Optimal Harvesting Research Articles

The chemical profile and seasonal variation of the composition of the phenolic acids in different plant parts of Centaurea sp.

This study investigated the seasonal variation (over seven months) of phenolic acid (PHA) components in different parts of Centaurea sp. The primary objective was to determine the pattern of variation, while the secondary objective was to identify which month or growth stage provides a greater total PHA content or percentage of bioactive components. Different patterns of seasonal variations were highlighted for the different PHA components and their classes (hydroxybenzoic and -cinnamic acids) in different parts of the plant. The leaves exclusively provided the highest PHA contents, with maximum values reached in April (1368.06 μg/g). The major hydroxybenzoic acid derivatives (HBAs) identified in the leaves were vanillic acid (VaA) “154.18–374.06 μg/g” and protocatechuic acid (PA) “9.37–595.61 μg/g”, while the major hydroxycinnamic acid derivatives (HCAs) were p-coumaric acid (p-CoA) “109.35–261.77 μg/g”, m-coumaric acid (m-CoA) “10.22–70.57 μg/g”, and ferulic acid (FeA) “35.54–109.13 μg/g”. The maximum percentage of PA was obtained in April “595.61 μg/g”, while the maximum p-CoA content was obtained in January “261.77 μg/g”. Therefore, the leaves can be recommended as the optimal source of PHAs. If there is a specific interest in certain PHA components, we recommend collection in either January or April. Multivariate statistical analysis (PCA & AHC) showed the existence of two main clusters. The first cluster comprised the leaves, distinguished by the highest VaA, PA, and p-CoA contents. The second cluster comprised roots and the root bark samples. This study provides information on the development of PHAs in different parts of Centaurea sp. and explores potential applications. It will be of considerable interest for determining the optimal harvesting time of shrub species used for their medicinal properties and bio-active phenolic contents.

Evidence of sustainable intensification in the production of palm oil from crops planted with Elaeis oleifera x Elaeis guineensis in Colombia

Cultivars from crossings between Elaeis oleífera x Elaeis guineensis (OxG hybrids) have proven to have partial resistance to Bud Rot (BR), the most challenging disease for the Colombian palm oil agroindustry. Therefore, OxG hybrids were planted in areas that have experienced BR outbreaks since 2007. Soon, it was discovered that OxG hybrid female inflorescences required to be pollinated with pollen from E. guineensis to improve bunch formation. Researchers from Cenipalma noted that OxG hybrid bunches have a greater proportion of partenocarpic fruits. The latter generated a line of research that yielded artificial pollination in 2018. Artificial pollination consists of applying naphtaleneacetic acid (NAA) to female inflorescences in order to promote the formation of partenocarpic fruits. If artificial pollination was to be scaled up, many questions needed an answer such as, what was labor productivity, the stage at which bunches should be harvested and even, if it was profitable. This paper synthesizes the results of studies carried out by Cenipalma together with managers from oil palm plantations. These research studies were undertaken by means of time and motion studies, optimal harvesting time studies and cost–benefit analysis. Results come from plantations located at all the zones that have planted OxG cultivars that have planted the most common OxG hybrids. Our results indicate that an artificial pollination worker may be asked between 188 and 249 inflorescences per workday depending upon inflorescences density (inflorescences per hectare). We also found that by implementing criteria on optimal harvest time one may increase the oil extraction rate from 6 to 29.7%. Finally, it was found that artificial pollination is more costly than using only E. guineensis pollen, but the increase in yields at the field and the increase in the amount of oil extracted offset this extra cost and provide greater profitability to oil palm growers. This is a sample of how, by using the same natural resources, implementing artificial pollination, and harvesting at the proper stage; one can have greater yields and have a more resilient business. In other words, this is an example of sustainable intensification.

Assessment of Three Major Shrimp Stocks in Bangladesh Marine Waters Using Both Length-Based and Catch-Based Approaches

Penaeus monodon (tiger shrimp), Metapenaeus monoceros (brown shrimp), and Fenneropenaeus indicus (white shrimp) are the most economically important shrimp species in the waters of the Bay of Bengal, Bangladesh. This is the first analytical study to assess three major shrimp stocks using both length-based and catch-based methods, such as length-based Bayesian biomass estimation (LBB), length-based indicator (LBI), and a catch-based method entitled JABBA (Just Another Bayesian Biomass Assessment), to explore and process the data; estimate the growth parameters, with length at first capture; present relative biomasses; and approximate the reference points. The parameters of the von Bertalanffy Growth Function (VBGF) analysis for the tiger, brown, and white shrimps were L∞ = 113.0 mm, 85.4 mm, and 76.4 mm, respectively, for carapace length. Our results showed that the relative biomass level (B/BMSY) of the tiger shrimp was 0.43, suggesting an overfished status, and brown and white shrimps were 0.84 and 0.96, indicating that they were fully exploited but not overfished. This study, therefore, advised an optimum carapace length limit to catch from 57.0–70.0 mm for tiger shrimp, 44.0–53.0 mm for brown shrimp, and 40.0–48.0 mm for white shrimp. The estimated maximum sustainable yield (MSY) reference points were as follows: optimal biomass BMSY = 3116 mt, 15,885 mt, and 2649 mt for tiger, brown, and white shrimp, respectively, and optimal harvest rate uMSY = 12%, 33%, and 8% for tiger, brown, and white shrimp, respectively. The average annual catch values for the last ten years were 265 mt, 2396 mt, and 115 mt below the estimated MSY values of 389 mt, 4899 mt, and 209 mt for tiger, brown, and white shrimp, respectively. But, brown shrimp had the estimated highest carrying capacity (31,770 mt) and intrinsic growth rate (0.66) than the tiger and white shrimp, which was replicated distinctly in the graphical representation of the Kobe plot and the surplus production plot. Hence, the brown shrimp stock is estimated to be in a better state than the tiger and white shrimp stocks.

Smart high-yield tomato cultivation: precision irrigation system using the Internet of Things.

The Internet of Things (IOT)-based smart farming promises ultrafast speeds and near real-time response. Precision farming enabled by the Internet of Things has the potential to boost efficiency and output while reducing water use. Therefore, IoT devices can aid farmers in keeping track crop health and development while also automating a variety of tasks (such as moisture level prediction, irrigation system, crop development, and nutrient levels). The IoT-based autonomous irrigation technique makes exact use of farmers' time, money, and power. High crop yields can be achieved through consistent monitoring and sensing of crops utilizing a variety of IoT sensors to inform farmers of optimal harvest times. In this paper, a smart framework for growing tomatoes is developed, with influence from IoT devices or modules. With the help of IoT modules, we can forecast soil moisture levels and fine-tune the watering schedule. To further aid farmers, a smartphone app is currently in development that will provide them with crucial data on the health of their tomato crops. Large-scale experiments validate the proposed model's ability to intelligently monitor the irrigation system, which contributes to higher tomato yields.

A Real-Time Detection and Maturity Classification Method for Loofah

Fruit maturity is a crucial index for determining the optimal harvesting period of open-field loofah. Given the plant’s continuous flowering and fruiting patterns, fruits often reach maturity at different times, making precise maturity detection essential for high-quality and high-yield loofah production. Despite its importance, little research has been conducted in China on open-field young fruits and vegetables and a dearth of standards and techniques for accurate and non-destructive monitoring of loofah fruit maturity exists. This study introduces a real-time detection and maturity classification method for loofah, comprising two components: LuffaInst, a one-stage instance segmentation model, and a machine learning-based maturity classification model. LuffaInst employs a lightweight EdgeNeXt as the backbone and an enhanced pyramid attention-based feature pyramid network (PAFPN). To cater to the unique characteristics of elongated loofah fruits and the challenge of small target detection, we incorporated a novel attention module, the efficient strip attention module (ESA), which utilizes long and narrow convolutional kernels for strip pooling, a strategy more suitable for loofah fruit detection than traditional spatial pooling. Experimental results on the loofah dataset reveal that these improvements equip our LuffaInst with lower parameter weights and higher accuracy than other prevalent instance segmentation models. The mean average precision (mAP) on the loofah image dataset improved by at least 3.2% and the FPS increased by at least 10.13 f/s compared with Mask R-CNN, Mask Scoring R-CNN, YOLACT++, and SOLOv2, thereby satisfying the real-time detection requirement. Additionally, a random forest model, relying on color and texture features, was developed for three maturity classifications of loofah fruit instances (M1: fruit setting stage, M2: fruit enlargement stage, M3: fruit maturation stage). The application of a pruning strategy helped attain the random forest model with the highest accuracy (91.47% for M1, 90.13% for M2, and 92.96% for M3), culminating in an overall accuracy of 91.12%. This study offers promising results for loofah fruit maturity detection, providing technical support for the automated intelligent harvesting of loofah.

The Development of the Optimal Harvesting Model of an Offshore Fishery Supply Chain Based on a Single Vessel

This study delves into the offshore fishing industry in Taiwan, emphasizing the significance of the aquatic product market, supply chain development, and the maturity of cold chain technology. Taiwan’s geographical advantage of being surrounded by the sea provides a thriving environment for marine resources and migratory fish. This study is motivated by the increasing demand for diverse fish products, driven by the growing need for high-quality protein. Recognizing the importance of meeting this demand, this study aims to investigate the capacity of logistics systems and cold storage in the offshore fishery industry, particularly under conditions of uncertainty. To tackle the optimization challenges prevalent in the offshore fishery supply chain, this study employs the bat algorithm (BA), a metaheuristic algorithm inspired by the remarkable echolocation behavior of bats. Additionally, a systematic literature review methodology is utilized to gather relevant articles and establish a comprehensive understanding of the study domain. This study culminates in proposing an optimized fishing model for the offshore fishery supply chain, highlighting the significance of evaluating supply chain value from a management perspective and identifying existing deficiencies and bottlenecks in current research. By focusing on optimizing the offshore fishery supply chain, this study aims to enhance the industry’s efficiency and effectiveness, providing valuable insights and recommendations to improve the capacity of logistics systems and cold storage. Furthermore, this study presents the results of the BA, showcasing its effectiveness in approaching optimization challenges, thereby validating its utility for the offshore fishery industry. Sensitivity analysis reveals the potential for higher profits by raising the inventory limit of the manufacturer, enabling the supplier to provide materials to more profitable trading partners. While this study is based on a revenue and cost model, it acknowledges that the objectives and constraints would become more complex in varying logistic system circumstances. The future study aims to expand the scale of the model and incorporate practical cases to further enhance its applicability.

Human-centered approach for an efficient cucumber harvesting robot system: Harvest ordering, visual servoing, and end-effector

This paper presents a human-centered approach for an efficient cucumber-harvesting robot system. Specifically, harvest ordering, visual servoing, and end-effector-based manipulation functionalities were integrated to realize efficient and stable harvesting. The proposed approach involved determining the optimal harvest ordering, guiding the end-effector to the cucumber pedicel through visual servoing, and designing an end-effector to effectively harvest long cucumbers. The performance of the system was evaluated through preliminary and field experiments. The results of the preliminary experiments showed that harvest ordering decreased the harvesting time and travel length and increased the battery efficiency. The visual servoing was robust, and pedicels could be rapidly detected at a speed of 16–23 FPS through computer vision technologies. The pedicel could be accurately positioned within the cutting area of the end-effector. Furthermore, the proposed end-effector could effectively cut thin cucumber pedicels (3–6 mm), with a 100.0% success rate. Field experiments were conducted at three sites in Korea: Green Monsters, Sangju smart-valley, and Fresh-farm. The harvest success rate at the three sites ranged from 50.9% to 60.0%, with an overall value of 56.6%. The overall average harvest time of 56.0 s. The positional accuracy of the system was within the optimal range of 0–30 mm. Furthermore, the primary causes of harvest failure were analyzed, and future research directions to improve the performance of harvest robots were discussed.

Vibration Energy Harvesting for Aerospace Applications

The aerospace industry is actively seeking to employ wireless sensors for various data collection and structural health monitoring applications. While continued development of wireless sensors and sensor networks holds promise in the aerospace industry, the challenge of powering these sensors must still be addressed. Vibration energy harvesting is one possible approach in this regard, and is particularly promising for aerospace applications due to the ubiquitous presence of vibration energy. In this review paper the generic design considerations and challenges involved in harvesting energy from vibration energy are described. The paper also highlights recent progress addressing current challenges in adapting a vibration energy harvesting approach in a practical application, including resonance frequency tuning and design considerations for optimal energy harvesting performance. Recent progress in adapting this approach to the microscale using MEMS technology is also highlighted.

FORRAJE VERDE HIDROPÓNICO COMO ALTERNATIVA FORRAJERA EN LA ALIMENTACIÓN ANIMAL

<p><strong>Background. </strong>Animal feeding and nutrition constitute the main limitation in livestock farming in arid and semi-arid zones, because it depends to a large extent on imported concentrated feed. Hydroponic green fodder (FVH) represents an efficient and economical alternative to obtain food with high nutritional value for domestic animals. However, little has been used due to ignorance of the method to produce it and its benefits. <strong>Objective. </strong>To analyze the scientific information available on the agronomic, productive and nutritional attributes of HGF, and its potential as a forage resource in animal feed. <strong>Methodology. </strong>A bibliographic review was carried out based in the following databases: Scopus, NCBI, Springer, Science direct, Google Scholar, Redalyc, ResearchGate and other digital repositories, using keywords in Spanish and English, such as hydroponic green fodder, hydroponic technology, hydroponic food and alternative fodder. <strong>Results.</strong> The HGF production cycle is basically carried out in six stages: <em>i</em>) seed selection, <em>ii</em>) seed washing and disinfection, <em>iii</em>) seed hydration, <em>iv</em>) sowing and germination, <em>v</em>) growth, and <em>vi</em>) harvest. Biomass production increases progressively in relation to a higher seed sowing density. The optimum harvest period for forage is from 12 to 14 days after sowing. However, the volume and yield of production must be assessed in terms of the nutritional quality of harvested fodder. In prolonged harvest periods, the crude protein content increases, while the dry matter content decreases. In cattle and goats, partial diet supplementation with HGF improves milk production and composition, as well as the weight gain rate and feed ratio, due to increased feed intake and nutrients digestibility. In monogastrics, the total replacement of conventional feed with HGF affects feed intake and growth rate. <strong>Implications. </strong>More research is required to determine the adequate portion of HGF in the supplementation of animal diets, without affecting their productive performance, especially in the case of monogastrics. <strong>Conclusion. </strong>The HGF technology represents a viable and economical alternative to counteract the scarcity of fresh and nutritious food, especially in dry seasons of arid and semi-arid areas; as well as in urban or suburban areas where there is limited land area for conventional fodder production.</p>

Full-thickness skin columns: A method to reduce healing time and donor site morbidity in deep partial-thickness burns.

The current standard of care for the coverage of large wounds often involves split thickness skin grafts (STSGs) which have numerous limitations. One promising technique that has gained traction is fractional autologous skin grafting using full-thickness skin columns (FTSC). Harvesting occurs orthogonally by taking numerous individual skin columns containing the epidermis down through the dermis and transferring them to the wound bed. The purpose of this porcine study was to investigate the efficacy of implanting FTSCs directly into deep partial-thickness burn wounds, as well as examining donor site healing at the maximal harvest density. It was hypothesised that by utilising FTSCs, the rate of healing in deep partial thickness burns can be improved without incurring the donor morbidity seen in other methods of skin grafting. Deep partial-thickness burns were created on the dorsum of female red duroc swine, debrided 3 days later and FTSCs were implanted at varying expansion ratios directly into the burn wounds. At day 14, 1:50 expansion ratio showed significantly faster re-epithelialisation compared to the debrided burn control and 1:200. Donor sites (at 7%-10% harvest density) were 100% re-epithelialised by day 7. Additionally, the maximal harvest density was determined to be 28% in an ex vivo model, which then five donor sites were harvested at 28% density on a red duroc swine and compared to five STSG donor sites. At maximal harvest density, FTSC donor sites were significantly less hypopigmented compared to STSGs, but no significant differences were observed in re-epithelialisation, contraction, blood flow or dermal thickness. In conclusion, implantation directly into deep partial-thickness burns is a viable option for the application of FTSCs, favouring lower expansion ratios like 1:50 or lower. Little difference in donor site morbidity was observed between FTSC at a maximal harvest density of 28% and STSGs, exceeding the optimal harvest density.

Seed Physiological Quality Testing Gambir (Uncaria gambir roxb.) at Various Ages of Harvest

Background: Gambier has a problem with low production levels which is caused by the use of inferior quality plants that come from inferior seeds. This study aims to determine the optimal harvesting age for producing gambir seeds with optimal physiological quality. The maturity phase of the fruit became the basis for determining the timescalation of the harvest that count from the day after anthesis. The research was carried out from June to December of 2021. This research was conducted at the Seed Technology Laboratory, Faculty of Agriculture andalas University, Padang. The plant material utilized is the seed of the Mancik Riau gambier. Methods: The research employed a Completely Randomized Design (CRD) with five treatments based on days after anthesis (DAA) because at this phase the plants pollinated and the fruit maturity phase begin and it used three replication : seeds harvested at 110-112 DAA, 107-109 DAA, 104-106 DAA, 101-103 DAA and 98-100 DAA. Quantitative data are analyzed at a 5% significance level using Analysis of Variance; if the effect is significant, the Duncan’s Multiple Range Test (MRT) is performed; and qualitative data are presented descriptively. Result: The results revealed that the harvest age of 98-100 DAA provided the highest viability and vigor, as measured by germinated power testing at 80.50%, seed growth potential testing at 81.83%, T50 test, index value testing at 11.04 and soil emergence testing at 84.33%, indicating that the plant had reached physiological maturity.

Prey–predator optimal harvesting mathematical model in the presence of toxic prey under interval uncertainty

This paper explores a multispecies prey–predator harvesting system based on Lotka–Volterra model with two preys (palatable and toxic prey) and one predator with interval biological parameters. Due to the enhancement in resource availability, prey–predator system becomes destabilized theoretically (the paradox of enrichment). Also, a prey may become toxic for the predator due to a little change in the resource stoichiometry. The presence of toxic prey influences the growth of the predator as well as that of a palatable prey. Thus the dynamics of prey–predator system is significantly affected by the presence of such toxic prey. Again, due to lack of precise numerical data of the biological parameters an interval number based mathematical toxic prey–predator model is developed and then discussed the dynamical behaviour of the model. Then, we observe the existence of different points of equilibrium and also the stabilities at these points of equilibrium of the system are presented. Also, the bionomic equilibrium of the harvesting model has been analysed. Next, the optimal harvest policy is carried out and obtained the solution in the interior equilibrium point using Pontryagin’s maximum principle. All important analytical findings are demonstrated through computer simulation using MATLAB followed by discussions and conclusions.

IoT based Plant Growth and Health Monitoring System for Mushrooms

This research paper proposes the development of an IoT-based monitoring system using a robot camera for mushroom cultivation in Sri Lanka. The system aims to address several sub-objectives, including harvest time prediction, disease detection, quality assurance, and environmental monitoring. The research utilizes image processing and machine learning techniques to capture mushroom images, analyze their size, shape, morphology, and color, and predict the optimal harvest time. It also focuses on identifying common diseases, researching their early symptoms, and recommending appropriate treatment options. The system incorporates user-friendly mobile interfaces for farmers to provide input on mushroom quality and receive alerts. Moreover, it establishes quality standards, defines criteria and thresholds, and uses machine learning algorithms to train a model for quality assessment. The research introduces an IoT device that integrates environmental sensors to optimize crop growth by analyzing real-time sensor data and recommending corrective actions. The novelties include the use of robotic camera and machine learning for feature detection, personalized disease recommendations based on individual mushroom conditions, promotion of sustainable farming practices, ui-based quality assurance score, and prioritization of corrective actions based on severity and impact. The proposed system aims to improve accuracy, increase product value, enhance profitability, and promote environmentally friendly agriculture.

Fish Monitoring from Low-Contrast Underwater Images

A toolset supporting fish detection, orientation, tracking and especially morphological feature estimation with high speed and accuracy, is presented in this paper. It can be exploited in fish farms to automate everyday procedures including size measurement and optimal harvest time estimation, fish health assessment, quantification of feeding needs, etc. It can also be used in an open sea environment to monitor fish size, behavior and the population of various species. An efficient deep learning technique for fish detection is employed and adapted, while methods for fish tracking are also proposed. The fish orientation is classified in order to apply a shape alignment technique that is based on the Ensemble of Regression Trees machine learning method. Shape alignment allows the estimation of fish dimensions (length, height) and the localization of fish body parts of particular interest such as the eyes and gills. The proposed method can estimate the position of 18 landmarks with an accuracy of about 95% from low-contrast underwater images where the fish can be hardly distinguished from its background. Hardware and software acceleration techniques have been applied at the shape alignment process reducing the frame processing latency to less than 0.5 us on a general purpose computer and less than 16 ms on an embedded platform. As a case study, the developed system has been trained and tested with several Mediterranean fish species in the category of seabream. A large public dataset with low-resolution underwater videos and images has also been developed to test the proposed system under worst case conditions.

The potential of different ripeness of blood oranges (Citrus sinensis L. Osbeck) for sale in advance after low-temperature storage: Anthocyanin enhancements, volatile compounds, and taste attributes.

To explore the optimal early harvest time similar to the ripe fruit qualities, the effects of storage temperatures on maturity indexes, weight losses, colour parameters, anthocyanin profiles, volatile and taste components of blood oranges at six different maturity levels were investigated. Total anthocyanin contents of cold-treated fruits increased to or exceed that of ripe fruits (0.24±0.12mg/100g), and fruits harvested from 260 d and 280 d after anthesis shared similar individual anthocyanin profiles to ripe fruits during storage at 8°C for 30 d and 20 d (III-30 d and IV-20 d groups), respectively. Moreover, comparative analyses of e-nose and e-tongue demonstrated the distances of volatile components and scores of taste attributes including sourness, saltiness, bitterness, sweetness, and umami in III-30 d and IV-20 d groups were close to that of ripe fruits, indicating that the fruits could be sold about 20 to 30 d ahead of the season.

Risk aversion in renewable resource harvesting

We study optimal harvesting of a renewable resource with stochastic dynamics. To focus on the effect of risk aversion, we consider a resource user who is indifferent with respect to intertemporal variability. In this setting, a constant escapement strategy is optimal, i.e. the stock after optimal harvesting is constant. We find that under common specifications of risk aversion, increasing risk and risk aversion increase current resource use. We show that this is due to an investment effect, i.e. the resource user invests in risk free alternatives, rather than the risky resource stock. A quantitative application of the model for the Eastern Baltic cod fishery shows that risk and risk aversion can have a much larger effect on optimal harvesting than found in the previous literature.

Effect of micronutrient iron on bioactive compounds isolated from cryptophytes.

Iron is one of the important micronutrients affecting algal growth due to its fundamental role in the physiological processes, including photosynthetic electron transport, respiration, and nitrogen fixation. In this study, the effect of different iron levels on growth and the production of bioactive compounds (phycoerythrin (PE), extracellular polymeric substances (EPS), and phenolic compounds (PCs)) of five cryptophyte strains were investigated. Also, the antioxidant capacity of the bioactive compounds was explored. The results showed species-specific responses to the impact of iron on growth of cryptophytes and accumulation of bioactive compounds. The growth rates of C. pyrenoidifera and Cryptomonas sp. varied significantly at different iron levels, and a reduction in the PE content was observed for several cryptophytes cultured at the highest iron level. However, no significant differences were detected in EPS content at different iron levels. Differences in PC contents of C. pyrenoidifera and Cryptomonas sp. at medium iron level were statistically significant compared with the other two treatments. The results also revealed species-specific differences in antioxidant activity at different iron levels; each studied strain followed its own pattern in response to change in iron level, and each bioactive compound had a different antioxidant activity. Overall, however, PCs demonstrated higher antioxidant activity than PE and EPS. In summary, iron has an impact on growth, bioactive compound accumulation, and antioxidant activity. However, the species-specific responses to changes in iron level should not be ignored when modifying culture conditions for optimal harvest of bioactive compounds.

Dynamics of a crop, pest and predator model in an agricultural system

A three-species model of the interactions of crop, pest, and predator has been constructed in this work. In the absence of pests and harvesting of crops, the rate of crop growth is considered as logistic. It is considered that pests consume crops whereas predators consume pests. Harvesting of the crops has been considered here. Holling type II functional response has been considered for crop and pest consumption. It is also assumed that the predators may consume pests via Holling type II functional response. Positivity and boundedness of the solution of the model have been investigated. The various equilibrium points of the model are evaluated, and the local and global stability are then investigated. Studies on the Hopf bifurcation existence criterion in connection to a crucial model parameter have been conducted. The optimal crop harvesting has then been determined using an objective function that has been defined. The Pontryagin’s maximum theory is used to calculate the ideal crop harvesting rate. The consumption of pests by predators has been proven to help keep the system stable up to a point, but a higher rate of pest consumption by predators can potentially cause instability. According to observation, the model may become unstable as the mortality rate of predators of pests rises. It is found that the increase in optimal harvesting of crops may lead the model towards stability. It has been noted that increased rate of crops consumption by pests may result in lower agricultural yields.

Effect of nitrogen application rate and harvesting stage on yield and agronomic parameters of industrial hemp (Cannabis sativa L.)

AbstractHemp (Cannabis sativa L.) is one of the world's oldest and most diversely cultivated plants, but legal constraints and a lack of agronomic rules have curtailed its production. This study aimed to determine the optimum nitrogen (N) application rate and harvesting stage for hemp yield in terms of seed, fiber, and stem. Treatments included (1) five nitrogen application rates (control [N0], 60 kg ha−1 [N60], 120 kg ha−1 [N120], 180 kg ha−1 [N180], and 240 kg ha−1 [N240]) and (2) three harvesting stages: before flowering (HS1), during flowering (HS2), and at the end of flowering (HS3). A 2‐year field experiment was conducted with three replicates. Harvest stages were randomly distributed over N rates in both years. The following agronomic parameters were evaluated: plant height, stem diameter (lower, middle, upper), number of leaves/plant, leaflet width, leaflet length, dry stem yield, fiber yield, and seed yield. The highest stem yield and fiber yield were obtained in the 240 kg ha−1 N treatment, and also the highest fiber (bast) yield and dry stem (hurd fiber) were obtained flowering and at the end of the flowering stages, respectively. When nitrogen application rate was increased from 0 to 240 kg ha−1, plant height, stem, and fiber yields increased by 48%, 65% and 57%, respectively. Agronomic parameters increased significantly (p < 0.01) when the harvesting time was moved from “before flowering” to “at the end of flowering.”

THE EFFECTS OF HARVEST TIME DURING FRUIT DEVELOPMENT ON FRUIT QUALITY AND ANTIOXIDANT CAPACITY OF KAGIZMAN LONG APPLE

The long apple cultivar is in increasing demand in the markets thanks to its unique shape, aroma, and its endemic and delicious taste. This study aimed to determine the physical and chemical changes throughout the fruit development, which indicates its optimum harvest time. Fruit samples were collected every two weeks from the last week of May to the middle of October. The fruit harvest time was evaluated depending on different quality note attributes including physical, mechanical, chemical, physicochemical, and pomological properties. The weight, fruit size, sphericity, color, soluble solids content, pH, firmness, elasticity, phenolic content (gallic acid and catechin), and organic acids (citric acid and ascorbic acid) were analyzed. Throughout the ripening period, fruit weight, fruit size, sphericity, and SSC gradually increased. There was a negative correlation between organic and phenolic contents and harvest dates. The optimum harvest time of Kagizman long apple was approximately 18 weeks after the full bloom.