Agriculture, Volume 14, Issue 1 (January 2024) – 162 articles

The contribution of animal waste storage on GHG emissions and climate change is a serious issue for agriculture. The carbon emissions that are generated from barns represent a relevant source of emissions that negatively affect the environmental performance measures of livestock production. In this experiment, CO₂ and CH₄ emissions from different animal wastes, namely, digestate, slurry, and manure, were evaluated both in their original form and with a biochar addition. The emissions were monitored using the static camber methodology and a portable gas analyzer for a 21-day period. The addition of biochar (at a ratio of 2:1 between the substrates and biochar) significantly reduced the emissions of both gases compared to the untreated substrates. Slurry exhibited higher emissions due to its elevated gas emission tendency. The biochar addition reduced CO₂ and CH₄ emissions by 26% and 21%, respectively, from the slurry. The main effect of the biochar addition was on the digestate, where the emissions decreased by 45% for CO₂ and 78% for CH₄. Despite a lower tendency to emit carbon-based gases of manure, biochar addition still caused relevant decreases in CO₂ (40%) and CH₄ (81%) emissions. Biochar reduced the environmental impacts of all treatments, with a GWP reduction of 55% for the digestate, 22% for the slurry, and 44% for the manure. Full article

In recent years, agricultural remote sensing technology has made great progress. The availability of sensors capable of detecting electromagnetic energy and/or heat emitted by targets improves the pre-harvest process and therefore becomes an indispensable tool in the post-harvest phase. Therefore, we outline how remote sensing tools can support a range of agricultural processes from field to storage through crop yield estimation, grain quality monitoring, storage unit identification and characterization, and production process planning. The use of sensors in the field and post-harvest processes allows for accurate real-time monitoring of operations and grain quality, enabling decision-making supported by computer tools such as the Internet of Things (IoT) and artificial intelligence algorithms. This way, grain producers can get ahead, track and reduce losses, and maintain grain quality from field to consumer. Full article

Systematic biases in general circulation models (GCM) and regional climate models (RCM) impede their direct use in climate change impact research. Hence, the bias correction of GCM-RCMs outputs is a primary step in such studies. This study compares the potential of two bias correction methods (the method from the third phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP3) and Detrended Quantile Matching (DQM)) applied to the raw outputs of daily data of minimum and maximum air temperatures and precipitation, in the Cap-Bon region, from eight GCM-RCM combinations. The outputs of GCM/RCM combinations were acquired from the European branch of the coordinated regional climate downscaling experiment (EURO-CORDEX) dataset for historical periods and under two representative concentration pathway (RCP4.5 and RCP8.5) scenarios. Furthermore, the best combination of bias correction/GCM-RCM was used to assess the impact of climate change on reference evapotranspiration (ET₀). Numerous statistical indicators were considered to evaluate the performance of the bias correction/historical GCM-RCMs compared to the observed data. Trends of the Hargreaves–Samani_ET₀ model during the historical and projected periods were determined using the TFPMK method. A comparison of the bias correction methods revealed that, for all the studied model combinations, ISIMIP3 performs better in reducing biases in monthly precipitation. However, for Tmax and Tmin, the biases are greatly removed when the DQM bias correction method is applied. In general, better results were obtained when the HadCCLM model was used. Before applying bias correction, the set of used GCM-RCMs projected reductions in precipitation for most of the months compared to the reference period (1982–2006). However, Tmin and Tmax are expected to increase in all months and for the three studied periods. Hargreaves–Samani ET₀ values obtained from the best combination (DQM/ HadCCLM) show that RCP8.5 (2075–2098) will exhibit the highest annual ET₀ increase compared to the RCP4.5 scenario and the other periods, with a change rate equal to 11.85% compared to the historical period. Regarding spring and summer seasons, the change rates of ET₀ are expected to reach 10.44 and 18.07%, respectively, under RCP8.5 (2075–2098). This study shows that the model can be used to determine long-term trends in ET₀ patterns for diverse purposes, such as water resources planning, agricultural crop management and irrigation scheduling in the Cap-Bon region. Full article

This study estimates consumers’ willingness to pay (WTP) for sustainability turfgrass attributes such as low-input and stress-tolerance attributes, while considering potential trade-off relationships between aesthetic attributes and sustainability attributes. To address our objectives, our study conducts a choice experiment and estimates two mixed logit models. The first model includes low-input, winter kill, and shade-tolerance attributes as predictor variables, and the second model extends the first model by adding interaction terms between the aesthetic and sustainability attributes. Another choice experiment is conducted under water policies with various water rate increase and watering restriction scenarios. Results from the mixed logit models show that, overall, higher low-input cost reduction, less winter-damaged, and more shade-tolerant grasses are preferred, and that the direct effect of aesthetic attributes on consumers’ preferences is strong, but the indirect effects represented by the interaction terms are generally statistically insignificant. Our results indicate that consumers like to have a pretty lawn, but no strong consideration is given to the aesthetics of their lawn when selecting low-input and stress-tolerant turfgrasses. Our choice experiment under water policy scenarios suggests that water pricing is more effective than watering restriction in increasing consumer demand for water-conserving turfgrasses. Full article

To explore the effects of pulsed electric field treatment on the germination of Scutellaria baicalensis seeds and the growth of seedlings, this study used the response surface methodology to design the working parameters of the pulsed electric field and treated and cultured Scutellaria baicalensis seeds. The results showed that the pulsed electric field treatment was beneficial for the germination of Scutellaria baicalensis seeds, improving the metabolic activity and stress resistance of seedlings. When the pulsed electric field treatment’s parameters were 0.5 kV·cm⁻¹, 120 μs, and 99 pulses, the germination potential of seeds was significantly increased by 29.25% and the germination index significantly increased by 20.65%, compared to the control. From 5th to 15th day, the activities of SOD, POD, and α-amylase in the seedlings, and the contents of Pro, soluble sugars, and soluble proteins were significantly increased, compared to the control. This study provides a theoretical basis for improving the germination and seedling growth of medicinal herbs such as Scutellaria baicalensis and their practical application in production. Full article

The risks brought by microplastics (MPs) to agricultural soil structure and crop growth in the agricultural system are the focus of global debate. MPs enter the soil through various routes, such as through the use of agricultural mulch and atmospheric deposition. Here, we review the research on MP pollution in the soil during the last 30 years. This review focuses on (i) the sources, types, and distribution characteristics of MPs in agricultural soils; (ii) the migration and transformation of MPs and their interactions with microorganisms, organic matter, and contaminants in agricultural soils; and (iii) the effects of environmental factors on the composition and structure of MPs in agricultural soils. This review also proposes key directions for the future research and management of MPs in the agricultural soil. We aim to provide a theoretical basis for the fine management of agricultural farmland. Full article

With the advancement of machine vision technology, pig face recognition has garnered significant attention as a key component in the establishment of precision breeding models. In order to explore non-contact individual pig recognition, this study proposes a lightweight pig face feature learning method based on attention mechanism and two-stage transfer learning. Using a combined approach of online and offline data augmentation, both the self-collected dataset from Shanxi Agricultural University's grazing station and public datasets underwent enhancements in terms of quantity and quality. YOLOv8 was employed for feature extraction and fusion of pig face images. The Coordinate Attention (CA) module was integrated into the YOLOv8 model to enhance the extraction of critical pig face features. Fine-tuning of the feature network was conducted to establish a pig face feature learning model based on two-stage transfer learning. The YOLOv8 model achieved a mean average precision (mAP) of 97.73% for pig face feature learning, surpassing lightweight models such as EfficientDet, SDD, YOLOv5, YOLOv7-tiny, and swin_transformer by 0.32, 1.23, 1.56, 0.43 and 0.14 percentage points, respectively. The YOLOv8-CA model’s mAP reached 98.03%, a 0.3 percentage point improvement from before its addition. Furthermore, the mAP of the two-stage transfer learning-based pig face feature learning model was 95.73%, exceeding the backbone network and pre-trained weight models by 10.92 and 3.13 percentage points, respectively. The lightweight pig face feature learning method, based on attention mechanism and two-stage transfer learning, effectively captures unique pig features. This approach serves as a valuable reference for achieving non-contact individual pig recognition in precision breeding. Full article

In this article, the impact of the Russia–Ukraine conflict on food security in the Middle East and North Africa (MENA) region is analyzed. With Ukraine being recognized as one of the major global grain producers and exporters, the conflict is seen as posing a significant challenge to MENA countries, which are heavily dependent on grain imports from Ukraine. The importance of global linkages in food supply chains and their influence on regional food security is highlighted in this context. Utilizing secondary data from 2002 to 2021 obtained from the United Nations Food and Agriculture Organization (FAO), the study focuses on demography and food security, analyzing how these factors intertwine with grain export dynamics. The escalating hostilities have disrupted transportation routes, damaged infrastructure, and hindered logistics, resulting in substantial export volume reductions. Geopolitical tensions have exacerbated these effects, diminishing confidence among MENA grain importers. The study highlights how these disruptions have influenced global supply chains, prices, and agricultural product availability, with a specific focus on the MENA region’s challenges in food security, compounded by conflicts, climate change, and import dependence. A detailed demographic analysis reveals the impact of population changes on food demand and distribution, offering insights into how population growth and urbanization, alongside shifts in malnutrition and obesity rates, affect food security. The study concludes that the MENA region’s increasing reliance on food imports, coupled with climatic and political variabilities, underscores its growing vulnerability to global supply chain disruptions and the need for robust strategies to address these challenges. Full article

Legumes, due to their symbiosis with papillary bacteria, can receive nitrogen from the air. The remaining nutrients must be supplied in fertilisers, either soil or foliar. In the pot experiment, we recorded the responses of two soybean cultivars (Annushka, Pompei) to the foliar application of micronutrients (control, Zn, Fe, Cu, Mn, B, or Mo). The physiological properties were expressed as net photosynthetic rate (P_N), intercellular CO₂ concentration (C_i), transpiration rate (E), stomatal conductance (g_s), maximum quantum yield of photosystem II (F_v/F_m), maximum quantum yield of primary photochemistry (F_v/F₀), photosynthetic performance index (PI), and the development of soil plant analyses (SPAD), which were analysed. The effects of individual micronutrients on nodulation, plant growth, and condition were also investigated. Micronutrient fertilisation had a positive effect on plant fresh weight and no negative effect on plant condition. It was shown that elements such as B, Fe, and Mo had the most beneficial effect on nodulation compared to the control, regardless of the cultivar analysed. The application of single-component foliar fertilisers improved the physiological parameters of the plants. The relative chlorophyll content was most favourably affected by the application of Mn, B, and Mo in the Annushka cultivar, and Fe, Mn, and Mo in the Pompei cultivar. Similarly, in the case of chlorophyll fluorescence, the most stimulating effect was found for Mn and B, regardless of the cultivar. In the case of gas exchange, the application of Fe, Mo, and B for the Annushka cultivar and Cu for the Pompei cultivar had the most favourable effect on physiological measurements. The results obtained indicate that the foliar application of the evaluated micronutrients is justified in soybean cultivation and does not disturb the nodulation process. Full article

Wild pigs (Sus scrofa) cause damage to agricultural crops in their native range as well as in the portions of the globe where they have been introduced. In the US, states with the highest introduced wild pig populations are Alabama, Arkansas, California, Florida, Georgia, Louisiana, Mississippi, Missouri, North Carolina, South Carolina, and Texas. The present study summarizes the first survey-based effort to value the full extent of wild pig damage to producers of six crops in these eleven US states. The survey was distributed by the USDA National Agricultural Statistical Service in the summer of 2022 to a sample of 11,495 producers of corn (Zea mays), soybeans (Glycine max), wheat (Triticum spp.), rice (Oryza sativa), peanuts (Arachis hypogaea), and sorghum (Sorghum bicolor) in these 11 states. Our findings suggest that the economic burden of wild pigs on producers of these crops is substantial and not limited to the direct and most identifiable categories of crop damage (i.e., production value lost due to depredation, trampling and rooting). We estimate that the annual cost to producers of these six crops in the surveyed states in 2021 was almost USD 700 million. Full article

The soil microbiome plays an important role in maintaining soil health, plant productivity, and soil ecosystem services. Current molecular-based studies have shed light on the fact that the soil microbiome has been quantitatively underestimated. In addition to metagenomic studies, metaproteomics and metatranscriptomic studies that target the functional part of the microbiome are becoming more common. These are important for a better understanding of the functional role of the microbiome and for deciphering plant-microbe interactions. Free-living beneficial bacteria that promote plant growth by colonizing plant roots are called plant growth-promoting rhizobacteria (PGPRs). They exert their beneficial effects in different ways, either by facilitating the uptake of nutrients and synthesizing particular compounds for plants or by preventing and protecting plants from diseases. A better understanding of plant-microbe interactions in both natural and agroecosystems will offer us a biotechnological tool for managing soil fertility and obtaining a high-yield food production system. Full article

In recent years, the primary sector in Italy and elsewhere has been profoundly affected by climate change and a deep economic crisis, mainly linked to stagnating prices and rising production costs. Because of this situation, we are witnessing renewed interest in alternative agricultural productions, which are characterized by their resilience and sustainability, including medicinal and aromatic plants (MAPs). This sector is characterized by a certain heterogeneity due to the great variety of species and their wide range of uses. Although these characteristics contribute to the sector’s economic success, they also hinder its study due to commodity complexity and limited data availability. At the farm level, the situation is complicated by the fact that MAP cultivation is often embedded in complex cropping systems, and more rarely, is practiced exclusively or predominantly. In light of these considerations, we concentrated solely on the agricultural phase of the supply chain, using data available in the Farm Accountancy Data Network. We aimed to examine the main structural characteristics and economic outcomes of Italian farms that grow MAP, as well as the profitability of some of the species. To ensure accurate species classification, only MAPs exclusively designated for botanical use in the Italian National List were considered. The analysis of farm economic performance indicators (gross output, variable costs, gross margins, etc.) focused mainly on the species most represented in the sample: saffron, rosemary, lavender, oregano, and sage. The results indicate that the total gross output and gross margin show the best performance in the case of saffron (66,200 and 57,600 EUR/ha, respectively) and rosemary (27,500 and 22,000 EUR/ha, respectively). However, for saffron, the biggest cost concerns propagation (purchase of bulbs), amounting to 50% of the variable costs, whereas fertilization ones are particularly high for sage and rosemary. Full article

Despite the known benefits of integrated pest management, adoption in Australian broadacre crops has been slow, in part due to the lack of understanding about how pests and natural enemies interact. We use a previously developed process-based model to predict seasonal patterns in the population dynamics of a canola pest, the green peach aphid (Myzus persicae), and an associated common primary parasitic wasp (Diaeretiella rapae), found in this cropping landscape. The model predicted aphid population outbreaks in autumn and spring. Diaeretiella rapae was able to suppress these outbreaks, but only in scenarios with a sufficiently high number of female wasps in the field (a simulated aphid:wasp density ratio of at least 5:1 was required). Model simulations of aphid-specific foliar pesticide applications facilitated biological control. However, a broad-spectrum pesticide negated the control provided by D. rapae, in one case leading to a predicted 15% increase in aphid densities compared to simulations in which no pesticide was applied. Biological and chemical control could therefore be used in combination for the successful management of the aphid while conserving the wasp. This modelling framework provides a versatile tool for further exploring how chemical applications can impact pests and candidate species for biological control. Full article

When aiming at the problems such as missed detection or misdetection of recognizing green walnuts in the natural environment directly by using target detection algorithms, a method is proposed based on improved UNet3+ for green walnut image segmentation, which incorporates the channel and spatial attention mechanism CBAM (convolutional block attention module) and cross-entropy loss function (cross-entropy loss) into the UNet3+ network structure, and introduces the five-layer CBAM in the encoder module to construct the improved UNet3+ network model. The model consists of an encoder module (down-sampling), a decoder module (up-sampling) and a full-scale skip connection module, a full-scale feature supervision module, and a classification guidance module. After utilizing data-enhanced approaches to expand the green walnut dataset, the improved UNet3+ model was trained. The experimental findings demonstrate that the improved UNet3+ network model achieves 91.82% average precision, 96.00% recall rate, and 93.70% F1 score in the green walnut segmentation task; the addition of five-layer CBAM boosts the model segmentation precision rate by 3.11 percentage points. The method can precisely and successfully segment green walnuts, which can serve as a guide and research foundation for precisely identifying and localizing green walnuts and finishing the autonomous sorting for intelligent robots. Full article

To address the problem of a lack of accurate parameters in the discrete element simulation study of the machine-picked fresh tea leaf mechanized-sorting process, this study used machine-picked fresh tea leaves as the research object, established discrete element models of different fresh tea leaf components in EDEM software version 7.0.0. based on the bonded particle model using three-dimensional scanning inverse-modeling technology, and calibrated the simulation parameters through physical tests and virtual simulation tests. Firstly, the intrinsic parameters of machine-picked tea leaves were measured using physical tests; the physical-stacking tea leaf test was conducted using the cylinder lifting method, the tea leaf repose angle being 32.62° as measured from the stacking images using CAD. With the physical repose angle as the target value, the Plackeet–Burman test, the steepest-ascent test and the Box–Behnken optimization test were conducted in turn, and the results showed that the static friction coefficient between tea leaves, the rolling friction coefficient between tea leaves and the static friction coefficient between tea leaves and PVC have a major effect on the repose angle, and the optimal combination of the three significant parameters was determined. Finally, five simulations were conducted using the optimal combination of parameters, the relative error between the repose angle measured by the simulation test and the physical repose angle being just 0.28%. Moreover, the t-test obtained p > 0.05, indicating that there was no significant difference between the simulation test results and the physical test results. The results showed that the calibrated discrete element simulation parameters obtained could provide a reference for the discrete element simulation study of fresh tea leaves. Full article

On the basis of data collected from 1208 apple farmers in the provinces of Shaanxi and Gansu, this study utilizes the weighted-frequency method to investigate the priority sequence of farmers’ preferences in choosing fertilizer-reduction and efficiency-increasing technologies. Subsequently, ordered-probit models, a mediating-effect model, and a moderating-effect model are employed to empirically analyze the influence of capital endowment on farmers’ choices related to fertilizer-reduction and efficiency-increasing technologies and their underlying mechanisms. The study further examines how agricultural-technology extension moderates these mechanisms. The main findings are: (1) The priority sequence of farmers’ choices concerning fertilizer-reduction and efficiency-increasing technologies is as follows: organic fertilizer substitution, new efficient fertilizers, soil testing and formula fertilization, green manure cultivation, straw mulching, fertilizer-reduction application, and deep mechanical application. (2) Capital endowment significantly enhances farmers’ choices in fertilizer-reduction and efficiency-increasing technologies. (3) The mechanism analyses indicate that capital endowment can promote farmers’ choices in fertilizer-reduction and efficiency-increasing technologies by improving their information-acquisition capabilities. (4) Moderation effects reveal that agricultural-technology extension methods, such as technical training, financial subsidies, and government publicity, significantly and positively moderate the relationship between information-acquisition capabilities and farmers’ choices in fertilizer-reduction and efficiency-increasing technologies. The moderating effects of educational attainment and generational differences on different agricultural-technology extension methods are heterogeneous. Technical training, financial subsidies, and government publicity can effectively enhance the positive impact of information-acquisition capabilities on farmers with a higher educational attainment. Financial subsidies can effectively strengthen the positive impact of information-acquisition capabilities on the older generation of farmers. Therefore, it is recommended to prioritize the accumulation of farmers’ capital endowment, improve their information-acquisition capabilities, and intensify agricultural-technology extension efforts, especially taking into account farmers’ educational attainment and generational differences. Full article

Weeds in paddy fields can seriously reduce rice yield. An intra-row weeding device with double-layer elastic rods was designed, considering the differences in mechanical properties between rice and weeds, which can press weeds into the soil and avoid damaging rice. The elastic force of the elastic rods can be adjusted by changing the position of the regulating mechanism to adapt to different weeding conditions. A measurement experiment was conducted to determine the variation rule of elastic force. The quadratic orthogonal rotation combination discrete element simulation experiment, which used weeding depth and weeding speed as experimental factors, and the amount of soil disturbance and the force of the inner and outer elastic rod in the horizontal and vertical directions as experimental indicators, was conducted to study the interaction between the weeding device and the soil. The optimal weeding parameters were obtained: the weeding depth was 15 mm, the weeding speed was 0.9 m/s. The field experiment, which used the various parameters of the weeding device as experimental factors and the weeding rate and damaging seedling rate as experimental indicators, was conducted to determine the weeding effect. The experimental results showed that the optimal position of the regulating mechanism was 270 mm, with a weeding rate of 80.65% and a damaging seedling rate of 3.36%. The weeding rate can be increased by at least 11.18% by adjusting the regulating mechanism to a suitable position under the same weeding conditions. This study can provide a reference for research on weeding machinery for organic rice. Full article

Temperature has a significant impact on the production of edible mushrooms. The industrial production of edible mushrooms is committed to accurately maintaining the temperature inside the mushroom room within a certain range to achieve quality and efficiency improvement. However, current environmental regulation methods have problems such as lagging regulation and a large range of temperature fluctuations. There is an urgent need to accurately predict the temperature of mushroom houses in the future period to take measures in advance. Therefore, this article proposes a temperature prediction model for mushroom houses using a data–physical hybrid method. Firstly, the Boruta-SHAP algorithm was used to screen out the key influencing factors on the temperature of the mushroom room. Subsequently, the indoor temperature was decomposed using the optimized variational modal decomposition. Then, the gated recurrent unit neural network and attention mechanism were used to predict each modal component, and the mushroom house heat balance equation was incorporated into the model’s loss function. Finally, the predicted values of each component were accumulated to obtain the final result. The results demonstrated that integrating a simplified physical model into the predictive model based on data decomposition led to a 12.50% reduction in the RMSE of the model’s predictions compared to a purely data-driven model. The model proposed in this article exhibited good predictive performance in small datasets, reducing the time required for data collection in modeling. Full article

Mussel shells, with their calcium carbonate content, serve as a natural pH buffer, aiding in neutralizing acidic soils and, consequently, enhancing nutrient availability for plants. The aim of this study was to evaluate the effect of treating soils with mussel shells as a soil pH amendment on the agronomic characteristics and productivity of Rosmarinus officinalis. A pot experiment was set up for two growing years. The treatments were amended using different doses of mussel shells. Overall, the treatments were the following: C: unamended soil (control); T1: 0.1%; T2: 0.3%; T3: 0.5%; T4: 1%; T5: 3%; T6: 6%. Plant height was higher in pots amended with 6% mussel shells and reached the value of 32.2 cm in the first year and 51 cm in the second. The application of mussel shells increased the branch length by 53.4–58.7% and the number of branches per plant by 61.3–62% in T6 compared to the control. The total yield of fresh and dry weight in the 1st and 2nd year was ordered as follows: T6 > T5 > T4 > T3 > T2 > T1 > C. In conclusion, while the established optimal quantity for neutralizing soil pH is 300 g of mussel shells per 10 kg of soil, it has been observed that a ratio of 600 g of mussel shells proves more effective in terms of both the productivity and agronomic characteristics of rosemary. Full article

Parsley is an herb/vegetable rich in nutritional compounds such as carbohydrates, vitamins, protein, crude fiber, minerals (especially potassium), phosphorus, magnesium, calcium, iron, and essential oils. Limited information is available in the literature on the quality of parsley roots depending on the cultivation technology used in the form of macronutrients and micronutrients, preparations to stimulate plant growth and development, as well as plant-protection products. A three-year study was undertaken to determine the effect of applying mineral fertilization with nitrogen, including magnesium on the nutritional value of parsley roots in terms of the content of ascorbic acid, total and reducing sugars, and minerals: (total N, K, Mg, Ca). The research material was the root of Petroselinum crispum ssp. tuberosum from an experiment where nitrogen was applied in soil at (0, 40, 80, 120 kg N ha⁻¹) and magnesium at (0, 30 kg MgO ha⁻¹). Nitrogen fertilization increased the nutritional value in terms of total and reducing sugars, as well as total N and Ca content. Applied magnesium fertilization caused a significant increase in the content of all tested nutrients. The most total sugars (127.7 g kg⁻¹ f. m.), reducing sugars (16.8 g kg⁻¹ f. m.), and total N (12.13 g kg⁻¹ d. m.) were accumulated by roots from the object where nitrogen was applied at a maximum rate of 120 kg N ha⁻¹, including magnesium. On the other hand, for the content of K (19.09 g kg⁻¹ d. m.) in the roots, a dose of 80 N ha⁻¹ was sufficient. For ascorbic acid (263.2 g kg⁻¹ f. m.) and Ca (0.461 g kg⁻¹ d. m.), a dose of 40 kg N ha⁻¹ with a constant fertilization of 30 kg MgO ha⁻¹ was sufficient. When applying high doses of nitrogen, lower doses of magnesium are recommended. This is sufficient due to the high nutritional value of parsley roots. Due to the worsening magnesium deficiency in soils in recent years, the use of this nutrient in the cultivation of root vegetables is as justified and timely as possible. Quality-assessment studies of root vegetables should be continued with higher amounts of magnesium fertilization. Different ways of applying magnesium in parsley cultivation should also be tested. Full article

Apples, as the fourth-largest globally produced fruit, play a crucial role in modern agriculture. However, accurately identifying apple diseases remains a significant challenge as failure in this regard leads to economic losses and poses threats to food safety. With the rapid development of artificial intelligence, advanced deep learning methods such as convolutional neural networks (CNNs) and Transformer-based technologies have made notable achievements in the agricultural field. In this study, we propose a dual-branch model named DBCoST, integrating CNN and Swin Transformer. CNNs focus on extracting local information, while Transformers are known for their ability to capture global information. The model aims to fully leverage the advantages of both in extracting local and global information. Additionally, we introduce the feature fusion module (FFM), which comprises a residual module and an enhanced Squeeze-and-Excitation (SE) attention mechanism, for more effective fusion and retention of both local and global information. In the natural environment, there are various sources of noise, such as the overlapping of apple branches and leaves, as well as the presence of fruits, which increase the complexity of accurately identifying diseases on apple leaves. This unique challenge provides a robust experimental foundation for validating the performance of our model. We comprehensively evaluate our model by conducting comparative experiments with other classification models under identical conditions. The experimental results demonstrate that our model outperforms other models across various metrics, including accuracy, recall, precision, and F1 score, achieving values of 97.32%, 97.33%, 97.40%, and 97.36%, respectively. Furthermore, detailed comparisons of our model’s accuracy across different diseases reveal accuracy rates exceeding 96% for each disease. In summary, our model performs better overall, achieving balanced accuracy across different apple leaf diseases. Full article

A clear understanding of the number of pigs plays a crucial role in breeding management. Computer vision technology possesses several advantages, as it is harmless and labour-saving compared to traditional counting methods. Nevertheless, the existing methods still face some challenges, such as: (1) the lack of a substantial high-precision pig-counting dataset; (2) creating a dataset for instance segmentation can be time-consuming and labor-intensive; (3) interactive occlusion and overlapping always lead to incorrect recognition of pigs; (4) existing methods for counting such as object detection have limited accuracy. To address the issues of dataset scarcity and labor-intensive manual labeling, we make a semi-auto instance labeling tool (SAI) to help us to produce a high-precision pig counting dataset named Count1200 including 1220 images and 25,762 instances. The speed at which we make labels far exceeds the speed of manual annotation. A concise and efficient instance segmentation model built upon several novel modules, referred to as the Instances Counting Network (ICNet), is proposed in this paper for pig counting. ICNet is a dual-branch model ingeniously formed of a combination of several layers, which is named the Parallel Deformable Convolutions Layer (PDCL), which is trained from scratch and primarily composed of a couple of parallel deformable convolution blocks (PDCBs). We effectively leverage the characteristic of modeling long-range sequences to build our basic block and compute layer. Along with the benefits of a large effective receptive field, PDCL achieves a better performance for multi-scale objects. In the trade-off between computational resources and performance, ICNet demonstrates excellent performance and surpasses other models in Count1200, $AP$ of 71.4% and $A{P}^{50}$ of 95.7% are obtained in our experiments. This work provides inspiration for the rapid creation of high-precision datasets and proposes an accurate approach to pig counting. Full article

This paper proposes a lightweight and efficient mango detection model named Light-YOLO based on the Darknet53 structure, aiming to rapidly and accurately detect mango fruits in natural environments, effectively mitigating instances of false or missed detection. We incorporate the bidirectional connection module and skip connection module into the Darknet53 structure and compressed the number of channels of the neck, which minimizes the number of parameters and FLOPs. Moreover, we integrate structural heavy parameter technology into C2f, redesign the Bottleneck based on the principles of the residual structure, and introduce an EMA attention mechanism to amplify the network’s emphasis on pivotal features. Lastly, the Downsampling Block within the backbone network is modified, transitioning it from the CBS Block to a Multi-branch–Large-Kernel Downsampling Block. This modification aims to enhance the network’s receptive field, thereby further improving its detection performance. Based on the experimental results, it achieves a noteworthy mAP of 64.0% and an impressive mAP0.5 of 96.1% on the ACFR Mango dataset with parameters and FLOPs at only 1.96 M and 3.65 G. In comparison to advanced target detection models like YOLOv5, YOLOv6, YOLOv7, and YOLOv8, it achieves improved detection outcomes while utilizing fewer parameters and FLOPs. Full article

Copper is a substance that has been used in plant protection for years. Currently, however, more and more attention is being paid to the need to limit the amount of it that ends up in the natural environment. At the same time, it is necessary to partially replace synthetic fungicides with alternative preparations. It is therefore worth looking for forms of copper that will contain a smaller amount of the mentioned ingredient while being highly effective. This experiment assessed the effect of selected preparations on the development of mycelium of pathogens of the Fusarium genus and the germination parameters of winter wheat. The efficacy of copper lignosulfonate and copper heptagluconate in seed treatment was tested, comparing them to copper oxychloride, copper hydroxide, and tebuconazole. The obtained results indicate that the use of copper lignosulfonate and copper heptagluconate allows for the effective limitation of the development of the tested pathogens (mycelium development was inhibited by up to 100%). Most of the preparations had no effect on the energy and germination capacity of winter wheat (only in one combination were the values lower than 90%). The use of preparations containing reduced doses of copper is an effective solution when applied as seed dressings. Full article

Seed treatment is a powerful technique for adding beneficial ingredients to plants during the seed preparation process. Biopolymers as drying agents and delivery systems in seed treatments were investigated for their biocompatibility with blastospores of the nematophagous fungus Pochonia chlamydosporia. To produce a novel seed treatment for the cover crop Phacelia tanacetifolia, xanthan gum TG and gellan gum were the most promising biopolymers in combination with potato starch and bentonite. The seed treatment process as well as the drying process were specially designed to be scalable, which make it suitable for applying the developed seed treatment in agriculture. Application of gellan gum in seed treatments led to 6.3% ± 1.6% of vital blastospores per seed compared to 3.8% ± 0.3% of vital blastospores when applying xanthan gum. Storage tests for seed treatments with 0.5% gellan gum indicated a higher stability at 4 °C compared to storage at 21 °C. After 42 days of storage at 4 °C, 54.1% ± 15.1% of the applied blastospores were viable compared to 0.3% ± 0.8% at 21 °C. This novel seed treatment application with P. chlamydosporia blastospores includes the seed treatment procedure, drying process, and storage tests and can easily be upscaled for application in agriculture. Full article

Aiming to solve the problems of excessive straw residue and large soil loss in the seeding belt of the straw row-sorting operation when the full volume of straw is crushed and returned to the field in the northeastern region of China, an active spiral pushing straw row-sorting device (ASPSRD) was designed in this paper. The straw on the surface of the land is collected and stirred by the high-speed rotating spiral-notched blades in the device and pushed to the non-seeding area for the purpose of cleaning the seeding belt. The parameters of key components were determined through theoretical analysis. The orthogonal combination test method of four factors and three levels was adopted. The EDEM discrete element simulation experiment was carried out by selecting the straw mulching quantity (SMQ), the rotating speed of the active rotating shaft (RSARS), the forward speed of the tractor (FST), and the notch width of the spiral notch blade (NWSNB) as the test factors, and the straw-cleaning rate (SCR) and soil loss rate (SLR) as the test indexes. Through range analysis, the parameters were optimized, and the optimal operation parameters of the straw row-sorting device were determined as follows: SMQ was 1.8 kg/m², RSARS was 120 rpm, FST was 4 m/s, and NWSNB was 9 mm. The operational performance of the device was verified by field experiments. The results of the test showed that after the device was operated with the optimal combination of parameters, the SCR of the 20 cm wide seeding belt was 95.32%, and the SLR was 7.12%, which met the agronomic and technical requirements of corn no-tillage sowing operation in Northeast China. This study can provide a reference for the design of a straw row-sorting device of a no-tillage machine. Full article

The autonecrotic tomato line V20368 (working code IGSV) spontaneously develops necrotic lesions with acropetal progression in response to an increase in temperature and light irradiation. The process is associated with the interaction between tomato and Cladosporium fulvum, the fungal agent of leaf mold. The contemporary presence of an in-house allele encoding the Rcr3^lyc protein and the resistance gene Cf-2^pim (from Solanum pimpinellifolium) causes auto-necrosis on the leaves even in the absence of the pathogen (hybrid necrosis). The aim of the work was (i) to examine the potential value of the necrotic genotype as a model system for setting up theoretical guidance for monitoring the phytosanitary status of tomato plants and (ii) to develop a predictive model for the early detection of pathogens (or other stresses) in the tomato or other species. Eighteen IGSV tomato individuals at the 4–5th true-leaf stage were grown in three rows (six plants per row) considered to be replicates. The healthy control was the F1 hybrid Elisir (Olter). A second mutant line (SA410) deriving from a cross between the necrotic mutant and a mutant line of the lutescent (l) gene was used during foliar analysis via microspectrometry. The leaves of the mutants and normal plants were monitored through a portable VIS/NIR spectrometer SCIO^TM (Consumer Physics, Tel Aviv, Israel) covering a spectral range between 740 and 1070 nm. Two months after the transplant, the acropetal progression of the autonecrosis showed three symptomatic areas (basal, median, apical) on each IGSV plant: necrotic, partially damaged, and green, respectively. Significantly lower chlorophyll content was found in the basal and median areas of IGSV compared with the control (Elisir). A supervised classification/modelling method (SIMCA) was used. Applying the SIMCA model to the dataset of 162 tomato samples led to the identification of the boundary between the healthy and damaged samples (translational critical distance). Two 10 nm wavelength ranges centred at 865 nm and 1055 nm exhibited a stronger link between symptomatology and spectral reflectance. Studies on specific highly informative mutants of the type described may allow for the development of predictive models for the early detection of pathogens (or other stresses) via proximal sensing. Full article

Food security has become a concerning issue because of global climate change and increasing populations. Agricultural production is considered one of the key factors that affects food security. The changing climate has negatively affected agricultural production, which accelerates food shortages. The supply of agricultural commodities can be heavily influenced by climate change, which leads to climate-induced agricultural productivity shocks impacting crop prices. This paper systematically reviews publications over the past ten years on the factors affecting the prices of a wide range of crops across the globe. This review presents a critical view of these factors in the context of climate change. This paper applies a systematic approach by determining the appropriate works to review with defined inclusion criteria. From this, groups of key factors affecting crop prices are found. This study finds evidence that crop prices have been both positively and negatively affected by a range of factors such as elements of climate change, biofuel, and economic factors. However, the general trend is towards increasing crop prices due to deceasing yields over time. This is the first systematic literature review which provides a comprehensive view of the factors affecting the prices of crops across the world under climate change. Full article

Agriculture is undergoing a profound change related to Agriculture 4.0 development and Precision Agriculture adoption, which is occurring at a slower pace than expected despite the abundant literature on the factors explaining this adoption. This work explores the factors related to agricultural Operations Management, farmer behavior, and the farmer mental model, topics little explored in the literature, by applying the Theory of Planned Behavior. Considering the exploratory nature of this work, an exploratory multi-method is applied, consisting of expert interviews, case studies, and modeling. This study’s contributions are a list of factors that can affect this adoption, which complements previous studies, theoretical propositions on the relationships between these factors and this adoption, and a model of irrigation Operations Management built based on these factors and these propositions. This model provides a theoretical framework to study the identified factors, the relationships between them, the theoretical propositions, and the adoption of Precision Agriculture. Furthermore, the results of case studies allow us to explore the relationships between adoption, educational level, and training. The identified factors and the model contribute to broadening the understanding of Precision Agriculture adoption, adding Operations Management and the farmer mental model to previous studies. A future research agenda is formulated to direct future studies. Full article

The ZRT/IRT-like proteins (ZIPs) play critical roles in the absorption, transport, and intracellular balance of metal ions essential for various physiological processes in plants. However, little is known about the pan-genomic characteristics and properties of ZIP genes in cucumber (Cucumis sativus L.). In this study, we identified 10 CsZIP genes from the pan-genome of 13 C. sativus accessions. Among them, only CsZIP10 showed no variation in protein sequence length. We analyzed the gene structure, conserved domains, promoter cis-elements, and phylogenetic relationships of these 10 CsZIP genes derived from “9930”. Based on phylogenetic analysis, the CsZIP genes were classified into three branches. Amino acid sequence comparison revealed the presence of conserved histidine residues in the ZIP proteins. Analysis of promoter cis-elements showed that most promoters contained elements responsive to plant hormones. Expression profiling in different tissues showed that most CsZIP genes were expressed at relatively low levels in C. sativus leaves, stems, and tendrils, and CsZIP7 and CsZIP10 were specifically expressed in roots, indicating their potential involvement in the absorption and transport of metal ions. Transcriptomic data indicated that these 10 ZIP genes displayed responses to both downy mildew and powdery mildew, and CsZIP1 was significantly downregulated after both salt and heat treatments. In conclusion, this study deepens our understanding of the ZIP gene family and enhances our knowledge of the biological functions of CsZIP genes in C. sativus. Full article