bluuebunny/crossref_metadata_2025 huggingface dataset:
Agent-Based Modeling to Improve Beef Production from Dairy Cattle: Model Description and Evaluation
Authors: Addis Addisu H. , Blair Hugh T. , Kenyon Paul R. , Morris Stephen T. , Schreurs Nicola M. , Garrick Dorian J.
Published: October 2022

Agent-based modeling (ABM) enables an in silico representation of complex systems and captures agent behavior resulting from interaction with other agents and their environment. This study developed an ABM to represent a pasture-based beef cattle finishing systems in New Zealand (NZ) using attributes of the rearer, finisher, and processor, as well as specific attributes of dairy-origin beef cattle. The model was parameterized using values representing 1% of NZ dairy-origin cattle, and 10% of rearers and finishers in NZ. The cattle agent consisted of 32% Holstein-Friesian, 50% Holstein-Friesian–Jersey crossbred, and 8% Jersey, with the remainder being other breeds. Rearers and finishers repetitively and simultaneously interacted to determine the type and number of cattle populating the finishing system. Rearers brought in four-day-old spring-born calves and reared them until 60 calves (representing a full truck load) on average had a live weight of 100 kg before selling them on to finishers. Finishers mainly attained weaners from rearers, or directly from dairy farmers when weaner demand was higher than the supply from rearers. Fast-growing cattle were sent for slaughter before the second winter, and the remainder were sent before their third winter. The model finished a higher number of bulls than heifers and steers, although it was 4% lower than the industry reported value. Holstein-Friesian and Holstein-Friesian–Jersey-crossbred cattle dominated the dairy-origin beef finishing system. Jersey cattle account for less than 5% of total processed beef cattle. Further studies to include retailer and consumer perspectives and other decision alternatives for finishing farms would improve the applicability of the model for decision-making processes.
Non-Destructive Viability Discrimination for Individual Scutellaria baicalensis Seeds Based on High-Throughput Phenotyping and Machine Learning
Authors: Tu Keling , Cheng Ying , Ning Cuiling , Yang Chengmin , Dong Xuehui , Cao Hailu , Sun Qun
Published: October 2022

It is crucial to identify and select high-quality seeds for improving Scutellaria baicalensis yield. In this study, we present a non-destructive and accurate method for predicting Scutellaria baicalensis seed viability that used seed phenotypic data with machine-learning algorithms to distinguish between vital and dead seeds. Meanwhile, the SMOTE was used to balance the dataset and make the established viability discrimination model more efficient by avoiding problems of overfitting or under-fitting. The results showed that hyperspectral imaging (HSI) combined with detrend (DT) preprocessing and a support vector machine (SVM) model could predict Scutellaria baicalensis seed viability with a 93.3% accuracy, and increased the germination percentage of the seed lot to 99.1%, while machine vision imaging provided the highest 87.9% accuracy and 87.0% germination percentage. This strategy is suitable for large-scale Scutellaria baicalensis seed viability discrimination operations for ensuring seed quality, expanding the cultivation and production scales of Scutellaria baicalensis, and accelerating the present solving of the problem of short supply. It can help to accelerate the breeding of quality Scutellaria baicalensis varieties.
Near-Infrared Model and Its Robustness as Affected by Fruit Origin for ‘Dangshan’ Pear Soluble Solids Content and pH Measurement
Authors: Cheng Tao , Guo Sen , Pan Zhenggao , Fan Shuxiang , Ju Shucun , Xin Zhenghua , Zhou Xin-Gen , Jiang Fei , Zhang Dongyan
Published: October 2022

Soluble solid content (SSC) and acidity (pH) are two important factors indicating the fruit quality of pears and can be measured by near-infrared spectroscopy (NIRS). However, the robustness of these measurements as affected by different origins of pears remains largely unknown. In this study, we developed an NIRS method to measure ‘Dangshan’ pear (Pyrus spp.) SSC and pH and evaluated the robustness of this non-destructive detection method by examining the effects of pears from three different origins in 2019 and 2020. First, the Kennard–Stone method was used to divide the calibration set of the 2020 pear samples from different orchards. The partial least squares (PLS) model was used to establish the local origin and hybrid origin models to predict the pears’ SSC and pH. Second, a combination of competitive adaptive reweighted sampling (CARS), successive projections algorithm (SPA), and uninformative variable elimination (UVE) was implemented to construct spectral prediction models based on effective variables for assessing the pears’ SSC and pH from local and hybrid origins. The results showed that the local origin detection model produced large errors in predicting the SSC and pH of pears from different origins, and the model, established based on the pear samples of three origins, performed better than the local origin and other hybrid origin models. Finally, the model could be effectively simplified using 70 and 52 characteristic variables selected by the CARS method. Pear samples harvested from three different orchards in 2019 were used as an independent set to verify the validity of the selected characteristic variables. The results showed that the predicted R2p for the SSC and pH measurements of pears of three different origins were more than 0.9 and 0.85, respectively. This finding indicates that the difference in the origin of pears has an important influence on the quantitative inversion of pear SSC and pH measurements, and the combination of the hybrid origin model constructed based on the characteristic variables can improve the prediction accuracy. These findings provide an important theoretical basis for the development of rapid detection devices for the measurements of pears’ SSC and pH.
Production Capacity Evaluation of Farmland Using Long Time Series of Remote Sensing Images
Authors: Lu Mei , Gu Xiaohe , Sun Qian , Li Xu , Chen Tianen , Pan Yuchun
Published: October 2022

Farmland is a crucial resource for the survival and evolution of humans. The accurate evaluation of farmland production capacity (FPC) is of great significance for planting structure optimization, the improvement of low-yield farmland and sustainable utilization. The objective of this study is to quantitatively evaluate the FPC at the county scale using time series remote sensing (RS) images. Taking winter wheat as a benchmark crop, the relations between annual yield and the Normalized Difference Vegetation Index (NDVI) were established by a multiple linear regression algorithm. The mean and standard deviations (SD) of the multi-year yield of winter wheat were used to evaluate FPC and its instability using the farmland parcels as the basic unit. The results show that the estimation model for annual winter wheat yield performed best in 2011. The R2 of the modeling sample was 0.93, and the RMSE of the testing sample was 368.1 kg/ha. The FPC grades in the south and north of the study area were relatively high with a good stability, while those in the center were low with poor stability. There was a certain correlation between FPC and soil organic matter (SOM), and the correlation coefficient was 0.525 (p < 0.01). In this study, taking the farmland parcel as a basic unit instead of a pixel, long time series of multi-source RS images with medium resolution were used to monitor the per unit yield of benchmark crops and then evaluate the FPC. This can provide a method for the rapid evaluation of FPC at the county scale.
Ultraviolet Radiation Management in Greenhouse to Improve Red Lettuce Quality and Yield
Authors: Lycoskoufis Ioannis , Kavga Angeliki , Koubouris Georgios , Karamousantas Dimitrios
Published: October 2022

The intensity of ultraviolet (UV) radiation affects the yield and quality of red lettuce. The current study aimed to develop a UV management system in a greenhouse to achieve high yield and quality in red lettuce production. The study consisted of two experiments. In the first experiment, the effects of the different UV transparencies of the plastic materials covering the greenhouse on plant growth and the concentration of antioxidants in red lettuce were studied. For this purpose, two greenhouses were covered with polyethene of different transparencies to UV radiation. One greenhouse was covered with a common type of polyethene transparent in a large spectrum of UV radiation (UV-open), while the second greenhouse was covered with polyethene untransparent to ultraviolet radiation (UV-block). The plants were grown in a deep flotation hydroponic system. At the end of the cultivation, plant growth measurements, leaf colour measurements, and the determination of antioxidant components’ concentration were carried out. Red lettuce plants harvested 42 days after planting had an average head weight 42% greater in the UV-block greenhouse compared to plants grown in the UV-open greenhouse. However, the red leaf colour of plants in the UV-block greenhouse lagged significantly compared to that in the UV-open greenhouse. Moreover, the total phenolic content, the total flavonoid content, and the antioxidant capacity of the lettuce leaves in the UV-block greenhouse were significantly lower compared to the corresponding values of the plants in the UV-open greenhouse. During the second experiment, a new cultivation system of red lettuce, which combined a UV-block polyethene film as a greenhouse cover and a pre-harvested supplemental UV light, was tested. For this purpose, various doses of supplemental UV lighting were tested in the UV-block greenhouse for ten days prior to harvest. From these tests, it emerged that applying supplemental UV lighting with a dose of 425 kJ m−2 d−1 for ten days before harvest produces red lettuces of the same quality as those produced in a UV-open greenhouse. This technique of growing red lettuce increases its yield by 30% without a negative effect on the quality of the product.
Isolation of Lignin from Anaerobically Digested Unhydrolyzed Solids Produced in a Biorefinery
Authors: Tang Xiaoyu , Zhao Kunyang , Gao Chunlin , Gao Xionghui , Balan Venkatesh , Wang Wenguo
Published: October 2022

About 30–40% of lignin-rich unhydrolyzed solids (UHS) are left behind after subjecting lignocellulosic biomass to thermochemical pretreated processes followed by enzymatic hydrolysis (EH) to produce sugars that are fermented to fuels and chemicals in a biorefinery. Ammonia Fiber Expansion (AFEX) is one of the leading alkaline pretreatment processes that use volatile ammonia that can be recovered and reused beneficially for the environment. In this work, we used AFEX-EH-UHS which are produced after subjecting corn stover to AFEX followed by EH and contain carbohydrates, ashes, and other impurities that are detrimental to the conversion of lignin to high-value products. In the study, we discovered that ~80% of the carbohydrates present in AFEX-EH-UHS were hydrolyzed and consumed during the AD process. The resulting solids, hereafter called AD-UHS, were subjected to lignin extraction using different combinations of solvents under reflux conditions. The solvent-extracted lignin was subjected to thermogravimetry, nuclear magnetic resonance (NMR) spectroscopy, and molecular weight analysis. Among the solvents, acetic acid could produce 95% pure lignin with some chemical modification, while aqueous ethanol was able to produce 80% pure lignin without any chemical modification.
Preliminary Evidence Regarding the Detection of Cortisol and IL-6 to Assess Animal Welfare in Various Rabbit Housing Systems
Authors: Bozzo Giancarlo , Dimuccio Michela Maria , Casalino Gaia , Ceci Edmondo , D’Amico Francesco , Petrontino Alessandro , Bonerba Elisabetta , Camarda Antonio , Circella Elena
Published: October 2022

The main purpose of this study was to investigate the influence of three different rabbit rearing systems using animal-based measures. Therefore, in 2021, cortisol and IL-6 were assessed during a Biosecurity System pilot program to evaluate rabbit welfare, based on EU Regulation 2016/429 in the Apulia region of Southern Italy. The study was carried out on a total of 45 meat-type rabbits, divided into 3 groups of 15 subjects, aged approximately 60–65 days. Groups came from either: (i) a niche open-air system; (ii) a niche system in a shed with conventional cages, or (iii) an industrial system. Based on our findings, the rearing system had a significant effect (p < 0.0001) on the animal-based measures. Cortisol levels were higher in rabbits raised in the niche open-air system compared to those raised in both the niche system located in a shed with conventional cages and the industrial system (11.91 vs. 2.86 and 2.72 ng/mL, respectively). Likewise, IL-6 values were higher in rabbits from the niche open-air system (45.80 ng/mL) and lower in rabbits from the niche system in a shed with conventional cages and those from the industrial system (23.30 and 16.80 ng/mL, respectively). According to the results of the stress indicators measured, cortisol and IL-6, rabbit welfare and meat quality may be affected by rabbit rearing systems.
Research on pH Value Detection Method during Maize Silage Secondary Fermentation Based on Computer Vision
Authors: Ren Xianguo , Tian Haiqing , Zhao Kai , Li Dapeng , Xiao Ziqing , Yu Yang , Liu Fei
Published: October 2022

pH value is a crucial indicator for evaluating silage quality. In this study, taking maize silage as the research object, a quantitative prediction model of pH value change during the secondary fermentation of maize silage was constructed based on computer vision. Firstly, maize silage samples were collected for image acquisition and pH value determination during intermittent and always-aerobic exposure. Secondly, after preprocessing the acquired image with the region of interest (ROI) interception, smoothing, and sharpening, the color and texture features were extracted. In addition, Pearson correlation analysis and RF importance ranking were used to choose useful feature variables. Finally, based on all feature variables and useful feature variables, four regression models were constructed and compared using random forest regression (RFR) and support vector regression (SVR): RFR model 1, RFR model 2, SVR model 1, and SVR model 2. The results showed that—compared with texture features—the correlation between color features and pH value was higher, which could better reflect the dynamic changes in pH value. All four models were highly predictive. The RFR model represented the quantitative analysis relationship between image information and pH value better than the SVR model. RFR model 2 was efficient and accurate, and was the best model for pH prediction, with Rc2, Rp2, RMSEC, RMSEP, and RPD of 0.9891, 0.9425, 0.1758, 0.3651, and 4.2367, respectively. Overall, this study proved the feasibility of using computer vision technology to quantitatively predict pH value during the secondary fermentation of maize silage and provided new insights for monitoring the quality of maize silage.
Influences of Government Policies and Farmers’ Cognition on Farmers’ Participation Willingness and Behaviors in E-Commerce Interest Linkage Mechanisms during Farmer–Enterprise Games
Authors: Wei Xiaolu , Ruan Junhu
Published: October 2022

E-commerce interest linkage mechanisms serve as an effective solution to the problems of farmer–market cooperation, agricultural supply-side reforms, and farmers’ income growth. This study, guided by the theory of planned behavior, undertook an evolutionary game analysis of farmer–enterprise cooperation with government interventions with farmers. Based on data from 554 questionnaires administered in Mei County, Shaanxi Province, China, this study found a difference between the realistic and optimal choices of farmers. In addition, this study used a structural equation model to investigate the influence of government policies and farmers’ cognition on the participation willingness and behaviors of farmers in e-commerce interest-linkage mechanisms. The results showed that the optimal choice for farmers in a farmer–enterprise cooperative game is participation in e-commerce, and government policies can be used to improve farmer–enterprise e-commerce interest-linkage mechanisms. Farmers’ basic characteristics and experiences impacted their cognition of e-commerce, which, in turn, had a significant positive effect on their e-commerce participation willingness and behaviors. Government policies had a positive effect on farmers’ experiences, cognition of e-commerce, and participation behaviors, but no direct positive impact on farmers’ willingness to participate. Government policies and farmers’ basic characteristics interacted and acted together on the participation willingness and behavior of farmers.
Effects of Host Ages and Release Strategies on the Performance of the Pupal Parasitoid Spalangia endius on the Melon Fly Bactrocera cucurbitae
Authors: Liu Jian-Feng , Wu Cheng-Xu , Idrees Atif , Zhao Hai-Yan , Yang Mao-Fa
Published: October 2022

The melon fly, Bactrocera cucurbitae Coquillett (Diptera: Tephritidae), is a quarantine pest that causes considerable economic losses to growers of vegetables and fruits worldwide. Spalangia endius (Walker) (Hymenoptera: Pteromalidae) is a solitary pupal parasitoid of various dipteran hosts. We assessed the impact of the host pupal age (2, 4, and 6 days old), host density (5, 10, 15, 20, 25, and 30), and parasitoid density (1, 2, 3, 4, 5, and 6) on the parasitizing potential of S. endius on B. cucurbitae pupae under laboratory conditions. The effects of different soil depths on the parasitism rate of S. endius and the dispersal behavior of S. endius at distances of 0, 5, and 10 m were also evaluated under field conditions. The results showed that the parasitism rate of S. endius significantly increased with the increase in host density and parasitoid density. The host pupal age did not influence the number of pupae parasitized by S. endius and the progeny sex ratio of S. endius under different host densities. In the fields, different puparium burial depths of B. dorsalis pupae significantly influenced the parasitism and eclosion rates of S. endius but did not influence the eclosion rate of B. cucurbitae. Spalangia endius females could travel up to 10 m to parasitize at a similar rate on B. cucurbitae pupae compared to S. endius placed at 0 m.
Improved Cotton Seed Breakage Detection Based on YOLOv5s
Authors: Liu Yuanjie , Lv Zunchao , Hu Yingyue , Dai Fei , Zhang Hongzhou
Published: October 2022

Convolutional neural networks have been widely used in nondestructive testing of agricultural products. Aiming at the problems of missing detection, false detection, and slow detection, a lightweight improved cottonseed damage detection method based on YOLOv5s is proposed. Firstly, the focus element of the YOLOv5s backbone network is replaced by Denseblock, simplifying the number of modules in the backbone network layer, reducing redundant information, and improving the feature extraction ability of the network. Secondly, the collaborative attention (CA) mechanism module is added after the SPP pooling layer, and a large target detection layer is reduced to guide the network to pay more attention to the location, channel, and dimension information of small targets. Thirdly, Ghostconv is used instead of the conventional convolution layer in the neck feature fusion layer to reduce the amount of floating-point calculation and speed up the reasoning speed of the model. The CIOU loss function is selected as the border regression loss function to improve the recall rate of the model. Lastly, the model was verified using an ablation experiment and compared with the YOLOv4, Yolov5s, and SSD-VGG16 network models. The accuracy, recall rate, and map value of the improved network model were 92.4%, 91.7%, and 98.1%, respectively, and the average recognition time of each image was 97 fps. The results show that the improved network can effectively solve the problem of missing detection, reduce false detection, and have better recognition performance. This method can provide technical support for real-time and accurate detection of damaged cottonseed in a cottonseed screening device.
Does Construction of High-Standard Farmland Improve Recycle Behavior of Agricultural Film? Evidence from Sichuan, China
Authors: Xu Rong , Zhan Yating , Zhang Jialan , He Qiang , Zhang Kuan , Xu Dingde , Qi Yanbin , Deng Xin
Published: October 2022

Recycle behavior of agricultural film (RBAF) plays an important role in protecting the ecological environment of farmland. Improving RBAF has become an urgent choice for agricultural countries to achieve sustainable development. Construction of high-standard farmland (CHSF) is defined as the artificial improvement of farmland facilities and considered beneficial to agricultural production and farmland environment. This study aims to evaluate the role of CHSF in improving RBAF. Based on survey data of rural areas in Sichuan, China, this study explores quantitative impacts of CHSF on RBAF by econometric model. The results are as follows: (1) There is a positive impact of CHSF on RBAF, i.e., compared with the farmers not participating in CHSF, the possibility of RBAF for the farmers participating in CHSF is increased by 16%. (2) For every 1% increase in the proportion of agricultural labor force in households, the possibility of RBAF is increased by 0.2%. Thus, this study indicates that governments should focus on improving rural infrastructure to help farmers improve their behavior towards environmental protection.
Heat Stress, Varietal Difference, and Soil Amendment Influence on Maize Grain Mineral Concentrations
Authors: Chukwudi Uchechukwu Paschal , Mavengahama Sydney , Kutu Funso Raphael , Motsei Lebogang Ezra
Published: October 2022

Improving the mineral concentrations of maize (Zea mays L.) will aid in the reduction of malnutrition in low-resource households that consume maize-based meals regularly. The study’s objective was to compare how different soil amendments and heat-stressed environments affect grain yield and mineral concentrations in maize. The study involved heat-stressed (HS) and non-heat-stressed (NHS) environments, three maize varieties (WE3128, WE5323, and ZM1523), and three soil amendments. The essential minerals analysis of the grain revealed a significant effect of variety, soil amendment, and heat stress on the grain yield and mineral contents. Among soil amendments, mineral fertilizer amendment (MF) gave the highest grain Zinc (Zn), 37.95 ± 15.3 µg/kg, while the highest grain iron (Fe) (136.9 ± 51.3 µg/kg) and yield were obtained with a combination of mineral fertilizer/poultry-manure amendment (MPM). The treatment interactions containing MPM in both the HS and NHS environments consistently produced positive results in the three maize varieties. When compared with the non-heat-stressed environment, the heat-stressed environment reduced grain weight (GWt) by 378% while increasing grain Fe and Zn concentrations by 43.6% and 15.8%, respectively. The HS was significantly higher than the NHS by 14.6%, 34.0%, 1.5%, 11.0%, 1.9%, and 89.2% for Ca, Cl, Mg, Na, P, and S, respectively. The highest macromineral concentrations were found in WE5323. All of the NHS treatments were grouped together, with the exception of NHS-4 and NHS-7, which produced the lowest means for the number of grain and GWt in the NHS, respectively. Although the variety was inconsistent in separating the treatment interactions, there was a good level of consistency in separating the treatment interactions along the heat stress factor and soil amendment factor. The correlation results revealed that a proportional relationship between Fe and Zn and grain yield tends to decrease the grain Fe or Zn concentrations. Therefore, selecting for high grain yield only may result in lower Fe and Zn concentrations in the grain. WE5323, amended with MPM, which produced the highest grain yield and stable mineral concentrations in non-heat-stressed and heat-stressed environments, should be considered in breeding programs aiming for high grain quantity and quality.
A Plant Unit Relates to Missing Seeding Detection and Reseeding for Maize Precision Seeding
Authors: Zhang Chunling , Xie Xiaodong , Zheng Zihao , Wu Xiaoqing , Wang Weiwei , Chen Liqing
Published: October 2022

For the current sowing machine, unavoidable reasons such as clogging of the seed row disc type opener and the seed guide tube, slipping off the ground wheel, and other unavoidable reasons lead to sowing machine leakage. This paper designs a system to detect and compensate for missed sowing of maize precision seeds. The system mainly consists of a missed seeding detection system module, a control system module, a display system module, an actuation system module, a compensation system module, etc. Detection of missed seedings uses a microcontroller as the controller and a fiber optic sensor as the detection element. Through the microcontroller’s input capture and timer timing function, the total number of rows of missed and reseeded seeds is displayed on the LED display and fed back to the replenishment system to realize the alarm of missed seeds and automatic compensation. The results show that: the detection accuracy of missed seeding is more than 96%, and the highest detection accuracy can reach 100%. Less than 4% error occurred in missed seeding compensation (0.3% error in stepper motor speed, 4% error in rotation angle). The replanting rate of replanters is more than 90%; the replanting pass rate is more than 95%. When the tractor driving speed is 3~8 km/h, the sowing qualified rate is more than 90% to meet the sowing and replanting requirements.
Coffee-Yield Estimation Using High-Resolution Time-Series Satellite Images and Machine Learning
Authors: Martello Maurício , Molin José Paulo , Wei Marcelo Chan Fu , Canal Filho Ricardo , Nicoletti João Vitor Moreira
Published: October 2022

Coffee has high relevance in the Brazilian agricultural scenario, as Brazil is the largest producer and exporter of coffee in the world. Strategies to advance the production of coffee grains involve better understanding its spatial variability along fields. The objectives of this study were to adjust yield-prediction models based on a time series of satellite images and high-density yield data, and to indicate the best phenological stage of coffee crop to obtain satellite images for this purpose. The study was conducted during three seasons (2019, 2020 and 2021) in a commercial area (10.24 ha), located in the state of Minas Gerais, Brazil. Data were obtained using a harvester equipped with a yield monitor that measures the volume of coffee harvested with 3.0 m of spatial resolution. Satellite images from the PlanetScope (PS) platform were used. Random forest (RF) regression and multiple linear regression (MLR) models were fitted to different datasets composed of coffee yield and time series of satellite-image data ((1) Spectral bands—red, green, blue and near-infrared; (2) Normalized difference vegetation index (NDVI); or (3) Green normalized difference vegetation index (GNDVI)). Whether using RF or MLR, the spectral bands, NDVI and GNDVI reproduced the spatial variability of yield maps one year before harvest. This information can be of critical importance for management decisions across the season. For yield quantification, the RF model using spectral bands showed the best results, reaching R2 of 0.93 for the validation set, and the lowest errors of prediction. The most appropriate phenological stage for satellite-image data acquisition was the dormancy phase, observed during the dry season months of July and August. These findings can help to monitor the spatial and temporal variability of the fields and guide management practices based on the premises of precision agriculture.
Mycorrhizal Types Regulated the Responses of Biomass in Different Plant Organs to N Addition
Authors: Luo Mengyuan , Shi Zhaoyong , Yang Shuang , Zhang Menghan , Wu Shanwei , Zhang Mengge
Published: October 2022

Nitrogen (N) deposition is known to significantly affect plant growth. Mycorrhizas play an important role in plant productivity, and plants of different mycorrhizal types respond differently to global change, which will inevitably affect plant response to N deposition. However, little is known about the differences of different mycorrhizas in biomass allocation of host plants in response to N addition. Here, a meta-analysis of data from N addition experiments was carried out to analyze the response of biomass in arbuscular mycorrhiza (AM) and ectomycorrhiza (ECM) plants to N addition. The results showed that biomass of leaf, stem, fine root (FR), and litter between AM and ECM plants responded differently to N addition (p < 0.05). Among them, biomass of leaf and stem in ECM plants (leaf: 46.89%; stem: 45.59%) was more sensitive (positively) to N addition than AM plants (leaf: 27.84%; stem: 10.30%) (p < 0.05). N addition suppressed biomass of FR in AM plants (−11.22%) but promoted that in ECM plants (13.77%). The effects on biomass also varied with different functional groups between AM and ECM plants. However, the N responses were influenced by other resources. When other treatments were added, biomass was less varied in AM plants compared to ECM plants. In addition, the N response of WB (whole biomass) and root biomass were positively correlated with annual temperature in ECM plants, but that in AM plants did not. The effects on shoot biomass in AM and ECM plants to N addition both decreased with annual temperature. The N response of root biomass increased with annual precipitation. It can be seen that different mycorrhizal types regulate the response of different plant organ biomass to N addition, which is significant for predicting ecosystem responses and feedback to environmental change.
Evaluating Effects of Bradyrhizobium and Arbuscular Mycorrhizal Fungi Inoculation on Yield Components of Mung Bean (Vigna radiata (L.) Wilczek) and Nitrogen Fixation
Authors: Mott Joshua , Abaye Ozzie , Reiter Mark , Maguire Rory
Published: October 2022

Mung beans (Vigna radiata (L.) Wilczek) are only inoculated in some production systems, but there is a current lack of knowledge on the best inoculants to use for effective nitrogen fixation (nodulation) and plant yields. The objectives of the present study were to determine if the dual inoculation of Arbuscular Mycorrhizal Fungi (F) and Bradyrhizobium (R) provides greater (a) mung bean yield and quality (b) nitrogen fixation for mung bean and residual soil nitrogen for the following crop, and (c) determine if these effects are consistent across various environments. Field trials were conducted in Blacksburg, VA (sandy clay loam), and Eastern Shore, VA (sandy loam), over the summers of 2020 and 2021. There were 5 treatments replicated 5 times for each variety at each site; R, F, R + F, high nitrogen (N) (100 kg ha−1), and a control, for a total of 25 plots per site. Mung beans grown in Blacksburg in 2020 and 2021 averaged 53.8% more seeds per pod than mung beans grown at the Eastern Shore. Overall yield components (seeds per pod, pods per plant) are heavily influenced by soil type. Dual inoculation significantly increased grain yield (+33%) compared to a synthetic N fertilizer application, but did not significantly increase grain yield compared to the control (+22%). Dual inoculation may increase the grain yields of mung beans compared to synthetic fertilizer regime, but does not show evidence of improving N fixation.
Evaluating Impacts between Laboratory and Field-Collected Datasets for Plant Disease Classification
Authors: Fenu Gianni , Malloci Francesca Maridina
Published: October 2022

Deep learning with convolutional neural networks represents the most used approach in recent years in the classification of leaves’ diseases. The literature has extensively addressed the problem using laboratory-acquired datasets with a homogeneous background. In this article, we explore the variability factors that influence the classification of plant diseases by analyzing the same plant and disease under different conditions, i.e., in the field and in the laboratory. Two plant species and five biotic stresses are analyzed using different architectures, such as EfficientB0, MobileNetV2, InceptionV2, ResNet50 and VGG16. Experiments show that model performance drops drastically when using representative datasets, and the features learned from the network to determine the class do not always belong to the leaf lesion. In the worst case, the accuracy drops from 92.67% to 54.41%. Our results indicate that while deep learning is an effective technique, there are some technical issues to consider when applying it to more representative datasets collected in the field.
Assessing the Population Structure of Colorado Potato Beetle Populations in Croatia Using Genetic and Geometric Morphometric Tools
Authors: Kadoić Balaško Martina , Bažok Renata , Mikac Katarina M. , Benítez Hugo A. , Correa Margarita , Lemic Darija
Published: October 2022

The Colorado potato beetle (CPB, Leptinotarsa decemlineata Say) is one of the most successful invasive species worldwide. It has been present in Croatia since 1947, where it has caused significant damage to potato plants and developed resistance to several insecticides. Our study is the first attempt to investigate the population structure of CPBs in Croatia. SNP and GM techniques provided us with data about the population structure of the CPB population. A Bayesian model-based clustering algorithm implemented in STRUCTURE, principal component analysis (PCA), and discriminant analysis of principal components (DAPC) were used to analyze the genetic structure of CPBs. For the morphometric analysis, the hindwing shape of the same CPB individuals was examined using wing venation patterns. We detected the low genetic and phenotypic variabilities of CPB populations and the presence of a single panmictic population in the study area, well adapted to different environmental conditions, indicating high phenotypic plasticity. Due to such exceptional adaptation of the CPB population, it is necessary to implement an area-wide approach in future pest control management.
The Phosphorus Availability in Mollisol Is Determined by Inorganic Phosphorus Fraction under Long-Term Different Phosphorus Fertilization Regimes
Authors: Wang Qiong , Zhang Naiyu , Chen Yanhua , Qin Zhenhan , Jin Yuwen , Zhu Ping , Peng Chang , Colinet Gilles , Zhang Shuxiang , Liu Jin
Published: October 2022

Understanding the effects of a fertilization regime on the long-term accumulation and transformation of soil phosphorus (P) is essential for promoting the development of sustainable management of soil P. Based on a 29-year field experiment in Mollisol, the compositions and changes of P forms using a modified Hedley sequential extraction method, solution 31P-NMR and P K-edge XANES and soil properties were investigated under continuous mono maize with and without manure (NPKM and NPK). Results showed a stronger positive related coefficient between soil total P and labile P, and mid-labile P fraction was found in NPKM than in NPK treatment. It indicated NPKM could improve the availability of soil accumulated P and reduce its transformation to stable P. Accumulated inorganic P (Pi) was dominated by aluminum phosphate (Al-P) and monobasic calcium phosphate monohydrate (MCP) for NPK treatment, Al-P, MCP, and tricalcium phosphate for NPKM treatment with XANES analysis, which contributed to the P availability in Mollisol. Moreover, the proportion of IHP with XANES and ratio of orthophosphate diesters to monoesters in NPK compared to NPKM indicated the higher Po lability with NPK treatment. Pi, especially NaHCO3-Pi and NaOH-Pi, were the potential sources of resin-Pi. Soil organic matter (SOM), organic-bound iron, and alumina oxide (Fep + Alp) showed significant influence on the transformation of P forms. Our research suggested that due to the rise in SOM and Fep + Alp, the fertilization regime significantly increased most highly active soil P fractions, especially in NPKM treatment. This work gives new insight into sustainable P management, which benefits the reduction in soil P accumulation.
Effects of 10 Years of the Return of Corn Straw on Soil Aggregates and the Distribution of Organic Carbon in a Mollisol
Authors: Gan Jiawei , Qiu Chen , Han Xiaozeng , Kwaw-Mensah David , Chen Xu , Yan Jun , Lu Xinchun , Zou Wenxiu
Published: October 2022

The return of straw is a widely used agricultural practice for increasing the soil organic carbon (SOC) content and improving soil structure in Mollisols, owing to the decline caused by continuous high-intensity tillage. We conducted a field experiment where corn straw was continuously returned for 10 years to investigate effects of the straw on the size distribution and stability of soil aggregates and on SOC density fractions. The treatments were no straw return (CK) and four rates of straw return: 6000 kg hm−2 (S1), 9000 kg hm−2 (S2), 12,000 kg hm−2 (S3), and 15,000 kg hm−2 (S4). SOC contents after straw return for bulk soil, a free light fraction (F-LF), an occluded light fraction (O-LF), and a heavy fraction (HF) were significantly higher by 27.0, 644.3, 720.0, and 69.2%, respectively, in S4 than CK. The contents of F-LF, O-LF, and HF in aggregates >2.00 mm were significantly higher by 194.2, 162.1, and 35.8%, respectively, in S4 than CK. Structural equation modeling indicated that SOC contents and aggregates >0.25 mm were directly correlated with the amount of straw returned. We conclude that returning 15,000 kg m−2 of straw would be an effective agronomic practice to restore Mollisol fertility.
Use of Winery and Animal Waste as Fertilizers to Achieve Climate Neutrality in Non-Irrigated Viticulture
Authors: Litskas Vassilis , Ledo Alicia , Lawrence Patrick , Chrysargyris Antonios , Giannopoulos George , Heathcote Richard , Hastings Astley , Tzortzakis Nikolaos , Stavrinides Menelaos
Published: October 2022

There is a growing interest globally in reducing the environmental impacts of farming by reusing agricultural waste and reducing agricultural greenhouse gas (GHG) emissions. In this work, the potential of Mediterranean viticulture for GHG emissions mitigation and Carbon (C) storage in biomass and soil is examined. In a field experiment, synthetic fertilizer use was reduced by 50 and 100% after applying a winery waste-based biofertilizer, and the C balance was determined at the vineyard level. The Vineyard Carbon Tool (VCT) for GHG emissions estimation in vineyards was developed by tailoring to vineyards the widely used Cool Farm Tool (CFT). Our results show that existing conventional viticulture could be easily transformed into zero-emissions by reducing nitrogen (N) fertilizers, lowering tillage frequency, using less fuel, and maintaining field margin vegetation at the farm level. Use of Life Cycle Analysis (LCA) showed that farm input reductions lead to a vineyard lifetime C storage equal to 25,124 kg CO2-eq ha−1 or 837 kg CO2-eq ha−1 year−1. This approach could be used for the design of eco-schemes related to C farming under the new Common Agricultural Policy (CAP).
Species Composition, Diversity, and Biomass Estimation in Coastal and Marine Protected Areas of Terengganu, Peninsular Malaysia
Authors: Pesiu Elizabeth , Lee Gaik Ee , Salam Muhammad Razali , Salim Jamilah Mohd , Lau Kah Hoo , Yong Jean Wan Hong , Abdullah Mohd Tajuddin
Published: October 2022

We investigated and compared the tree species composition and diversity of different forest types in Setiu Wetlands and on the three major islands of Terengganu. A total of 24 plots of 25 m × 25 m with four plots in each study site were established, viz. Melaleuca swamp forest in Kampung Fikri, freshwater swamp forest in Kampung Gong Batu, mangrove forest in UMT Setiu research station, and the islands, namely Pulau Bidong, Pulau Redang, and Pulau Perhentian. We calculated the basal area, stand density, Importance Value Index, species diversity, and above-ground biomass in the designated study areas. We assessed 139 tree species from 96 genera and 50 families based on a total of 2608 tree samples of 5 cm DBH and above. The freshwater swamp forest harbored the highest number of species with 20 species in Setiu Wetlands, and among the islands, Pulau Redang had the highest with 56 species. Melaleuca cajuputi was the most dominant species in the Melaleuca swamp forest, while Alstonia spatulata and Rhizophora apiculata are expected in the freshwater swamp and mangrove forest, respectively. Pulau Bidong, Pulau Redang, and Pulau Perhentian are mostly represented by Licania splendens, Shorea glauca, and Vatica sp., respectively. All the dominant species but Licania splendens contributed to the highest amount of above-ground biomass. Our current study indicated that different forest types vary in composition and structure, which may contribute to their unique ecological roles within their specific environment.
Effects of Different Regulating Measures on the Floral and Nutritional Physiology of Lemon
Authors: Du Yuxia , Li Jinxue , Dong Jianmei , Hu Chengxiao , Li Danping , Tan Qiling , Zhang Jinzhi , Li Jing , Zhou Xianyan , Zhu Chunhua , Lai Xinpu
Published: October 2022

Lemon is an important economic crop in the world and can bloom several times each year. The Dehong prefecture of Yunnan province is one of the main lemon-producing areas in China, and is a suitable area for planting high-quality lemons in China. However, the hot and rainy summers in Yunnan are not conducive to flower bud differentiation, which leads to low lemon yield. Therefore, normal flower bud differentiation is important to guarantee lemon production and quality. In this study, we selected some lemon trees for a pot experiment, and we sprayed the lemon leaves with gibberellin and paclobutrazol to regulate lemon flower formation. We set four separate concentration gradients for each regulator: 50, 100, 150, and 200 mg/L. The PBZ concentration gradients were 200, 400, 600, and 1200 mg/L. After the experiment, we determined and analyzed the morphological index and fruit quality of the lemon trees. The results showed that under the same cultivation and management conditions, spraying paclobutrazol substantially inhibited the growth of lemon shoots, increased the fruit setting rate, and improved the fruit yield. However, gibberellin considerably reduced the number of lemon flowering branches and promoted the vegetative growth of the lemons. When the concentration of paclobutrazol was 600 mg/L, the amount of lemon fruits reached the maximum, which remarkably increased the titratable acid and soluble solids contents of the fruit and ultimately increased the fruit quality compared with the control. Altogether, selecting the appropriate concentration of regulators to control the flowering and fruit setting of fruit trees is important and has value in guiding actual production.
Cultivating Improved Varieties of Maize Does Not Guarantee Sufficient Dietary Provision of Fe and Zn in a Maize–Bean Intercropping System in Eastern Uganda: Evaluating Effects of Climate and Soil Types
Authors: Lugoi Lazarus Pierentino , Martinsen Vegard , Almås Åsgeir Rossebø
Published: October 2022

Since hunger and malnutrition are affecting billions of people, the effect of cropping seasons, soil types and climatic conditions (temperature and precipitation) on yield, iron (Fe), zinc (Zn) and amino acids content in grains of hybrid maize (Zea mays), at smallholder farms in Eastern Uganda, was examined. Thirty-six (36) on-farm experiments under maize–bean intercropping with NPK fertilizer were established across three soil types (Petric Plinthosols, Lixic Ferralsols and Vertisols) and growing conditions (seasons, temperature and rainfall). We found significant differences in the grain yield between seasons, but not between soil types. Fe (mean of 22.48 mg/kg) in maize–grains significantly correlated with Zn (mean of 23.21 mg/kg), soil pH, soil organic matter and total nitrogen. Grain amino acid concentrations differed significantly between the seasons and across soil types. Precipitation and temperature did not significantly influence grain-yield nor grain Fe and Zn contents. For two seasons, the hybrid maize variety did not perform better than other varieties in the regions. Thus, a critical finding is that improved varieties of maize is not enough to facilitate increased Fe and Zn uptake nor amino acids content in their grains to desired levels. Hence, a diet dominated by maize will not supply sufficient micronutrients (Fe and Zn) to meet the human dietary requirements in this region.
Design and Implementation of Artificial Intelligence of Things for Tea (Camellia sinensis L.) Grown in a Plant Factory
Authors: Chang Chung-Liang , Huang Cheng-Chieh , Chen Hung-Wen
Published: October 2022

An innovative plant growth monitoring and environmental control platform is designed and implemented in this study. In addition to using multi-band artificial light sources for plant growth and development, an artificial intelligence of things (AIoT) approach is also utilised for environmental parameter monitoring, control, and the recording of plant growth traits and diseases. The five LED bands are white (5000 K), cool white (5500 K), blue (peak: 450 nm), red (660 nm), and light red (630 nm). The tea plant (Camellia sinensis f. formosana) is irradiated using lighting-emitting diodes (LED) composed of bands of different wavelengths. In addition, the number of leaves, contour area of the leaves, and leaf colour during the growth period of two varieties of tea plants (Taicha No. 18 and Taicha No. 8) under different irradiation intensities are analysed. Morphological image processing and deep learning models are simultaneously used to obtain plant growth characterization traits and diseases. The effect of the spectral distribution of the light source on the growth response of tea leaves and the effect of disease suppression are not fully understood. This study depicts how light quality affects the lighting formula changes in tea plants under controlled environments. The experimental results show that in three wavelength ranges (360–500 nm, 500–600 nm, and 600–760 nm), the light intensity ratio was 2.5:2.0:5.5 when the illuminance intensity was about 150 µmol∙m−2∙s−1 with a photoperiod of 20:4 (dark); this enabled more leaves, a smaller contour area of the leaves, and a light green colour of the leaves of the tea plant (Taicha No. 18). In addition, during the lighting treatment, when the ratio of the band with an irradiation intensity of 360–500 nm to that with an irradiation intensity of 500–600 nm was 2:1.5, it resulted in a better leaf disease inhibition effect. When the light intensity was increased to more than 400 µmol∙m−2∙s−1, it had little effect on the growth and development of the tea plants and the inhibition of diseases. The results of the study also found that there was a significant difference between the colour of the leaves and the relative chlorophyll content of the tea trees. Finally, the tea plant growth response data obtained from manual records and automatic records are compared and discussed. The accuracy rates of leaf number and disease were 94% and 87%, respectively. Compared with the results of manual measurement and recording, the errors were about 3–15%, which verified the effectiveness and practicability of the proposed solution. The innovative platform provides a data-driven crop modeling application for plant factories.