Agronomy, Volume 14, Issue 5 (May 2024) – 195 articles

The rice–crayfish rotation system (RCR), originating in the Jianghan Plain, is developing rapidly in various regions of China and has been characterized by unbalanced regional development, which has also led to widespread concerns and discussion on its environmental impacts and sustainability. This study selects representative RCR production areas in the Jianghan Plain, including Jianli, Qianjiang, Shishou, Shayang, Gong’an and Honghu, to analyze resource inputs, resource utilization efficiency, environmental impacts and sustainability by employing the emergy analysis method. Our analysis of Jianli, Honghu, Qianjiang, Gong’an, Shishou and Shayang reports total emergy inputs ranging from 6.46 × 10¹⁶ to 8.25 × 10¹⁶, with renewable rates between 78.38% and 84.34%. Shishou leads in the unit emergy value (5.58 × 10⁻¹) and the emergy yield ratio (5.30). The sustainability evaluation finds that the environmental loading ratio is from 0.19 to 0.28 and the emergy index for sustainable development varies between 1.27 and 3.00. This analysis indicates that the southern regions have higher inputs and efficiency, with southeastern areas showing lower environmental impact and higher sustainability. We also underscore the impact of non-renewable resources on environmental outcomes and sustainability, suggesting tailored development strategies for the rice–crayfish rotation system’s optimization and sustainable growth. Full article

The greenhouse environment plays a crucial role in providing favorable conditions for crop growth, significantly improving their quality and yield. Accurate prediction of greenhouse environmental factors is essential for their effective control. Although artificial intelligence technologies for predicting greenhouse environments have been researched recently, there are limitations in applying these to general greenhouse environments due to computing resources or issues with interpretability. Moreover, research on environmental prediction models specifically for melon greenhouses is also lacking. In this study, machine learning models based on MLR (Multiple Linear Regression), SVM (Support Vector Machine), ANN (Artificial Neural Network), and XGBoost were developed to predict the internal temperature, relative humidity, and CO₂ conditions of melon greenhouses 30 min in advance. The XGBoost model demonstrated high accuracy and stability, with an R² value of up to 0.9929 and an RPD (Residual Predictive Deviation) of 11.8464. Furthermore, the analysis of the XGBoost model’s feature importance and decision trees revealed that the model learned the complex relationships and impacts among greenhouse environmental factors. In conclusion, this study successfully developed a predictive model for a greenhouse environment for melon cultivation. The model developed in this study can facilitate an understanding and efficient management of the greenhouse environment, contributing to improvements in crop yield and quality. Full article

Plant height is regarded as a key indicator that is crucial for assessing the crop growth status and predicting yield. In this study, an advanced method based on solid-state LiDAR technology is proposed, which is specifically designed to accurately capture the phenotypic characteristics of plant height during the maize growth cycle. By segmenting the scanned point cloud of maize, detailed point cloud data of a single maize plant were successfully extracted, from which stem information was accurately measured to obtain accurate plant height information. In this study, we will concentrate on the analysis of individual maize plants. Leveraging the advantages of solid-state LiDAR technology in precisely capturing phenotypic information, the data processing approach for individual maize plants, as compared to an entire maize community, will better restore the maize’s original growth patterns. This will enable the acquisition of more accurate maize plant height information and more clearly demonstrate the potential of solid-state LiDAR in capturing detailed phenotypic information. To enhance the universality of the research findings, this study meticulously selected key growth stages of maize for data validation and comparison, encompassing the tasseling, silking, and maturity phases. At these crucial stages, 20 maize plants at the tasseling stage, 40 at the flowering stage, and 40 at the maturity stage were randomly selected, totaling 100 samples for analysis. Each sample not only included actual measurement values but also included plant height information extracted using point cloud technology. The observation period was set from 20 June to 20 September 2021. This period encompasses the three key growth stages of maize described above, and each growth stage included one round of data collection, with three rounds of data collection each, each spaced about a week apart, for a total of nine data collections. To ensure the accuracy and reliability of the data, all collections were performed at noon when the natural wind speed was controlled within the range of 0 to 1.5 m/s and the weather was clear. The findings demonstrate that the root mean square error (RMSE) of the maize plant height data, procured through LiDAR technology, stands at 1.27 cm, the mean absolute percentage error (MAPE) hovers around 0.77%, and the peak R² value attained is 0.99. These metrics collectively attest to the method’s ongoing high efficiency and precision in capturing the plant height information. In the comparative study of different stem growth stages, especially at the maturity stage, the MAPE of the plant height was reduced to 0.57%, which is a significant improvement compared to the performance at the nodulation and sprouting stage. These results effectively demonstrate that the maize phenotypic information extraction method based on solid-state LiDAR technology is not only highly accurate and effective but is also effective on individual plants, which provides a reliable reference for applying the technique to a wider range of plant populations and extending it to the whole farmland. Full article

Gentiana lutea L. subsp. aurantiaca M. Lainz is a plant endemic to the north-western mountainous areas of the Iberian Peninsula. Its roots are widely used mainly because of the high content of bitter compounds. The occurrence of these valuable bitter compounds in the roots is rather inhomogeneous, resulting in fluctuating root quality. Methanolic extracts obtained from different parts and tissues of wild and cultivated gentian, in and out of its natural environment, were analysed using HPLC chromatography to investigate the variation in the concentration of amarogentin, gentiopicroside, sweroside and swertiamarin. The distribution patterns of these compounds in the different analysed fractions showed that the concentration of bitter compounds varies significantly. Amarogentin is much more highly concentrated in the secondary roots, and all of the analysed compounds were found in a significantly higher content in the root cortex than in the vascular tissues. Roots cultivated in the natural habitat showed much higher concentrations in amarogentin and more biomass, while in those cultivated out of the natural environment, sweroside concentration was higher. These results allow us to understand that, when cultivated, the variability in the concentration of the different bitter compounds is linked with the edaphoclimatic conditions, but more importantly that it is linked with the dominating kind of tissues and the root system structure, especially when analysing the content of amarogentin and sweroside. The selection of plants with an optimal root system structure for breeding may increase the yield in bitter compounds and contribute to developing the commercial cultivation of this protected plant. Full article

Soil organic matter (SOM) is important for the global carbon cycle, and hyperspectral remote sensing has proven to be a promising method for fast SOM content estimation. However, because of the neglect of the spectral response of soil physical properties, the accuracy and spatiotemporal transferability of the SOM prediction model are poor. This study aims to improve the spatiotemporal transferability of the SOM prediction model by alleviating the coupling effect of soil physical properties on spectra. Based on satellite hyperspectral images and soil physical variables, including soil moisture (SM), soil surface roughness (root-mean-square height, RMSH), and soil bulk weight (SBW), a soil spectral correction model was established based on the information unmixing method. Two important grain-producing areas in Northeast China were selected as study areas to verify the performance and transferability of the spectral correction model and SOM content prediction model. The results showed that soil spectral corrections based on fourth-order polynomials and the XG-Boost algorithm had excellent accuracy and generalization ability, with residual predictive deviations (RPDs) exceeding 1.4 in almost all the bands. In addition, when the soil spectral correction strategy was adopted, the accuracy of the SOM prediction model and the generalization ability after the model migration were significantly improved. The SOM prediction accuracy based on the XG-Boost-corrected spectrum was the highest, with a coefficient of determination (R²) of 0.76, a root-mean-square error (RMSE) of 5.74 g/kg, and an RPD of 1.68. The prediction accuracy, R² value, RMSE, and RPD of the model after the migration were 0.72, 6.71 g/kg, and 1.53, respectively. Compared with the direct migration prediction of the model, adopting the soil spectral correction model based on fourth-order polynomials and XG-Boost reduced the RMSE of the SOM prediction results by 57.90% and 60.27%, respectively. This performance comparison highlighted the advantages for considering soil physical properties in regional-scale SOM predictions. Full article

Medicinal and aromatic plants are a consistent component of the biodiversity heritage in numerous countries worldwide. Origanum dictamnus L. (Lamiaceae family), also known as Dittany, an endemic plant of the Greek island of Crete, has been widely used as traditional medicine since antiquity, all over Europe. The aim of the present review is to provide a thorough and detailed account of Dittany in antiquity, the plant’s physical characteristics and ecology, and its cultivation methods, as well as its chemical components, biological properties, and pharmacological studies. The information is presented and analyzed in a critical manner. A total of 86 research studies were systematically reviewed based on the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. The findings indicate that Dittany is one of the most important medicinal and aromatic plants, with many uses not only in pharmacology but also in gastronomy. While a large body of literature exists regarding the application of essential oils, the number of publications concerning the plant’s cultivation is rather small. Therefore, the main focus of this review is on the cultivation methods and the significance of cultivating and employing Dittany in Greece and the wider Mediterranean region in the future. Further research on this plant species is warranted since it has significant medicinal, economic, and environmental value. Full article

Enhancing yield and achieving environmental goals represent challenges for the future of agriculture. Rational nitrogen (N) management is one of the most promising ways to meet this challenge. However, complicated nitrogen management strategies and considerable input requirements still exist in rice–ratoon rice production. To address this issue, field experiments were conducted with two main high-yield rice crop genotypes and five fertilization treatments at six sites in Southwest China from 2018 to 2020. The results showed the following: (1) the yield of the main rice crop was extremely significantly affected by the year, location, and fertilization, but not by genotype; (2) the yield of the ratoon rice was extremely significantly affected by year, genotype, location, and fertilization; and (3) the total plant N content (TPN) and leaf SPAD value at the full heading stage of the main crop were significantly positively correlated with the total soil N content (TSN) and soil available N (SAN) content of the basic soil. The highly efficient N application rate of grain- and bud-promoting fertilizer for ratoon rice was 60–120 kg ha⁻¹. The TSN, SAN, TPN, and SPAD values higher than 0.247 kg N kg⁻¹, 298 mg N kg⁻¹, 2.159 kg N kg⁻¹, and 49.94 were, respectively, considered the reference values when not applying grain- and bud-promoting fertilizer. A regression equation was established to predict the amount of high-efficiency grain- and bud-promoting fertilizer based on the TSN and SPAD. Overall, the yield of rice–ratoon rice was significantly affected by year, genotype, location, fertilization, and their interactions. The use of the predicted grain- and bud-promoting fertilizer regression equation can achieve high yields under simplified and reduced N input practices in the rice–ratoon rice systems. Full article

Small-scale cultivation of irrigated fodder is emerging as a vital production system in mixed farming communities. Efficient water management plays a key role in enhancing forage production, especially in the face of changing climate. A field-scale experimental study was conducted in Robit Bata kebele, Ethiopia, with the following objectives: (1) to examine the effects of conventional farmers’ irrigation scheduling versus climate-based irrigation scheduling; and (2) to assess the influence of water-lifting technologies (manual pulley and solar Majipump) on dry matter yield (DMY), water productivity (WP), irrigation labor productivity (ILP), and water productivity in terms of crude protein and metabolizable energy (WP.CP and WP.ME) of Napier grass. The experiment used 10 farmers’ plots each with a size of 100 m². Half of the plots were treated using farmers’ scheduling while the other half were treated using climate-based irrigation scheduling. Monitoring of irrigation water use and crop yield took place over two irrigation seasons from November 2020 to June 2021. Results showed there was an interaction effect of irrigation management (p = 0.019) and water-lifting technologies (p = 0.016) with season on DMY. The highest DMY occurred in the first irrigation season with climate-based scheduling and solar Majipump use. The interaction effect of irrigation management and season affected WP (p = 0.047). Climate-based scheduling had a higher WP in the first season, while farmers’ scheduling had a higher WP during the second season. On average, the solar Majipump outperformed the pulley, achieving 5 kg m⁻³ WP compared to the pulley’s 4 kg m⁻³ (p = 0.018). Emphasizing the seasonal impact, it is recommended to promote full irrigation (climate-based) in the first season for maximum yield and WP. Conversely, in the second season, advocating only deficit irrigation is advised due to water scarcity and sustainability concerns. Statistical parity in DMY and lower WP with full irrigation in the second season supports this recommendation, addressing the challenge of optimizing water use in the context of a changing climate and ensuring sustainable smallholder agriculture practices. Therefore, implementing appropriate irrigation management alongside efficient water-lifting technologies holds the potential to enhance fodder productivity and bolster smallholder farmers’ livelihoods. Future research should explore the comparative benefits of irrigated fodder versus other crops and the overall advantages of investing in irrigated fodder over vegetables. Full article

The goal of this study was to understand the effect of light intensity on the chlorogenic acid content and biosynthesis-related gene expression in Marsdenia tenacissima. In this study, M. tenacissima plants were treated with different light intensities; the chlorogenic acid content was determined by high-performance liquid chromatography; and transcriptome sequencing was performed. The amount of chlorogenic acid in the control was the highest and differed significantly from that under three different shading treatments. With a decrease in light intensity, the content of chlorogenic acid also showed a decreasing trend. A total of 1149 differentially expressed genes were identified by transcriptome sequencing, and most of the genes were down-regulated under the 90% shading treatment. A weighted gene co-expression network analysis identified the differentially expressed genes associated with light-induced chlorogenic acid biosynthesis. The different shading treatments down-regulated the expression of the chlorogenic acid biosynthesis pathway structural genes (HCTs). The MIKC family genes were the main transcription factors regulating light-induced chlorogenic acid biosynthesis, but the MYB and SBP family genes were also involved. In summary, combined physiological and transcriptome analysis, candidate structural genes, and transcription factors in the biosynthesis pathway of chlorogenic acid were identified in M. tenacissima. Full article

Bacteria belonging to the genus Micromonospora are recognized as microorganisms with the potential to be used in biotechnology processes, given their beneficial influence on plant growth and the biocontrol of phytopathogens. In this study, nineteen Micromonospora isolates originating from the root nodules of white clover plants were taxonomically assigned based on the phylogenetic analysis of the 16S rRNA gene and four housekeeping genes. The antifungal properties of the bacteria against phytopathogenic Botrytis cinerea, Fusarium oxysporum, Fusarium equiseti, Sclerotinia sclerotiorum, and Verticillium albo-atrum were tested with the agar plug test and the dual culture test. The ability to produce various metallophores was determined with the agar plug diffusion test on modified chrome azurol S (CAS) agar medium. International Streptomyces Project-2 medium (ISP2) broth amended with 0.2% L-tryptophan was used to indicate the bacterial ability to produce auxins. The strains belonging to M. tulbaghiae, M. inaquosa, and M. violae showed in vitro potential as antimicrobial agents against the tested fungi. M. inaquosa strain 152, M. violae strain 126, M. violae strain 66, and M. violae strain 45 were recognized as the most efficient metallophore producers. M. alfalfae strain 55 and M. lupini strain 5052 were identified as the most promising auxin compound producers and, therefore, show potential as plant-growth-promoting bacteria. Full article

To ascertain the effect of low-light stress (80 μmol·m⁻²·s⁻¹) on cucumbers, we report on improving and breeding low-light-tolerant varieties by mining genes related to low-light tolerance. In this study, the quantitative trait locus (QTL) mapping of cucumber plant height and internode length under low-light stress was conducted using the F₂ population, employing specific-length amplified fragment sequencing (SLAF-seq) and phenotypic analysis. A genetic map with a total length of 1114.29 c M was constructed from 1,076,599 SNPs, and 2233 single-nucleotide polymorphism (SNP) markers were distributed on seven linked groups, with an average map distance of 0.50 c M. Two QTLs related to plant height, CsPlH5.1 and CsPlH6.1, were detected on Chr.5 and Chr.6, with a cumulative contribution rate of 16.33%. The contribution rate (PVE), max LOD value, additive effect (ADD), and dominant effect (DOM) of CsPlH5.1 were 9.446%, 4.013, 1.005, and 0.563, respectively. CsPlH5.1 was located between 4,812,907 and 5,159,042 in the Gy14_V2.0 genome of cucumber, with a genetic distance of 0.32 Mb; the interval contained 41 candidate genes, and CsPlH6.1 was found to be located between Marker537985 (171.10 c M) and Marker 537984 (171.55 c M), a range containing only one candidate gene. A total of 42 candidate genes related to photosynthesis, chloroplast development, abiotic stress, and plant growth were found in the location range associated with plant height. Simultaneously, a QTL (Csnd2_NdL6.1) for the second internode length was detected, and the max LOD, ADD, and DOM values were 5.689, 0.384, and −0.19, respectively. Csnd2_NdL6.1 was located between 29,572,188 and 29,604,215, with 0.03 Mb on Chr. 6 including seven candidate genes. The molecular function of the CsGy6G032300 gene is involved with the binding of calcium ions, which may be related to the elongation and growth of plants; however, the population needs to be further expanded for acceptable localization verification. The results of this study provide a preliminary basis for the mining of essential genes of cucumber’s low-light tolerance and identifying low-light-tolerance genes. Full article

To clarify the migration and accumulation of nitrogen (N), magnesium (Mg), calcium (Ca), and potassium (K) in soil profiles of plastic-greenhouse vegetable fields with cultivation years, soil samples from the 0–7 m soil profiles were collected from 10 pepper greenhouses with 10 and 20 years planting history, and parallel soil samples were taken from adjacent wheat-maize fields as controls. The results showed that: (1) Compared with wheat-maize fields, the total N amount in the 0–7 m soil layers from the greenhouses increased by 6.19 ± 1.16 and 9.11 ± 3.43 t ha⁻¹ at 10 and 20 years, respectively, accounting for about 30.4% and 17.5% of the N input. (2) The N amount that entered the environment outside the 0–7 m soil layers were 6.95 t ± 2.76 and 29.10 ± 10.14 t ha⁻¹ after 10 and 20 years of continuous planting, accounting for approximately 34.2% and 55.9% of the N input, respectively. (3) The concentration of water-soluble Ca and Mg in the 0–7 m soil layers increased significantly with cultivation years, and correlated positively with mineral N concentration. (4) Moreover, K mainly accumulates in the form of non-exchangeable K in the surface layers (0–50 cm). Our results demonstrated that huge amounts of N migrate to the deep soil with the extension of cultivation years in plastic-greenhouse pepper production systems, accompanied by significant leaching of Ca and Mg, while K mainly accumulates in the surface layers. Full article

Developing biostimulants is a promising approach for sustainable agriculture under a saline environment. Thymol is a plant-derived compound with a potential antioxidative capacity. However, little is known about whether and how the antioxidative property of thymol plays a role in inducing plant tolerance against abiotic stresses. Here, we find that thymol induces saline tolerance in Chinese cabbage seedlings via enhancing the antioxidative capacity. Treatment with NaCl (100 mM) decreased the seedling fresh weight by 59.9% as compared to a control. Thymol at 20 μM showed the greatest effect on promoting seedling growth under saline stress, with the seedling fresh weight being increased by 71.0% as compared to NaCl treatment. Thymol remarkably decreased the overaccumulation of ROS (hydrogen peroxide and a superoxide radical); cell membrane damage (evaluated by lipid oxidation, membrane integrity, and relative conductivity); and cell death in seedlings under saline stress. Thymol induced three antioxidative systems to lower the ROS level in salt-treated seedlings. First, thymol remarkably activated a set of antioxidative enzymes, such as SOD (superoxide dismutase), APX (ascorbate peroxidase), CAT (catalase), and POD (peroxidase). Second, thymol balanced the cellular redox status by increasing the ratio of AsA/DHA (ascorbic acid/dehydroascorbic acid) and GSH/GSSG (glutathione/oxidized glutathione). Third, thymol significantly enhanced the level-two kinds of antioxidants (total phenol and flavonoid). All of these physiological responses were observed in both the shoots and the roots. In sum, thymol deploys multiple antioxidative systems to help Chinese cabbage seedlings against saline stress. Such findings suggest that thymol has great potential to be developed as a novel biostimulant enhancing crop tolerance against saline stress. Full article

The control of irrigation volume is of significant importance in arid regions of northwest China. Particularly, it has a crucial impact on the salinization of shallow groundwater areas. In 2022 and 2023, field experiments were conducted to test three distinct under-membrane irrigation treatments. These treatments were assigned water quotas of HW (27 mm), MW (22.5 mm), and LW (18 mm). The HYDRUS-2D model was integrated with a field experiment to accurately simulate the dynamic fluctuations of soil water and salt in the sunflower root zone. The model’s performance was assessed and verified using real-field data from 2022 and 2023, and the simulation results closely matched the measured values. This research also used stable hydroxide isotopes to assess the water supply from various soil layers at different time intervals in sunflower plants. The results indicated that the three different levels of irrigation applied under the membrane had a significant impact on soil water content. Specifically, there was a significant difference in soil water content at a depth of 0–40 cm (p < 0.05), while there was little effect on the water content at a depth of 40–60 cm (p > 0.05). After irrigation, the average salt content in the top 0–20 cm of soil decreased by 7.0% compared to the medium and low irrigation levels, and by 10.8% compared to the medium irrigation level. Additionally, the medium irrigation level resulted in a 10.8% decrease in salt content compared to the low irrigation level, and a 4.1% decrease compared to the medium irrigation level. During the same period, the soil salinity levels at depths of 0–20 cm, 20–40 cm, 40–60 cm, and 60–100 cm in the area outside the membrane were measured to be 2.7~4.8 g·kg⁻¹, 2.8~4.0 g·kg⁻¹, 2.7~3.4 g·kg⁻¹, and 1.7~2.6 g·kg⁻¹, respectively. These levels decreased by 13.1~55.5%, 0.7~42.8%, −0.4~16.2%, and −72.7~7.5%, respectively. Following irrigation, the HW treatment mostly absorbed water in the 0–40 cm soil layer, while the MW and LW treatments absorbed water in both the 0–40 cm and 60–80 cm soil levels. The results indicated that the most optimal drip irrigation method beneath the membrane in this location was achieved when the amount of water applied was between 25–30 mm. This method demonstrated a combination of water conservation, high crop yield, and effective salt suppression. Full article

In the realm of commercial trade, the appearance quality of wheat is a crucial metric for assessing its value and grading. Traditionally, evaluating wheat appearance quality is a manual process conducted by inspectors, which is time-consuming, laborious, and error-prone. In this research, we developed an intelligent detection system for wheat appearance quality, leveraging state-of-the-art neural network technology for the efficient and standardized assessment of wheat appearance quality. Our system was meticulously crafted, integrating high-performance hardware components and sophisticated software solutions. Central to its functionality is a detection model built upon multi-grained convolutional neural networks. This innovative setup allows for the swift and precise evaluation and categorization of wheat quality. Remarkably, our system achieved an exceptional overall recognition accuracy rate of 99.45% for wheat grain categories, boasting a recognition efficiency that was approximately five times faster than manual recognition processes. This groundbreaking system serves as a valuable tool for assisting inspectors, offering technical support for customs quarantine, grain reserves, and food safety. Full article

Soil pH significantly impacts microbial activity and community assembly, which in turn determines the temperature sensitivity (Q₁₀) of soil respiration. Due to the high soil acidification in China, it is necessary to understand how soil acidification impacts Q₁₀. Here, the Q₁₀ of soil respiration was examined in a long-term field experiment (1982–present) with different soil pH caused by fertilization management. In this experiment, we selected treatments with neutral pH: (1) no crops and fertilization (CK); (2) crops without fertilization (NF); low pH with (3) crops with chemical fertilization (NPK); and (4) crops with chemical fertilization combined with wheat straw incorporation (WS). Under natural soil temperature changes, we observed that soil acidification lowered the Q₁₀ value of soil respiration. Considering only temperature changes, the Q₁₀ of soil respiration was strongly associated with microbial community composition, alpha diversity, and soil ammonium nitrogen. Considering the interaction between soil pH and temperature, warming strengthened the negative effect of soil pH on the Q₁₀ of soil respiration, and the pathway through which soil pH mediated Q₁₀ included not only microbial community composition, alpha diversity, and biomass but also the soil’s available phosphorus. This work enhanced our insights into the relationships between Q₁₀, temperature, and soil pH by identifying important microbial properties and key soil environmental factors. Full article

Red clover (Trifolium pretense) is widely cultivated as an excellent forage and green manure crop. In 2021, a leaf spot disease was discovered in a red clover field in Min County, Gansu Province, China. Symptoms on T. pratense manifested as small white spots that gradually expanded into nearly oval or irregularly shaped gray-white lesions. The causal agent of this new disease was identified as Leptosphaeria weimeri (=Longiseptatispora meliloti) based on morphological identification, pathogenicity tests, and the phylogenetic identification of ITS, LSU, and SSU sequence. The optimal growth temperature was found to be 20 °C under different culture conditions, while the optimal spore-producing temperature was 25 °C. The pH for optimal growth and spore production was seven. The fungus grew and produced spores successfully on both PDA and PSA media. Additionally, the pathogen was efficiently inhibited using 450 g/L of prochloraz fungicide in vitro. To our knowledge, this is the first report of leaf spot disease on red clover caused by L. meliloti in China. Full article

The need to import phosphorus raw materials for fertilizer purposes in Europe as well as the need to manage increasing amounts of waste contributed to the search for alternative sources of phosphorus. One of these is waste sodium–potassium phosphate from the production of polyols. Additionally, a current problem is providing an adequate amount of food, where fertilizers play the main role. Due to the increase in meat consumption, the attractiveness of growing corn for feed is increasing due to its high yield potential and rich composition. The article presents the impact of suspension fertilizers based on waste from the production of polyols on the yield of corn intended for green fodder. In a 3-year field study, the effects of a waste phosphorus source were compared with a commercial granulated phosphorus fertilizer—fosdar. In addition, the suspension fertilizers were assessed according to their composition by testing fertilizers containing only basic nutrients (NPK) and ones enriched with secondary ingredients (S and Mg) and microelements (Zn, Mn and B). The research confirmed the effectiveness of the tested suspension fertilizers. Although the yield obtained was lower than in the case of fosdar fertilization, it still remained at a high level of over 70 t∙ha⁻¹ of fresh yield. Full article

Maize/peanut intercropping may improve soil health through reducing nitrogen (N) fertilization. However, the effects of maize/peanut intercropping combined with reduced N fertilization on the soil fungal community structure have not been well reported. Using a long-term localized micro-zone experiment, we investigated the combined effects of intercropping and N fertilizer application on soil fungal community diversity and composition. Three cropping patterns (maize/peanut intercropping, maize monoculture, and peanut monoculture) and three N application levels (0 kg·hm⁻², 150 kg·hm^−2, and 300 kg·hm⁻²) were assessed. The results showed that the total numbers of fungal species and unique species (operational taxonomic units, OTUs) in both maize and peanut soils tended to first increase and then decrease with increasing N application. Compared with monoculture, the numbers of total OTUs and unique OTUs in intercropped maize soil decreased by 4.14% and 12.79%, respectively, but the total numbers of OTUs and unique OTUs in peanut soil increased by 1.08% and 3.78%, respectively. With increasing N application, the soil fungal Ace and Chao indices of maize soil first increased and then decreased, while the fungal Shannon, Ace, and Chao indices of peanut soil decreased. Compared with the monoculture system, intercropping significantly reduced the maize soil fungal Ace and Chao indices but increased the peanut soil fungal Shannon, Ace, and Chao indices. Nitrogen application and intercropping significantly altered the fungal community structure of maize soil, while N application had no significant effect on the fungal community structure of peanut soil, though intercropping significantly changed the fungal community structure of peanut soil. At the phylum level, Ascomycota, Basidiomycota, Mortierellomycota, unclassified_k_Fungi, and Chytridiomycota were the dominant taxa. Redundancy analysis (RDA) showed that soil nitrate (NO₃⁻) content was the main environmental factor shaping the soil fungal community. In conclusion, excessive N fertilization (300 kg·hm⁻²) can reduce soil fungal community diversity; maize/peanut intercropping reversed the negative effect of N application on fungal community of peanut soil, but not that of maize soil. Soil NO₃⁻ content is the primary environmental driver of soil fungal communities. Full article

Leaf nitrogen concentration (LNC) is a primary indicator of crop nitrogen status, closely related to the growth and development dynamics of crops. Accurate and efficient monitoring of LNC is significant for precision field crop management and enhancing crop productivity. However, the biochemical properties and canopy structure of wheat change across different growth stages, leading to variations in spectral responses that significantly impact the estimation of wheat LNC. This study aims to investigate the construction of feature combination indices (FCIs) sensitive to LNC across multiple wheat growth stages, using remote sensing data to develop an LNC estimation model that is suitable for multiple growth stages. The research employs UAV multispectral remote sensing technology to acquire canopy imagery of wheat during the early (Jointing stage and Booting stage) and late (Early filling and Late filling stages) in 2021 and 2022, extracting spectral band reflectance and texture metrics. Initially, twelve sensitive spectral feature combination indices (SFCIs) were constructed using spectral band information. Subsequently, sensitive texture feature combination indices (TFCIs) were created using texture metrics as an alternative to spectral bands. Machine learning algorithms, including partial least squares regression (PLSR), random forest regression (RFR), support vector regression (SVR), and Gaussian process regression (GPR), were used to integrate spectral and texture information, enhancing the estimation performance of wheat LNC across growth stages. Results show that the combination of Red, Red edge, and Near-infrared bands, along with texture metrics such as Mean, Correlation, Contrast, and Dissimilarity, has significant potential for LNC estimation. The constructed SFCIs and TFCIs both enhanced the responsiveness to LNC across multiple growth stages. Additionally, a sensitive index, the Modified Vegetation Index (MVI), demonstrated significant improvement over NDVI, correcting the over-saturation concerns of NDVI in time-series analysis and displaying outstanding potential for LNC estimation. Spectral information outperforms texture information in estimation capability, and their integration, particularly with SVR, achieves the highest precision (coefficient of determination (R²) = 0.786, root mean square error (RMSE) = 0.589%, and relative prediction deviation (RPD) = 2.162). In conclusion, the sensitive FCIs developed in this study improve LNC estimation performance across multiple growth stages, enabling precise monitoring of wheat LNC. This research provides insights and technical support for the construction of sensitive indices and the precise management of nitrogen nutrition status in field crops. Full article

The olive fruit fly, known as Bactrocera oleae (Rossi) (Diptera: Tephritidae), is causing substantial economic losses in olive crops worldwide. Studying the activity patterns of the insect may expand our knowledge to eventually adopt more sustainable and effective pest control approaches. In the present study, we investigated the impact of food and water deprivation on the mobility of olive fruit flies using a modified version of the LAM25 system (locomotor activity monitor)—Trikinetics, an automated locomotor activity electronic device. Both male and female flies at four different age groups, reared on olives in the laboratory, were individually placed in glass tubes. Their locomotor activity was recorded every minute by three monitors within the digital device over a three-day period. Our observations revealed that adults exhibited significantly reduced movement during nighttime compared to daytime. The greatest mobility was observed during the period of 15:00 to 20:59. Additionally, younger flies demonstrated higher levels of mobility compared to older ones. Flies subjected to both food and water deprivation exhibited higher mobility compared to the control group. These insights offer valuable insights for enhancing pest management strategies aimed at controlling olive fruit flies adopting a more sustainable approach. Full article

Abiotic stimuli severely restrict the growth and development of plants, resulting in massive losses in the quality and yield of crops. Exploring genes that can improve crop tolerance to abiotic stress is important. In a previous study, we found that overexpression of the Arabidopsis nucleotide-binding domain leucine-rich repeat (NB-LRR) gene AtRPM1(D505V) increased disease resistance in rice. In this research, we found that AtRPM1(D505V) transgenic plants were more sensitive to abscisic acid (ABA) than wild type (WT) plants. Abiotic-stress resistance in AtRPM1(D505V) transgenic plants was investigated. We found that AtRPM1(D505V) transgenic plants exhibited improved resistance to drought and salt stress; the phonotype and survival rates of transgenic rice were better than WT plants. The expression of stress responsive genes including OsDREB2A, OsDREB2B, OsRD22, and OsRD29A were significantly upregulated in AtRPM1(D505V) overexpressed plants than in WT plants. Moreover, the activities of catalase (CAT), superoxide dismutase (SOD), and peroxidase (POD) were significantly increased in AtRPM1(D505V) overexpressed plants than in WT plants under drought and salt stress. Under cold stress, the expression of stress responsive genes and the activities of antioxidant enzymes in AtRPM1(D505V) transgenic plants were significantly lower than in WT plants. Our research demonstrated that AtRPM1(D505V) confers drought and salt resistance to transgenic rice. Therefore, AtRPM1(D505V) could act as a potential candidate gene to cultivate drought- and salt-tolerant plants. Full article

Intercropping presents an opportunity to optimise land use and resource efficiency in cotton cultivation, particularly for small and marginal farmers facing climate-related challenges and rising input costs. This study explores the potential of intercropping short-duration vegetables with cotton to transform this production system into a more economically viable and sustainable one. The study was conducted in the Cotton Department of Tamil Nadu Agricultural University in Coimbatore during the winter irrigated season, from August to January, in both 2020 and 2021. The growth, yield parameters, equivalent yield (3645 and 4234 kg ha⁻¹), and net return (Rs. 123,434 ha⁻¹ and Rs. 154,034 ha⁻¹) were higher in the intercropping system with the paired row planting of Bt cotton with two rows of cluster bean. Upon comparing sole cropping and the paired row method of planting, it was found that adopting the paired row system of planting Bt cotton with two rows of cluster bean was highly profitable in all aspects of crop production. Therefore, the adoption of paired row cropping systems with compatible intercrops that promote synergistic effects on the main crop should be considered for enhancing overall productivity, as well as sustainability. Full article

This study evaluated the salt tolerance in 40 celery germplasm resources to clarify the different salt tolerances of celery germplasm. A gradient treatment with different concentrations of NaCl solutions (100, 200, and 300 mmol·L⁻¹) was used to simulate salt stress. After 15 days of salt treatment, 14 indicators related to plant growth, physiology, and biochemistry were determined. The results showed that different celery varieties responded differently to salt stress. Notably, there were significant variations in below-ground dry weight, root–crown ratio, antioxidant enzyme activity, and soluble protein content among the accessions under salt stress. Principal component analysis was used to identify important indices for evaluating salt tolerance, including plant height, spread, content of soluble protein, and so on. A comprehensive evaluation was conducted utilizing the salt damage index, principal component analysis, affiliation function analysis, and cluster analysis. The 40 celery germplasms were classified into five highly salt-tolerant, seven salt-tolerant, fifteen moderately salt-tolerant, nine salt-sensitive, and four highly salt-sensitive germplasms. SHHXQ, MXKQ, XBQC, XQ, and TGCXBQ were highly salt-tolerant germplasms, and BFMSGQ, HNXQ, ZQ, and MGXQW were highly salt-sensitive germplasms. The results of this study provide a reference for the variety of celery cultivation in saline areas and lay a foundation for the selection and breeding of salt-tolerant varieties of celery. Full article

This study aimed to investigate the impact of selenium (Se) treatment on the accumulation of reactive oxygen species (ROS) and the expressions of related genes in broccoli. To achieve this, one group of broccoli heads was treated with a selenite solution of 2 mg L⁻¹, while another group was soaked in distilled water, serving as the control. The effects of these treatments were evaluated by analyzing the browning, hydrogen peroxide (H₂O₂) and malondialdehyde (MDA) contents, enzyme activity, and gene expression levels of WARK and RBOH. Our results show that the Se treatment effectively inhibited H₂O₂ accumulation in the broccoli and reduced harmful MDA levels. The inhibition of ROS accumulation following the Se treatment was associated with enhanced activity of the CAT and SOD enzymes, increased expression levels of BoCAT and BoSOD, and decreased expression levels of the WRKY and RBOH transcription factors. Our study provides insights into the mechanism of action of selenium and its potential application in vegetable storage. Full article

This study investigated the impacts of biochar addition on N conversion, humification, and bacterial community during Flammulina velutipes waste composting. The mixture of chicken manure and Flammulina velutipes waste was 4:6 (dry weight basis). The biochar was added into the mixture and mixed thoroughly at ratios of 0, 2.5, 5, and 7.5% (w/w) and labeled as CK, T1, T2, and T3, respectively. The results showed that the biochar treatment significantly improved the compost maturity by increasing humic substances and the conversion of NH₄⁺-N to NO₃⁻-N. With the increase in biochar supplemental level, the abundance, diversity, and uniformity of the microbial community were improved. The dominant taxa were Firmicutes, Bacteroidota, Actinobacteriota, Proteobacteria, and Gemmatimonadota, especially the Firmicutes and Bacteroidota. Biochar addition facilitated the proliferation of thermophilic bacteria such as Bacillus, Actinobacteriota, Parapedobacter, and Sphingobacterium, leading to enhanced organic decomposition to increase humus. The findings of this study highlighted the positive effects of biochar addition on the composting mixture of chicken manure and Flammulina velutipes waste. These results can help to produce high-quality biochar composting products by balancing organic decomposition and humification based on the bacterial community. Full article

Drought stress is a common abiotic stress, and Lupinus polyphyllus presents strong adaptability, but its drought resistance mechanism has not been explored. This study used PEG-6000 to simulate drought stress, and the H₂O₂ content, O₂⁻ generation rate and MDA content were determined. Transcriptome sequencing and untargeted metabolome analyses were also carried out on an Iceland germplasm and American B germplasm under different drought stress durations. The results showed that the gene regulation range in the American B germplasm was greater, whether genes were upregulated or downregulated. And the number of genes in the American B germplasm was higher than that in the Iceland germplasm. Additionally, the Iceland germplasm produced less peroxide under PEG stress than the Iceland germplasm. The Iceland germplasm was more stable than the American B germplasm under PEG stress, which can be shown in two aspects: peroxide content and gene regulation quantity. Joint transcriptomics and metabolomics analysis showed that genes and metabolites related to secondary and carbon metabolism were mainly involved in the response of Lupinus polyphyllus to PEG-simulated drought stress. The metabolites mainly included phenylalanine, tyrosine, trans-2-hydroxycinnamate, starch synthase, 1,4-alpha glucan branching enzyme and glycogen phosphorylase, and genes mainly included COMT, F5H, REF1, CAD, UGT72E and TPS. These results provided genetic resources and a theoretical basis for further molecular breeding of Lupinus polyphyllus. Full article

This study explores the impact of water and nitrogen management on the dynamics of water, heat, and nitrogen in farmland soil. It also explores the correlations soil factors, enzyme activity, and crop yield. To achieve this, field experiments and HYDRUS model simulations were conducted in the broad furrow irrigation system of the Yinhuang Irrigation Area. The experiment involved three irrigation levels (60%, 70%, and 80% of field water holding capacity, labeled as W1, W2, and W3, respectively) and three nitrogen application rates (120, 220, and 320 kg·ha⁻¹, labeled as N1, N2, and N3). Results indicated that the HYDRUS model, optimized using field trial data, accurately represented soil dynamics. Soil profile water and nitrogen exhibited greater variation in the root zone (0–40 cm) than in the deeper layers (40–100 cm). Water–nitrogen coupling predominantly influenced water and nitrogen content changes in the soil, with minimal effect on soil temperature. Soil enzyme activities at the trumpet, silking, and maturity stages were significantly affected by water–nitrogen coupling, displaying an initial increase and subsequent decrease over the reproductive period. The highest summer maize yield, reaching 10,928.52 kg·ha⁻¹ under the W2N2 treatment, was 46.64% higher than that under the W1N1 treatment. The redundancy analysis revealed a significant positive correlation between soil nitrate nitrogen content and soil enzyme activity (p < 0.05). Furthermore, there was a significant positive correlation between soil enzyme activity and both maize yields (p < 0.01). This underscores that appropriate water and nitrogen management can effectively enhance yield while improving the soil environment. These findings offer valuable insights for achieving high yields of summer maize in the Yellow River Basin. Full article

The intelligent control of precision planting can detect and regulate the operation quality of the planter in real time, which plays an important role in improving the operation quality of the planter and the yield of the corn. In this paper, the control system of a corn precision planter is designed to realize the operating quality monitoring and electric driving of the seed-metering device. The planting quality is calculated by the time interval between the neighboring falling seeds, instead of the plant spacing, to improve the operational efficiency of the system. At the same time, the forward speed of the planter is obtained by radar, which is used to accurately match the speed of the seed-metering device with the forward speed of the planter. The velocity error of the radar is analyzed, and the relevant relationship of the radar output frequency and forward speed is established. Comparative test results of this system and the JPS-12 test bench show that the detection performance of the system is reliable, and the maximum detection error of the quality parameters is less than 2.88%. Field experiments were carried out to verify the operational performance of the control system. Two speed sensors, radar and GPS, were chosen to study the effect of speed measuring on the performance of the control system. We found that speed measuring has a significant effect on planting performance. The qualified parameters of radar were significantly higher than those of GPS, at a forward speed of 6–12 km/h. The qualification feeding index (QFI) of radar was 0.51%, 0.67%, and 2.05% higher than that of GPS at speeds of 6, 8, 10, and 12 km/h. The precision index (PREC) of radar was 17.60%, 5.44%, 16.81%, and 17.30% lower than that of GPS. Therefore, the control system based on the radar speed measurement developed in this paper can significantly improve the operating quality of the planter. Full article

Phenotyping of genetic resources is an important prerequisite for the selection of resistant varieties in breeding programs and research. Computer vision techniques have proven to be a useful tool for digital phenotyping of diseases of interest. One pathogen that is increasingly observed in Europe is Diplocarpon coronariae, which causes apple blotch disease. In this study, a high-throughput phenotyping method was established to evaluate genetic apple resources for susceptibility to D. coronariae. For this purpose, inoculation trials with D. coronariae were performed in a laboratory and images of infested leaves were taken 7, 9 and 13 days post inoculation. A pre-trained YOLOv5s model was chosen to establish the model, which was trained with an image dataset of 927 RGB images. The images had a size of 768 × 768 pixels and were divided into 738 annotated training images, 78 validation images and 111 background images without symptoms. The accuracy of symptom prediction with the trained model was 95%. These results indicate that our model can accurately and efficiently detect spots with acervuli on detached apple leaves. Object detection can therefore be used for digital phenotyping of detached leaf assays to assess the susceptibility to D. coronariae in a laboratory. Full article