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This study addresses the imperative need for early detection of leaf diseases in tobacco, pepper, and tomato plants, as these diseases significantly impact crop yield and quality. Existing methods often fall short in accurately identifying diseases across diverse plant species. The research aims to bridge this gap by proposing a hybrid deep learning approach, combining the robust feature extraction capabilities of AlexNet with the precise segmentation and classification prowess of UNet. The proposed hybrid model leverages AlexNet proficiency in extracting hierarchical features from plant leaf images and subsequently utilizes UNet for accurate disease classification. This synergistic combination enables the model to overcome the challenges posed by the varied morphologies of tobacco, pepper, and tomato leaves. Experimental results demonstrate the effectiveness of the proposed methodology, showcasing superior performance in terms of accuracy, sensitivity, and specificity compared to existing techniques. The hybrid deep learning approach exhibits promising potential for early and accurate detection of leaf diseases, contributing to sustainable crop management practices. [ABSTRACT FROM AUTHOR]

Copyright of Ecosistemas y Recursos Agropecuarios is the property of Universidad Juarez Autonoma de Tabasco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Crop insect detection becomes a tedious process for agronomists because a substantial part of the crops is damaged, and due to the pest attacks, the quality is degraded. They are the major reason behind crop quality degradation and diminished crop productivity. Hence, accurate pest detection is essential to guarantee safety and crop quality. Conventional identification of insects necessitates highly trained taxonomists to detect insects precisely based on morphological features. Lately, some progress has been made in agriculture by employing machine learning (ML) to classify and detect pests. This study introduces a Modified Metaheuristics with Transfer Learning based Insect Pest Classification for Agricultural Crops (MMTL-IPCAC) technique. The presented MMTL-IPCAC technique applies contrast limited adaptive histogram equalization (CLAHE) approach for image enhancement. The neural architectural search network (NASNet) model is applied for feature extraction, and a modified grey wolf optimization (MGWO) algorithm is employed for the hyperparameter tuning process, showing the novelty of the work. At last, the extreme gradient boosting (XGBoost) model is utilized to carry out the insect classification procedure. The simulation analysis stated the enhanced performance of the MMTL-IPCAC technique in the insect classification process with maximum accuracy of 98.73%. [ABSTRACT FROM AUTHOR]

In terms of the potential effects of the natural (geogenic) and anthropogenic agricultural environment on the crop quality, it must be remembered that it is necessary to maintain an uncontaminated environment with high soil production potential. The objective of this research was to analyse and evaluate soil under sugar beet, the compost produced and the soil affected by the addition of compost, in order to determine the Cd, NO3--N, NH4+-N contents in topsoil and soil with added compost (1:1 ratio), to evaluate the statistical significance of Cd in relation to the fertile forms of nitrogen, and to find out whether compost influences the monitored parameters. The analyses were carried out using the Atomic Absorption Spectrometry and Colorimetric methods. The obtained results were evaluated statistically using the SAS 9.4 software method and Spearman's correlation coefficient. The results showed that NO3--N contents in soil reached 14.73 mg·kg-1, NH4+-N 9.50 mg·kg-1, Nin 23.05 mg·kg-1, Cd 2.96 mg·kg-1, soil + compost (1:1 ratio) NO3--N 23.93 mg·kg-1, NH4+-N 26.42 mg·kg-1, Nin 42.63 mg·kg-1, Cd 2.71 mg·kg-1 dry matter. Very high statistical dependence (P < 0.001) was observed for all inorganic fertile forms of nitrogen. High statistical dependence (P < 0.01) was observed for soil Cd to soil NO3--N, soil + compost Cd to soil NO3--N, soil + compost Cd to soil Nin. Compost had a statistically highly significant effect on Cd in relation to forms of nitrogen, reducing Cd concentration levels and increasing the proportion of inorganic fertile forms of nitrogen. [ABSTRACT FROM AUTHOR]

The destructive environmental impacts of large-scale agriculture are further aggravated by drier climate in the Danube-Tisza sand region, where soil fertility is low, water retention is very limited and groundwater levels are steadily dropping. The prevention of wind erosion is a central task in raising the level of ecosystem services. Among other related services the enrichment of the blown-sand soil with organic matter (enhancing carbon sequestration), the improvement of soil structure and water retention capacity and achieving higher soil biodiversity are major objectives. The international Diverfarming project (2017-2022) within the EU Horizon 2020 Program focussed on the impacts of crop diversification and low-input practices. In the experiments intercropping with different annual crops was investigated and its influences on some provisioning and regulating ecosystem services were evaluated in an asparagus field in Jakabszállás. Although the findings point out the decisive role of soil moisture conditions, the positive impacts of diversification are also remarkable. The local farmer agrees that crop diversification improves soil quality, but he denies that it would directly influence farm competitiveness, which is primarily dependent on the costs of fertilization, plant protection and labour. Further analyses are needed to prove the benefits of diversification through enriching soil microbial life and the possible reduction of fertilizer use while water demand is kept at a low level and the same crop quality is ensured. [ABSTRACT FROM AUTHOR]

Paddy is a major food crop serving more than half the population of people in the world. It is inevitable to improve the quantity and quality of food crop with the growing population. Different factors including soil fertility, water availability, erratic climate variations, diseases, and pests, have an impact on paddy crop yield. It is crucial to identify the root cause for the reduction in yield of paddy. Early disease diagnosis prevents the plants from getting worst through its consecutive stage. The concern with manually diagnosing plant leaf diseases with the naked eye is that the results can be less accurate and even unreliable. Automatic disease diagnosis eliminates the need for experts and provides accurate results. This paper will assist the farmers to identify the leaf diseases automatically with the aid of Convolutional Neural Networks. This research includes paddy leaf disease categories: bacterial blight, blast, tungro, brown spot and healthy leaves. The dataset contains 800 images, 160 images from each of the five categories. Images are resized to 256 * 256 pixels and normalized. The network architecture created with convolutional, maxpooling, flatten and dense layers. The Dataset is divided into training and validation set in 70:30 ratios and model is trained with 20 epochs of batch size 16. The novelty of the study is the implementation of extended Huber loss function for minimizing the loss. Furthermore, it is cross compared with existing loss functions. The Proposed model has achieved 96.63% training accuracy and 86.61% validation accuracy with 5 classes. Performance of model is evaluated with confusion matrix with precision, recall, F1-score and support as parameters. [ABSTRACT FROM AUTHOR]

The impact of emerging stressors, such as pesticides and heavy metals, on the nutritional quality, resistance, and antioxidant systems of crops is the subject of intense monitoring. Due to its low toxicity and biocompatibility, nano-selenium (nano-Se) increases antioxidant capacity more effectively than selenium (Se). However, the protective mechanism of nano-Se in plum trees is still unknown when subjected to long-term abiotic stress. In this study, nano-Se foliar application enhanced the fruit's fresh weight and diameter and plant growth and development by increasing the content of trace elements (Zn and Se) and amino acids (Try, Phe, Pro, and Arg) in leaves and fruits. Compared to the control, nano-Se treatment dramatically improved the plant's antioxidant system, resulting in a substantial increase in SOD (44.3 %), POD (24.3 %), and CAT (95.6 %) levels. It also increased IAA (118.8 %), total flavonoids (23.0 %), total phenols (15.8 %), rutin (37.7 %), quercetin (146.8 %), and caffeic acid (19.8 %) contents by regulating phenylpropane metabolic pathways. Targeted amino acid analysis indicated that nano-Se biofortification greatly enhanced the levels of His (60.7 %), Ser (123.5 %), Thr (105.7 %), Val (202.1 %), Ile (236.2 %), Leu (84.0 %), Tyr (235.0 %), and Phe (164.7 %). The non-target metabolomics results showed that nano-Se treatment stimulated plum growth and nutrition by boosting phenylpropane metabolism and amino acid production. Therefore, nano-Se can improve the quality and resistance of plums by regulating both the primary and secondary metabolic pathways of plants and enhancing the antioxidant capacity. This investigation provides a reference for extrapolating the positive effects of nano-Se on crop quality to other plant species. [Display omitted] • Nano-Se biofortification increases the fruit's fresh weight and diameter. • Up-regulation of amino acids-related metabolites enhances plum's nutrient levels. • Nano-Se increased plum resistance by boosting phenylpropane metabolism. • Nano-Se treatment substantially improved the plant's antioxidant system. [ABSTRACT FROM AUTHOR]

Precision agriculture technology has become a crucial means of improving the quality of crop production. As an emerging technology in farmland management, intelligent weeding robots utilize intelligent spraying systems to effectively manage weeds, adjusting the types and dosages of herbicides in a timely manner. The accuracy and real-time performance of weed identification algorithms are the keys to intelligent weeding. This study established a proprietary dataset comprising 6690 images of soybean seedlings and weeds and proposed an improved lightweight algorithm, YOLOv8-ECFS. Based on YOLOv8s, this model introduces the EfficientNet network to improve feature extraction capability and accelerate the inference speed, replacing the CIoU loss with Focal_SIoU to optimize the regression accuracy of the bounding boxes. Furthermore, the coordinate attention module is introduced into the neck to enable the model to precisely capture textural and color differences between various weeds and soybean crops, thereby ensuring precise identification of multiple weed species. The results demonstrate that YOLOv8-ECFS achieves precision, mAP, and F1 values of 92.2%, 95.0%, and 90.9%, representing an increase of 2.5%, 1.3%, and 1.6%, respectively, compared to YOLOv8s. Simultaneously, the model's GFLOPs and model size have been reduced by 11.1G and 9.1 MB, respectively, ensuring both recognition accuracy and lightweight performance. The test set results show that YOLOv8-ECFS accurately identifies densely growing and mutually occluding weeds, reducing cases of false positives and missed detections. Compared to other mainstream YOLO algorithms, YOLOv8-ECFS demonstrates the best overall performance, thus providing support for intelligent weeding robots in farmland management and unmanned farms. • A complex dataset containing soybeans and weeds was created. • Proposed YOLOv8-ECFS lightweight weed identification model. • Achieved accurate identification and classification of different weeds. [ABSTRACT FROM AUTHOR]

Effective crop farming depends on wise selection of crops. It is an essential factor that has to be fulfilled before beginning an agricultural endeavor. Conventionally, the crop that has to be grown is selected without considering the location and cultivated site's characteristics by only considering its profit and demand on the market. Choosing the best crop for the circumstances can minimize the need for additional fertilizer and water for irrigation and help in attaining enhanced crop yield. Therefore, choosing the right crop is crucial for a successful agricultural situation. Thus, a novel crop recommendation model by considering the soil and geographical conditions is developed to aid the farmers in choosing the appropriate crop for the right condition so that the overall production can be enhanced to increase the overall profit and decrease the losses faced by the farmers. At first, a certain geographical area is selected, and the ideal parameters for growing a particular plant are gathered from the standard database. Next, the deep optimal features are extracted using a Serial Cascaded network in which an autoencoder is cascaded with a "Dimensional Convolutional Neural Network (1DCNN)" from the gathered data. The obtained deep features are optimally selected using the developed Modified Movement Territory of Fire Hawk Optimizer (MMTFHO). These optimally selected features are given to the Adaptive and Attention-based Hybrid Network (AAHNet) in which "Gated Recurrent Unit (GRU), and Long Short Term Memory (LSTM)" are utilized for choosing the right crop for the provided geographical condition. The parameters in the AAHNet are optimized using the same enhanced MMTFHO algorithm for improving the precision of the appropriate crop selection process. The final prediction of crops for the given geographical condition is obtained from the AAHNet. The final or overall rating of the recommended approach regarding accuracy metrics is 96.73%. • To design an effective crop recommendation system using Hybrid approaches. • To design an Adaptive and Attention-based Hybrid Network (AAHNet) model. • To promote an AAHNet model for enhancing the precision of crop selection. • To introduce a Modified Movement Territory of Fire Hawk Optimizer (MMTFHO). • To optimize the variables and select the optimal features using MMTFHO algorithm. [ABSTRACT FROM AUTHOR]

Copyright of Ecosistemas y Recursos Agropecuarios is the property of Universidad Juarez Autonoma de Tabasco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

India is an agricultural country, and farming is the most common occupation among Indians. Rice is a vital crop in the agricultural industry. Productivity has been declining for almost a decade. There are several causes for this, including fragmented land holdings, Indian farmer illiteracy, a lack of decision-making capacity in selecting excellent seeds, manure, and irrigational infrastructure. One of the major reasons for rice crop failure is due to malnutrition. Rice, maybe in particular, lacking in nutrients such as potassium, nitrogen, and phosphorus. Nutrient deficiency detection in crops is necessary to plan further actions to enhance yield. Most studies have relied on the use of transfer learning models for agricultural uses. Ensembling of different transfer learning techniques has the ability to greatly increase the predictive model's performance. Five transfer learning architectures InceptionV3, Xception, VGG16, Resnet50, and MobileNet are all taken into account, and their different ensemble models are used to perform deficiency detection in rice plants where ensembled models performs better when compared to individual models. The ensembled model i.e. InceptionV3 + Xception has achieved an accuracy of 98% when compared to other models and it can be utilized in real time situations. The mobile application was created as a user-friendly interface to assist farmers. The accurate diagnosis of these nutritional deficiencies like nitrogen, potassium, phosphorus and recommendation of fertilizer to corresponding nutrient deficiency with the help of mobile application could aid farmers in providing correct plant intervention and to keep track of crop growth. [ABSTRACT FROM AUTHOR]

Recent research emphasises the importance of both within-group cooperation and between-group competition for human sociality, past and present. We hypothesise that the shift from foraging to food production in eastern North America provided novel socioecological conditions that impacted interpersonal and intergroup interactions in the region, inspiring both greater cooperation as well as competition. We predict that (1) successful exploitation of this indigenous crop complex encouraged greater cooperation leading to site aggregation in high-quality locations as expected by an ideal free distribution with an Allee effect, and (2) continued population growth driven by the domestication and adoption of the crop complex eventually inspired a shift from positive to negative density dependent settlement dynamics, driving declines in site suitability. Our results demonstrate that there was an increase in both site clustering and site location quality coincident with crop management and domestication in the Middle Holocene, and that territorial violence appears at this time as well. Site quality later declined after c. 3000 cal BP, also as predicted. These results indicate that managing and domesticating plants inspired an Allee effect and led to greater within-group cooperation, but was also related to the rise of territorial between-group competition in the region. [ABSTRACT FROM AUTHOR]

Over the past few years, total financial investment in the agricultural sector has increased substantially. Palm tree is important for many countries' economies, particularly in northern Africa and the Middle East. Monitoring in terms of detection and counting palm trees provides useful information for a variety of stakeholders; it helps in yield estimation and examination to ensure better crop quality and prevent pests, diseases, better irrigation and other potential threats. Despite their importance, these information still difficult to obtain. In this study, we systematically review research articles between 2011 and 2021 on artificial intelligence (AI) technology for smart palm tree detection. A systematic review (SR) was performed using the PRISMA approach based on a four-stage selection process. Twenty-two articles were included for the synthesis activity reached from the search strategy alongside the inclusion criteria in order to answer tow two main research questions. The study's findings reveal patterns, relationships, networks, and trends in the application of artificial intelligence in the palm tree detection over the last decade. Overall, despite the good results achieved in most of the studies, the effective and efficient management of large-scale palm plantations still a challenge. In addition, countries which their economy strongly related to intelligent palm services especially in North Africa should give more attention to this kind of studies. The results of this research could benefit both the research community and stakeholders. [ABSTRACT FROM AUTHOR]

It is common to have various clones from cross-seedlings or unintended planting by the farmers in a tea plantation. Since each tea clone has distinctive features such as quality, resistance to diseases, etc., visual inspections are usually conducted on the plantations to segment areas with different tea clones within the plantation to produce crops with consistent quality. However, this would be costly and time-consuming. In this work, we apply machine learning and develop an application to recognize tea clones automatically. We propose a convolutional variational autoencoder-based feature learning algorithm to produce robust features against data distortions. There are two main advantages of using this algorithm for feature learning. First, there is no need to design complex handcrafted features for classifications, usually conducted in machine learning. Second, the resulting features are more robust when tested with data taken from unideal conditions. The proposed method is evaluated using the original and the distorted image. Our proposed method achieves the best performance of 0.83 (83%) for the original image test, 0.75 (75%) for the gaussian blur image test, and 0.78 (78%) for the median blur image test. This is a much more robust result than VGGNet16, a popular supervised deep convolutional neural network. [ABSTRACT FROM AUTHOR]

Food and income loss caused by conventional open-sun drying of crops could be reduced through the use of protected solar drying equipment. Yet, solar dryers have not been widely adopted among the majority 470 million smallholder farms worldwide. Combining primary survey and field data on dried chili harvests collected in India, this paper demonstrates for the first time how technical, financial, and operational barriers combine to limit smallholder ownership of commercially available hoophouse tunnel dryers. It then addresses those barriers using a systems-level approach. First, economic loss due to degradation of chilies in open-sun drying is quantified, averaging 34% of potential revenue. Despite demonstrated revenue gains of 22% based on improved quality, technoeconomic analysis indicates that seasonally stranded solar dryers are not profitable enough to overcome capital and operational cost barriers: cumulative income parity with open-sun drying is not reached for 3–7 years. Second, to address this major adoption barrier, complementary multi-seasonal revenue streams are introduced that incorporate both drying of crops and growing of seedlings in the same dryer structure for year-round revenue generation. Together with service-based operation to support smallholder access, payback time is decreased by at least 50% relative to single-season usage. Adding available financing schemes, multi-season dual-use dryers could reach payback in 1 year versus 6.5 years for drying-only usage, reducing the financial adoption barrier by up to 85%. This suggests that multi-season utility can drive solar dryer adoption. Finally, a sensitivity analysis of single- and dual-use solar dryer operations is provided and their practical implementation by smallholder farmers is discussed. [Display omitted] • Traditional open-sun crop drying causes quality degradation and wastage that can lower farmer income by 34%. • Protected solar dryers improved dried crop quality and increased market prices of chilies by 22%. • Solar dryers are seasonally stranded assets with a long payback time that limits adoption. • Multi-seasonal uses of solar dryers with growing can greatly reduce payback time by over 50% and increase adoption. • Service-based operations give farmers access to drying and growing support without the upfront capital cost. [ABSTRACT FROM AUTHOR]

Heavy metals increase in nature due to anthropogenic activities and negatively impact the growth, progress, and efficiency of plants. Among the toxic metal pollutants that can cause dangerous effects when accumulated by plants, mercury (Hg) and cadmium (Cd) were investigated in this study. These metals typically inhibit important enzymes and halt their functioning, thereby adversely affecting the capability of plants to achieve photosynthesis, respiration, and produce quality crops. Acetylcholine (ACh) serves as a potent neurotransmitter present in both primitive and advanced plant species. Its significant involvement in diverse metabolic processes, particularly in regulating growth and adaptation to stress, needs to be further elucidated. For this aim, effects of acetylcholine (ACh1, 10 μM; ACh2, 100 μM) were survey in Triticum aestivum under Hg and/or Cd stress (Hg, 50 μM; Cd, 100 μM). Wheat seedlings exhibited a growth retardation of about 24% under Hg or Cd stress. Combined stress conditions (Cd + Hg) resulted in a decrease in RWC by approximately 16%. Two different doses of ACh treatment to stressed plants positively affected growth parameters and regulated the water relations. Gas exchange was limited in stress groups, and the photochemical quantum competency of PSII (F v /F m) was suppressed. Cd + ACh1 and Cd + ACh2 treatments resulted in approximately 2-fold and 1.5-fold improvement in stomatal conductance and carbon assimilation rate, respectively. Similarly, improvement was observed with ACh treatments in wheat seedlings under Hg stress. Under Cd and/or Hg stress, high levels of H 2 O 2 accumulated and lipid peroxidation occurred. According to our results, ACh treatment upon Cd and Hg stresses improved the activities of SOD, POX, and APX, thereby reducing oxidative damage. In conclusion, ACh treatment was found to ensure stress tolerance and limit the adverse effects caused by heavy metals. [Display omitted] • Heavy metal stress caused growth retardation in wheat seedlings. • Cd and Hg suppressed the photosynthetic capacity of wheat leaves. • ACh reduced oxidative stress in wheat leaves under heavy metal pollution. • ACh alleviated the negative effects of stress on physiological parameters. • ACh treatment improved antioxidant activity and provided resilience. [ABSTRACT FROM AUTHOR]

Cadmium contamination can lead to a decrease in crop yield and quality. However, Cd-tolerant rice can improve rice resistance genes, improve crop tolerance to heavy metals, and protect plants from oxidative damage. In this study, Japonica rice: Chunyou 987 and Indica rice: Chuanzhong you 3607 were used to reveal the molecular response mechanism of Cd-tolerant rice under cadmium concentration of 3 mg/kg through comparative experiments combined with physiology and proteomics. The results showed that compared with indica rice, japonica rice showed more robust resistance to Cd stress and effectively retained many Cd ions in roots. Moreover, it enhanced its enzymatic and non-enzymatic anti-oxidative stress mechanism, which increased the activities of catalase (CAT), peroxidase (POD), and superoxide dismutase (SOD) by 47.37%, 21.75%, and 55.42%, respectively. The contents of non-enzymatic antioxidant substances ascorbic acid (AsA), glutathione (GSH), cysteine (Cys), proline (PRO), anthocyanins (OPC), and flavonoids were increased by 25.32%, 42.67%, 21.43%, 50.81%, 33.23%, and 72.16%, respectively. Through proteomics analysis, it was found that in response to the damage caused by cadmium stress, Japonica rice makes Photosynthesis functional proteins (psbO and PetH), Photosynthesis antenna proteins (LHCA and ASCAB9), Carbon fixation functional proteins (PEPC and OsAld), Porphyrin metabolism functional proteins (OsRCCR1 and SE5), Glyoxylate and dicarboxylate The expression of metabolism functional proteins (CATC and GLO4.) and Glutathione metabolism functional proteins (APX8 and OsGSTU13) were significantly up-regulated, which stimulated the antioxidant stress mechanism and photosynthetic system, and constructed a robust energy supply system to ensure the normal metabolic activities of life. Strengthening the mechanisms of plant homeostasis. In summary, this study revealed the molecular mechanism of tolerance to Cd stress in japonica rice, and the results of this study will provide a possible way to improve Cd-resistant rice seedlings. [Display omitted] • Many cadmium ions were retained in the roots of Cd-tolerant japonica rice. • Japonica rice shows its stress resistance by significantly enhancing its own enzymatic and non-enzymatic antioxidant mechanisms. • Japonica rice enhances its enzymatic and non-enzymatic antioxidant mechanisms to show its stress resistance. • Japonica rice maintains its homeostasis by constructing a solid energy supply system. [ABSTRACT FROM AUTHOR]

The adversities of cadmium (Cd) contamination are quite distinguished among other heavy metals (HMs), and so is the efficacy of zinc (Zn) nutrition in mitigating Cd toxicity. Rice (Oryza sativa) crop, known for its ability to absorb HMs, inadvertently facilitates the bioaccumulation of Cd, posing a significant risk to both the plant itself and to humans consuming its edible parts, and damaging the environment as well. The use of nanoparticles, such as nano-zinc oxide (nZnO), to improve the nutritional quality of crops and combat the harmful effects of HMs, have gained substantial attention among scientists and farmers. While previous studies have explored the individual effects of nZnO or Serendipita indica (referred to as S.i) on Cd toxicity, the synergistic action of these two agents has not been thoroughly investigated. Therefore, the gift of nature, i.e., S. indica , was incorporated alongside nZnO (50 mg L−1) against Cd stress (15 μM L−1) and their alliance manifested as phenotypic level modifications in two rice genotypes (Heizhan43; Hz43 and Yinni801; Yi801). Antioxidant activities were enhanced, specifically peroxidase (61.5 and 122.5% in Yi801 and Hz43 roots, respectively), leading to a significant decrease in oxidative burst; moreover, Cd translocation was reduced (85% for Yi801 and 65.5% for Hz43 compared to Cd alone treatment). Microstructural study showed a decrease in number of vacuoles and starch granules with ameliorative treatments. Overall, plants treated with nZnO displayed gene expression pattern (particularly of ZIP genes), different from the ones with alone or combined S.i and Cd. Inferentially, the integration of nZnO and S.i holds great promise as an effective strategy for alleviating Cd toxicity in rice plants. By immobilizing Cd ions in the soil and promoting their detoxification, this novel approach contributes to environmental restoration and ensures food safety worldwide. [Display omitted] • NZnO and S. indica interplay with Cd increased carotenoid contents. • Precipitation by nZnO and adsorption by S. indica reduced Cd bioaccumulation. • Co-application of nZnO + S. indica relieved oxidative overdrive in Cd-stressed plants. • Metallothionein levels and expression of MT2b were reduced under nZnO and S. indica. [ABSTRACT FROM AUTHOR]

Tomato (Solanum lycopersicum L.) is one of the most important and widely cultivated fruit. By characterizing local germplasm, exclusive breeding material is successfully identified and improved varieties could be created. The research conducted between November 2020 and 2021 at the Ayub Agricultural Research Institute (AARI) in Pakistan used developmental, vegetative and fruit traits to individualize sixty tomato inbred lines for 17 morphological traits. The study showed that 6 qualitative and 11 quantitative morphological traits exhibited a wide range of variation, while 8 traits showed a high coefficient of variation among genotypes according to biplot principal component analysis (PCA). According to biplot PCA, the first component contributed 17.6% of the variation, while the second component contributed 13.0%. Together, the first and second components explained 30.6% of the variation. A correlation analysis was performed to determine the relationship between genotypic traits and phenotypic traits. A very positive correlation was observed between inter-nodal distance, flower per cluster and immature fruit color. The morphological data for firmness, pericarp thickness, fruit width, TSS, inter-nodal distance, number of flowers and fruits per cluster showed specific characteristics that can be used to distinguish the genotypes. The highest value of lycopene of 21.2 mg kg−1 fresh weight was observed among the genotypes. We conclude the results of this study, presenting the crop quality and yield characteristics that could be used in future plant breeding and genetic enhancement of tomato germplasm for improved food security. Based on yield characteristics, Nyagous, Korneseivje and Earliana were selected as the best and most promising genotypes. [ABSTRACT FROM AUTHOR]

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• Explores the ways that socio-material qualities of crops shape agrarian speculation in central India. • Analyses how uneven access to the 'means of speculation' reflect and exacerbate rural inequalities of caste and class. • Develops a deeper understanding of the centrality of speculative practices to contemporary agriculture. This paper examines the political ecology of onions in India, one of the largest producer countries in the world. Although the cost of producing onions is significant, an increasing number of farmers are drawn to its cultivation. Onion prices are, however, highly volatile, fluctuating wildly within the span of a few days, making it a notorious gamble on the market. As a kitchen staple and the second highest consumed vegetable in India, it is also a politically significant crop – high retail rates are held responsible for the fall of governments, and are met with price controls, storage limits, and export restrictions. Yet, in the expectation of spectacular gains, farmers try their luck with its cultivation, investing in inputs, storing and sorting, hoping and waiting for the perfect time to sell. Building on ethnographic fieldwork in the Malwa region of western Madhya Pradesh, I consider how the specific socio-ecological qualities of onion cultivation, harvesting and storage – from high yields to limited storability and the seasonality of cultivation – shape its speculative possibilities. This research finds that it is largely privileged classes and castes of farmers who have access to the 'means of speculation' and thereby, to spectacular profits from onion cultivation, leading to the worsening of social inequalities among farmers of this crop. Broadly, this article theorizes the onion as a 'speculative crop', one that symbolizes the precarity and possibilities of Indian agriculture in an age of intensive cash-cropping and climate change. [ABSTRACT FROM AUTHOR]

Epoxiconazole is a triazole compound. However, the effects of epoxiconazole on crop productivity and quality were rarely reported. In this study, we investigated the effects of epoxiconazole application on yield formation, grain quality attributes, and 2-acetyl-1-pyrroline (2-AP) content in fragrant rice. A three-year field experiment was carried out with a fragrant rice variety, Meixiangzhan 2. At the heading stage, 0, 0.02, 0.04, 0.08, 0.16 and 0.32 g/L epoxiconazole solutions were foliar applied to fragrant rice plants, respectively. The results showed that epoxiconazole application significantly increased grain yield, seed-setting rate and 1000-grain weight. Chlorophyll content and net photosynthetic rate of fragrant rice during the grain-filling stage significantly increased due to epoxiconazole application. Foliar application of epoxiconazole at 0.08 g/L increased grain protein content and decreased both chalky rice rate and chalkiness area ratio of fragrant rice. Epoxiconazole also substantially increased grain 2-AP content by inducing the regulation in contents of related synthetic precursors, including proline, pyrroline-5- carboxylic acid, Δ1-pyrroline and methylglyoxal. Overall, foliar application of epoxiconazole could be used for the improvement in grain yield, grain quality and 2-AP content in fragrant rice production when applied concentration at 0.08–0.32 g/L. Our findings provided the new roles of epoxiconazole in crop production. [ABSTRACT FROM AUTHOR]

Copyright of Revista de la Universidad del Valle de Guatemala is the property of Universidad del Valle de Guatemala and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Pollinators face multiple pressures and there is evidence of populations in decline. As demand for insect‐pollinated crops increases, crop production is threatened by shortfalls in pollination services. Understanding the extent of current yield deficits due to pollination and identifying opportunities to protect or improve crop yield and quality through pollination management is therefore of international importance. To explore the extent of "pollination deficits," where maximum yield is not being achieved due to insufficient pollination, we used an extensive dataset on a globally important crop, apples. We quantified how these deficits vary between orchards and countries and we compared "pollinator dependence" across different apple varieties. We found evidence of pollination deficits and, in some cases, risks of overpollination were even apparent for which fruit quality could be reduced by too much pollination. In almost all regions studied we found some orchards performing significantly better than others in terms of avoiding a pollination deficit and crop yield shortfalls due to suboptimal pollination. This represents an opportunity to improve production through better pollinator and crop management. Our findings also demonstrated that pollinator dependence varies considerably between apple varieties in terms of fruit number and fruit quality. We propose that assessments of pollination service and deficits in crops can be used to quantify supply and demand for pollinators and help to target local management to address deficits although crop variety has a strong influence on the role of pollinators. [ABSTRACT FROM AUTHOR]

In sub-Saharan Africa, the high cost of synthetic fertilisers and the need to produce higher quality crops have necessitated research on alternative nutrient inputs. The effects of soil amendments with poultry manure + Tithonia diversifolia compost at 10, 20, 30 t ha−1 (PMC10, PMC20, PMC30), cow dung + T. diversifolia compost at 10, 20, and 30 t ha−1 (CDC10, CDC20, CDC30) poultry manure (PM) at 10 t ha−1 (PM10), cow dung (CD) at 15 t ha−1 (CD15), and T. diversifolia (TT) at 10 t ha−1 (TT10) on growth, yield and quality of okra was investigated in a field trial in Abeokuta, Nigeria in 2011 and 2013. Treatments were arranged using a randomised block design with three replicates. The highest numbers of leaves per plant (14) were recorded in the treatments with PMC20 and PMC30 in 2011 and 2013, respectively. Application of PMC20 significantly reduced the number of days to 50% flowering compared with the control. In 2011, the potassium concentration in okra leaves and the nitrogen concentration in okra pods were highest for plants grown in soil amended with PMC30. The uptake of phosphorus was highest for plants treated with CDC20 (1.26 g m−2) and with TT10 (0.65 g m−2) in 2011 and 2013, respectively. All treatments resulted in significant increases in the yield of pods compared with the control. PM and CD used either in their original forms or composted with Tithonia diversifolia improved the growth and yield of the okra and the nutrient concentrations of the pods. [ABSTRACT FROM AUTHOR]

Bacterial community found in biodynamic preparations (BD500–BD507) can help improve soil health, plant development, yield, and quality. The current work describes a metagenomic investigation of these preparations to identify the bacterial communities along with the functional diversity present within them. Metagenome sequencing was performed using the Illumina MiSeq platform, which employs next-generation sequencing (NGS) technology, to provide an understanding of the bacterial communities and their functional diversity in BD preparations. NGS data of BD preparations revealed that maximum operational taxonomic units (OTUs) of the phylum Proteobacteria were present in BD506 (23429) followed by BD505 (22712) and BD501 (21591), respectively. Moreover, unclassified phylum (16657) and genus (16657) were also highest in BD506. Maximum alpha diversity was reported in BD501 (1095 OTU) and minimum in BD507 (257 OTU). Further, the OTUs for five major metabolic functional groups viz carbohydrate metabolism, xenobiotic degradation, membrane transport functions, energy metabolism, and enzyme activities were abundant in BD506 and BD501. The bacterial communities in BD506 and BD501 are found to be unique and rare; they belong to functional categories that are involved in enzyme activity, membrane transport, xenobiotic degradation, and carbohydrate metabolism. These preparations might therefore be thought to be more effective. The investigation also found a highly varied population of bacteria, which could explain why BD preparations work well in the field. In view of this, the BD preparations may be utilized for unexploited bacterial communities for sustainable agriculture production. [ABSTRACT FROM AUTHOR]

Weeds can decrease yields and the quality of crops. Detection, localisation, and classification of weeds in crops are crucial for developing efficient weed control and management systems. Deep learning (DL) based object detection techniques have been applied in various applications. However, such techniques generally need appropriate datasets. Most available weed datasets only offer image-level annotation, i.e., each image is labelled with one weed species. However, in practice, one image can have multiple weed (and crop) species and/or multiple instances of one species. Consequently, the lack of instance-level annotations of the weed datasets puts a constraint on the applicability of powerful DL techniques. In the current research, we construct an instance-level labelled weed dataset. The images are sourced from a publicly available weed dataset, namely the Corn weed dataset. It has 5997 images of Corn plants and four types of weeds. We annotated the dataset using a bounding box around each instance and labelled them with the appropriate species of the crop or weed. Overall, the images contain about three bounding box annotations on average, while some images have over fifty bounding boxes. To establish the benchmark dataset, we evaluated the dataset using several DL models, including YOLOv7, YOLOv8 and Faster-RCNN, to locate and classify weeds in crops. The performance of the models was compared based on inference time and detection accuracy. YOLOv7 and its variant YOLOv7-tiny models both achieved the highest mean average precision (mAP) of 88.50% and 88.29% and took 2.7 and 1.43 ms, respectively, to classify crop and weed species in an image. YOLOv8m, a variant of YOLOv8, detected the plants in 2.2 ms with the mAP of 87.75%. Data augmentation to address the class imbalance in the dataset improves the mAP results to 89.93% for YOLOv7 and 89.39% for YOLOv8. The detection accuracy and inference time performed by YOLOv7 and YOLOv8 models in this research indicate that these techniques can be used to develop an automatic field-level weed detection system. • A crop-weed dataset is repurposed with object-level labels for species recognition. • Deep learning techniques have been applied to detect multi-class weeds in crops. • The performance was improved using data augmentation. [ABSTRACT FROM AUTHOR]

Oat (Avena sativa) is a quality feed crop for livestock with an expanding growth area of cultivation in China. Leaves of oat plants with necrotic spots were widely observed in the summer of 2019 in Tongwei county, China, with a rate of diseased leaves of 94.32% and a disease index of 69.96. Pathogenicity was tested by inoculating of oat plants with a conidia suspension of the isolate YMZZ2. Colony characteristics and conidial morphology of the isolates were determined on potato dextrose agar (PDA) and molecular identification of the isolate was conducted using phylogenetic analysis of the ITS, ACT, and HIS3 genes. The results showed that the morphological characteristics of the conidia were consistent with those described for species in the genus Cladosporium. Leaf spot symptoms appeared on all inoculated leaves within 5 days post-inoculation. At 10 days post-inoculation, all symptomatic leaves developed necrotic spots, each consisting of a brown or light brown border with a yellow halo. All control plants were asymptomatic. The morphological characteristics of the colony reisolated from the pathogenicity test lesions were morphologically similar to those of the isolate from the naturally infected leaves. Phylogenetic analysis of the gene sequences grouped them into a clade belonging to Cladosporium herbarum. To our knowledge, this is the first report of C. herbarum causing lesions on oat in China. • Necrotic spots with a diseased leaf rate of 94.32% were widely observed in Tongwei county, China. • The fungus was diagnosed as Cladosporium herbarum by morphological and phylogenetic analyses, and fulfilled Koch's postulate. • This is the first report of leaf spot caused by Cladosporium herbarum in China. [ABSTRACT FROM AUTHOR]

The main aim is to describe plant production systems and introduce bokashi technology for the advanced utilisation of food waste in Europe. The secondary objective is to demonstrate that the new Bokashi technology fertiliser products for field crops can improve soil fertility, plant health, yield and food quality in Europe. Bokashi technology is a straightforward yet innovative technology to recycle and valorise various kinds of biowaste into a nutrient-rich product that can be used as an organic fertiliser. Bokashi technology is a method for treating biowaste in general and food waste in specific, using controlled lactic acid fermentation (LAF) under anaerobic conditions. The term is based on a traditional Japanese method of the same name. The acidic and anaerobic environment quickly suppresses the viability of pathogens so even contaminated biowaste can be recycled. The Bokashi technology is almost absent in Europe at the moment. Conclusion: Bokashi technology – coming from Asia (Japan) – is an innovative technology to improve soil fertility, plant health, yield and food quality. This technology has a huge potential to be applied in Europe, but the right methodology should be developed based on the knowledge of temperature regimes for effective microorganisms that will influence the crop production. [ABSTRACT FROM AUTHOR]

Background: Productivity and quality of crops can be increased through polyploidy plants induced by colchicine. The use of colchicine has never been applied to Katokkon pepper, a local red pepper variety from North Toraja Indonesia. This pepper is characterized by its unique shape like the small-bell pod type of paprika and its strong spicy taste. Therefore, this study aimed to determine the effects of colchicine treatment on the ploidy level and morphological characters of Katokkon pepper. Results: Flow cytometer analysis showed that all colchicine concentration treatments ranging from 0.025 to 0.1% with 24 h immersion time generated two plant groups based on their ploidy level; 50% of the total treatment plants was diploid while the rest plants were mixoploid. All colchicine treatment plants were significantly different in their width of stomata guard cells from the control plant. The effect of colchicine was also significantly on the plant height, thickness of fruit flesh, and the number of fruits per plant. Conclusions: The results of this study showed that colchicine treatment had a significant effect on the ploidy level and several morphological characters of the Katokkon pepper. The colchicine treatment increased the number of fruits per plant and thicker flesh of fruits but reduced the size and weight of Katokkon pepper. Our findings provide essential information to obtain tetraploid Katokkon plants through colchicine treatment in further research. This study benefits as a preliminary step for increasing the productivity and quality of the local red peppers in Indonesia. [ABSTRACT FROM AUTHOR]

Copyright of Progress in Plant Protection is the property of Institute of Plant Protection and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper highlights the changes brought about by artificial intelligence (AI) systems on business. Developments in this area can complement the human decisionmaking process. Artificial intelligence can reshape companies, management and the innovation process. AI will continue to develop and modify the activity in various economic sectors, due to the high degree of adoption of new automation possibilities. Businesses will have more and more automated processes, but new processes will appear, in which human experts will be also needed. For example, AI may be used in the agricultural sector and may help farmers in their activities, at sowing, in crop and weather monitoring, pest control, harvesting, crop rotation, etc. Thus, AI may help farmers to adopt measures to improve crop quality. [ABSTRACT FROM AUTHOR]

• Critique of classical post-harvest loss (PHL) assessments. • Need to contextualize handling practices when tackling PHL. • Fresh-fruit chain actors' losses can be a source of benefits for interlinked actors. • Quality understandings emerge from context-specific value negotiations. • Findings disrupt narratives of high PHL in the Global South. Post-harvest loss (PHL) assessments are usually commodity-oriented and approaches for reducing these losses tend to rely on technological interventions for improving product handling and quality. However, such narrow problem framing neglects the situatedness of PHL, particularly when perishable crops of heterogeneous quality are traded through networks of small-scale value chain actors, as is common in the Global South. To illustrate our critique, we lean on systems as a conceptual construct to unpack PHL with the empirical example of the pineapple value chain in Uganda. Specifically, we aim (i) at surfacing multiple actors' perspectives related to the spectrum of losses and (ii) to improve understanding of their strategies to deal with related problems. We draw from participant observation, semi-structured interviews and group meetings with farmers, brokers, traders and interlinked actors such as small-scale processors and pig farmers. Our study shows how they navigate existing resource constraints and how their practices to recover potential food losses are embedded in their local food systems. The contextualized understandings of quality and the consequences of what can be considered a loss, challenge dominant narratives about high levels of PHL in Sub-Saharan Africa. Extending system boundaries beyond the fresh-fruit value chain revealed how interlinked actors were able to benefit from using 'damaged' fruits. Our findings encourage going beyond generic quality systems towards more diverse understandings of quality and loss. Investigating losses in a systemic and context-specific way can put local perspectives at the forefront to encourage actor-driven approaches for sustainable PHL reduction. [ABSTRACT FROM AUTHOR]

Copyright of Progress in Plant Protection is the property of Institute of Plant Protection and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The plant growth regulators are the organic chemical compounds, other than nutrients and vitamins, which modify or regulate physiological processes in an appreciable measure in the plant when used in small concentration. They are known to influence fruit set, retention, yield and quality of fruit crops. Fruit trees are considered high-value crops and even small modifications in production efficiency, product quality or enhanced cosmetic appeal have the potential to significantly increase product value. Application of plant growth regulators results in better output as it improves the internal physiology of developing fruits to improve fruit set, reduce fruit drop and to amend various physiological disorders in order to improve quality and yield. In this review, we have discussed about impact of various plant growth regulators on fruit set, retention, quality and yield of fruit crops. [ABSTRACT FROM AUTHOR]

Agriculture was the most prominent occupation in India once upon a time. Nowadays due to various changes in technology, living style, trends, environment, and climate changes 80% of the population are not willing to be a farmer anymore. If this continues what could be the state of tomorrow's world. In today's world, the same technology can be implemented in the agriculture sectors for optimizing the output and fetching the quality crop. For retrieving best harvest some solutions based on images taken by satellites can be analyzed and appropriate prescriptions are given for the crops. This work proposes a system to identify the quality of the crop, fungus detection and infected areas by surveying based on image pattern recognition by collecting the pictures are taken by UAVs (Unmanned Aerial Vehicle); these images are very close to the crops for detecting weeds, differentiating the dried, healthy leaves and identifying the ripen fruits with different levels of intersections. This solution achieves a 50% growth in the harvest and quality of the crop. These technologies should again pave a way for agriculture to be a major occupation in India. [ABSTRACT FROM AUTHOR]

Prohydrojasmon has been reported to improve the quality of crops. However, most previous studies have investigated its application on fruits. Here, we evaluated the effect of prohydrojasmon on the growth and total phenolic content, anthocyanin content, and antioxidant activity in komatsuna (Brassica rapa var. periviridis) and lettuce (Lactuca sativa L.). Prohydrojasmon did not show any serious inhibitory effect. Prohydrojasmon applied to komatsuna at a concentration of 0.5 µM significantly increased the total phenolic content and anthocyanin content, and a concentration of 1 µM increased the antioxidant activity. In lettuce, prohydrojasmon at a concentration of 400 µM significantly increased the total phenolic content and anthocyanin content, while a concentration of 0.5 µM significantly increased the antioxidant activity. These results suggest that prohydrojasmon positively affects the phenolic compound and anthocyanin accumulation and antioxidant activity in komatsuna and lettuce without adversely affecting growth. Prohydrojasmon positively affects the phenolic compound and anthocyanin accumulation and antioxidant activity in komatsuna and lettuce. [ABSTRACT FROM AUTHOR]

Grain producers are challenged to maximize crop production while utilizing nutrients efficiently and minimizing negative impacts on water quality. There is a particular concern about nutrient export to the Gulf of Mexico via loss from subsurface drainage systems. The objective of this study was to investigate the effects of crop rotation, tillage, crop residue removal, swine manure applications, and cereal rye (Secale cereale L.) cover crops on nitrate‐N (NO3‐N) and total reactive phosphorus (TRP) loss via subsurface drainage. The study was evaluated from 2008 through 2015 using 36 0.4‐ha plots outfitted with a subsurface drainage water quality monitoring system. Results showed that when swine manure was applied before both corn (Zea mays L.) and soybean [Glycine max (L.) Merr.], drainage water had significantly higher 8‐yr‐average flow‐weighted NO3‐N concentrations compared with swine manure applied before corn only in a corn–soybean rotation. The lowest NO3‐N loss was 15.2 kg N ha−1 yr−1 from a no‐till corn–soybean treatment with rye cover crop and spring application of urea‐ammonium nitrate (UAN) to corn. The highest NO3‐N loss was 29.5 kg N ha−1 yr−1 from swine manure applied to both corn and soybean. A rye cover crop reduced NO3‐N loss, whereas tillage and residue management had little impact on NO3‐N loss. Losses of TRP averaged <32 g P ha−1 yr−1 from all treatments. Corn yield was negatively affected by both no‐till management and cereal rye cover crops. Results showed that cropping management affected N leaching but impacts on P leaching were minimal. [ABSTRACT FROM AUTHOR]

Long-term cold storage poses a threat of cold stress to postharvest crops, leading to the development of chilling injury and a reduction in quality and shelf life. Nitric oxide (NO) has emerged as a key cellular molecule for defensing chilling stress and alleviating postharvest chilling injury by regulating various mechanisms at both the molecular and physiological levels. This comprehensive review summarizes the symptoms and factors affecting chilling injury development, the effects of NO in alleviating postharvest chilling injury, the mechanism of NO-alleviated chilling injury, and the regulatory mechanism of NO-enhanced chilling tolerance, along with its interactions with other signaling molecules, such as ethylene, abscisic acid, melatonin, hydrogen sulfide, and hydrogen peroxide. Our study highlights the crucial role of NO in mitigating chilling injury in crops and identifies various mechanisms, both at the molecular and physiological levels, through which NO works. Additionally, our research sheds light on the regulatory mechanism of NO-enhanced chilling tolerance in postharvest crops. We have discovered that NO interacts with several important signaling molecules to enhance the chilling tolerance of stored fruits and vegetables. By providing a comprehensive overview of the symptoms and factors affecting chilling injury development, along with the effects and mechanisms of NO in alleviating chilling injury, this review serves as a theoretical reference for reducing chilling injury and enhancing the quality of postharvest crops during long-term cold storage. [ABSTRACT FROM AUTHOR]

Abstract Crops are prone to various types of stress, such as caused by heavy metals, drought and pest/disease, during their life cycle. Heavy metal stress in crops poses a serious threat to crop quality and human health. However, differentiating between heavy metal and non-heavy metal stress presents a great challenge, since responses to environmental stress in crops are complex and uncertain, with different stressors possibly triggering similar canopy reflectance responses. This study aims to infer the occurrence probability of heavy metal stress (i.e., Cd stress) on a regional scale by integrating satellite-derived vegetation index and spatio-temporal characteristics of different stressors with a Bayesian method. The study area is located in the Hunan Province, China. Seven scenes of Sentinel-2 satellite images from 2016 to 2017 were collected, as well as Cd concentrations in the soil. First, the probability of rice being stressed was screened using the normalized difference red-edge index (NDRE) at all the growth stages of rice. Further, the stressed rice was used as input, along with the coefficients of spatio-temporal variation (CSTV) derived from NDRE, for a Bayesian method to infer rice exposed to Cd pollution. The results demonstrated that NDRE was a sensitive indicator for assessing stress levels in rice crops. The CSTV with a threshold of 2.7 successfully detected rice under Cd as well as abrupt stress on a regional scale. A high map accuracy for Cd induced stress in rice was achieved with an accuracy of 81.57%. This study suggests that vegetation index obtained from satellite images can assist in capturing crop stress, and that the used Bayesian method can be very useful for distinguishing a specific stressor in crops by incorporating temporal-spatial characteristic of different stressors in crops into satellite-derived vegetation index. Graphical abstract Image 1 Highlights • Normalized difference red-edge index was used to detect stressed and unstressed rice. • Spatio-temporal characteristic of different stressors as prior information can distinguish a specific stressor in rice. • Bayesian method was used to map the occurrence probability of heavy metal stress (i.e., Cd stress). [ABSTRACT FROM AUTHOR]

The aim of this research was to assess the effect of leguminous pre-crop species on quality characteristics of subsequent spring and winter cereals. The experiment was carried out in an organic crop rotation in north-eastern Europe. The influence of biomass dry matter yield, carbon and nitrogen content of perennial red clover (Trifolium pratense L.), alsike clover (Trifolium hybridum L.), Washington lupin (Lupinus polyphyllus Lindl.), biennial white sweet clover (Melilotus albus L.), annual crimson clover (Trifolium incarnatum L.) and Alexandria clover (Trifolium alexandrinum L.) on protein concentration, bulk density and thousand grain weight of subsequent cereals were assessed. Barley, oats, winter rye, spring and winter wheat were grown in the first post-legume year and barley and oats in the second post-legume year. In the first year after perennial and biennial pre-crops, the protein concentration of the cereals, except for rye, increased by 0.8-2.6 percentage points compared with the control. The largest increases in protein concentration of the cereals were after red and alsike clovers. The legumes increased the bulk density of all of the cereals, while thousand kernel weights were increased only for barley, spring and winter wheat. All the legume species had a positive second-year after-effect on the protein concentration of barley and oats, with the largest effect after red and alsike clover at 1.0-1.3 percentage points. The results showed that all of the leguminous pre-crops were suitable for increasing the quality of cereals. The effect was greater after perennial and biennial species compared with the annual species. [ABSTRACT FROM AUTHOR]

Abstract The controlled-release fertilizers (CRFs) are the fertilizers which gradually release their mineral nutrients, while at the same time providing proper nutrition to the plants. The CRFs have been widely used to improve crops' quality and quantity, but have not been applied on Freesia hybrida so far. In this study, Freesia 'Shangnong Ruxiang' was used to determine the effect of CRFs on its vegetative and reproductive growth. Two different kinds of commercial CRFs i.e. Osmocote Classic and Agroblen were applied in various doses. In an appropriate dose, Osmocote Classic and Agroblen improved the growth of Freesia. The overall effect of Agroblen is better than Osmocote. Under treatments of 5 g/L Osmocote and 5 g/L or 7 g/L Agroblen, the plant height, scape height, scape number, scape strength, and chlorophyll content were higher than other groups. Under 7 g/L Agroblen, the plants blossomed 10 days earlier than that of control. Taken together, 5 g/L of Osmocote and 5 g/L or 7 g/L of Agroblen are recommended for strong leaf and corm growth with good flowering phenotype, while 5 g/L or 7 g/L of Agroblen only is optimum for early flowering in Freesia. [ABSTRACT FROM AUTHOR]

• Batch effects in GBS give strong signals in genetic parameter estimates. • Large variation in fecundity across years and genotypes. • Frost tolerance is reflected by parental origin but to a smaller degree than expected. Extensive gene flow can be detrimental to local adaptation and similarly, forestry seed sources such as seed orchards can be heavily influenced by external pollination, especially if the orchard material has been translocated a great distance. Here we conducted a coordinated genotyping-phenotyping study to examine how external pollination events and fecundity variation in a Pinus sylvestris seed orchard influence the genetic composition and the seed-lots' autumn frost hardiness when genetic material had been translocated 630 km south. The results were then compared to those of a in situ established seed orchard. We genotyped and phenotype >1000 seedlings from these orchards, and constructed their pedigrees and scored their autumn frost tolerance in a controlled climate chamber environment. The hardiness scores were compared with a reference of nine natural stands along a latitudinal cline. We find substantial variation in fecundity and external pollination over crop years, thus unpredictable genetic composition because the contribution of some orchard clones is high in one crop but low in another. We observed that seedlings produced by mating among orchard genotypes were less hardy than expected (corresponding to an origin of −0.6°N) but the opposite in externally pollinated seedlings (+0.3 to +0.7°N). The freeze damage levels reflect the origin of parental genotypes, but to a smaller degree than expected (13% lower than expected damage levels for externally pollinate seedlings and 21% greater damage levels for internally pollinates seedlings). These results suggest that orchard parents' origins, mating composition and orchard local environment could all affect the seed crops' quality and their climate adaptation. Seed orchard crops are the key to realize the gain in forestry from breeding efforts. However, genetic monitoring of seed crops is necessary to improve the performance of seed orchards further and adjust deployment areas of seed crops in a timely manner for a more dynamic forestry, considering climate change and biodiversity demands. [ABSTRACT FROM AUTHOR]

Abstract Insect pollination improves the yield and quality of many crops, yet there is increasing evidence of insufficient insect pollinators limiting crop production. Effective Integrated Crop Pollination (ICP) involves adaptable, targeted and cost-effective management of crop pollination and encourages the use of both wild and managed pollinators where appropriate. In this study we investigate how the addition of honeybee hives affects the community of insects visiting oilseed rape, and if hive number and location affect pollinator foraging and oilseed rape pollination in order to provide evidence for effective ICP. We found that introducing hives increased overall flower visitor numbers and altered the pollinator community, which became dominated by honeybees. Furthermore a greater number of hives did not increase bee numbers significantly but did result in honeybees foraging further into fields. The timing of surveys and proximity to the field edge influenced different pollinators in different ways and represents an example of spatial and temporal complementarity. For example dipteran flower visitor numbers declined away from the field edge whereas honeybees peaked at intermediate distances into the field. Furthermore, no significant effects of survey round on wild bees overall was observed but honeybee numbers were relatively lower during peak flowering and dipteran abundance was greater in later survey rounds. Thus combining diverse wild pollinators and managed species for crop pollination buffers spatial and temporal variation in flower visitation. However we found no effect of insect pollination on seed set or yield of oilseed rape in our trial, highlighting the critical need to understand crop demand for insect pollination before investments are made in managing pollination services. [ABSTRACT FROM AUTHOR]

This research was conducted to investigate the natural, quantitative composition of the most common Fusarium species directly in fields of northeastern Poland. The concentration of Fusarium spp. and grain quality traits (yield, 1,000 kernel weight, test weight, grain moisture, ergosterol content, protein content, gluten content and starch content) were compared in four wheat varieties (Mandaryna, Struna, Kandela and Arabella). Obtained results indicated a relation between grain moisture, test weight, ergosterol content, yield and fungi concentration. Protein, starch and gluten content was similar in all wheat varieties. Fusarium culmorum was the most common pathogen in Mandaryna and Struna and F. graminearum in Kandela and Arabella. Fusarium avenaceum and F. poae occurred in low amounts in all wheat varieties except Mandaryna. Fusarium oxysporum was found in comparable concentrations in Struna, Kandela and Arabella. Struna despite medium Fusarium spp. colonization possessed the most desirable grain quality compared to other varieties. We carried out real-time PCR detection of Fusarium spp. which is an efficient, cost effective and time saving method in evaluating the development of fungal diseases which are not visible in standard observations. [ABSTRACT FROM AUTHOR]