; and E—rate of sulfur (S) fertilizer applied in spring: (E0) 0, (E1) 40, (E2) 80 kg ha⁻¹. The crude fat (CF) content of WOSR seeds was highest in the strip-till system (498 g kg⁻¹ dry matter, DM), and the total protein (TP) content of seeds was highest (196 g kg⁻¹ DM) in low-till and conventional tillage systems. The content of neutral detergent fiber (NDF) was higher in seeds harvested from strip-till and low-till systems than from the conventional tillage system. The seeds of WOSR plants grown in the conventional tillage system accumulated more (by 0.4%) polyunsaturated fatty acids (PUFAs) and less (by 0.5–0.6%) monounsaturated fatty acids (MUFAs). An increase in the N rate from 160–200 to 240 kg ha⁻¹ decreased the CF content (495 vs. 484 g kg⁻¹ DM) and increased the TP content of seeds (191 vs. 199 g kg⁻¹ DM). Sulfur fertilization induced a 34% increase in glucosinolate (GLS) concentrations in WOSR seeds, mainly by enhancing the biosynthesis of alkenyl GLS (by 39%). Full article

The growing number of photovoltaic installations has created competition in land use between the need for electricity and food. Agrivoltaic systems (AVSs) can help solve this problem by increasing land use efficiency through the co-production of electricity and food. However, in Japan, where more than 2000 AVSs have been installed, some undesirable AVS cases have led to new problems. In this study, we developed an AVS installation model that is compatible with a regional society and limits the scale of AVS installation to a low-risk level. AVS projects have also entered local industrial clusters and stimulated the local economy. In this study, we used public information and geographic information systems to ensure quantifiability and applicability. The results revealed that the rural area targeted in this study had an AVS generation potential of 215% (equal to 17.8 GWh) of the region’s annual electricity consumption and an economic ripple effect of 108.9% (EUR 47.8 million) of the region’s gross regional product. Furthermore, the levelized cost of electricity was estimated to be 14.94–25.54 Euro cents/kWh under secure settings. This study provides solutions to food, economic, and energy problems in rural areas by promoting the installation of AVSs. Full article

Flowering in temperate fruit trees depends on the temperatures during the previous months; chill is required to overcome endodormancy, and then heat exposure is needed. These agroclimatic requirements are cultivar-specific and determine their adaptability to the growing area and their response to climate change. We aim to estimate the agroclimatic requirements of 16 traditional cultivars of European pears grown in Zaragoza (Spain). We used Partial Least Squares regression analysis to relate 20-year records of flowering dates to the temperatures of the 8 previous months. This approach allowed us to establish the chilling and forcing periods, through which we quantified temperatures with three models for chill accumulation (Chilling Hours, Utah model, and Dynamic model) and one model for heat accumulation (Growing Degree Hours). The results indicated very little difference in the chilling and forcing periods. Chill requirements ranged from 43.9 to 49.2 Chill Portions; from 1027 to 1163 Chilling Units; and from 719 to 774 Chilling Hours. Heat requirements ranged from 6514 to 7509 Growing Degree Hours. Flowering dates were mainly determined by the temperatures during the chilling period. This means that reductions in winter chill caused by global warming in many regions could cause flowering delays or even failures in the fulfillment of chill requirements. Full article