Harvest Scheduling Research Articles

Evaluation of carbon emission reduction effect of Cryptomeria japonica stand management in Yamagata Prefecture, Japan

Planted forests are in the utilisation phase in Japan, contributing to the increased use of timber and the realisation of carbon neutrality. We simulated and compared the carbon emission reduction effects of three forest management scenarios, including a no final cutting scenario at two geographical scales, in the context of a privately owned Cryptomeria japonica planted forest in Tsuruoka City, Yamagata Prefecture, Japan. We adopted two substitution approaches: the status quo approach and the dynamic approach in line with Japan’s nationally determined contribution. We used hypothetical stands over three rotation periods of 150 years at the stand level, and a harvest schedule of 120 years at the regional level. The harvesting scenarios based on the status quo substitution approach reduced carbon emissions more than the no final cutting scenario for current wood usage at the study site, although those based on the dynamic substitution approach reduced carbon emissions as much as the no final cutting scenario during the first 30 years. Sustainable timber production from forests may reduce carbon emissions as much as unharvested forests until 2050, indicating that the substitution effects of harvested wood products could have a significant impact on the climate change mitigation effect arising from timber.

Prediction of the Harvest Time of Cabernet Sauvignon Grapes Using Near-Infrared Spectroscopy

Harvest time assessment during the grape-ripening process can provide meaningful information for vineyard harvest scheduling. The purpose of this study was to investigate the identification of the harvest time of grape clusters using near-infrared (NIR) spectroscopy. During the harvest season from September to October 2019, bunches of Cabernet Sauvignon grapes were examined. Before establishing two classification models, namely partial least-squares discriminant analysis (PLS-DA) and support vector machine (SVM) models, raw spectra were processed by different pre-processing methods, including multiplicative signal correction (MSC), mean-centering, the standard normal variable (SNV), and the Savitzky-Golay method. Competitive adaptive weighted sampling (CARS) and the successive projections algorithm (SPA) were employed to select the optimal wavenumbers. The results indicate that NIR spectroscopy is a potentially promising approach for the rapid identification of different harvest times of Cabernet Sauvignon grapes, and the proposed technique is helpful for the prediction of ripened and over-ripened Cabernet Sauvignon grapes during the harvest time.

Comprehensive review: Effects of climate change and greenhouse gases emission relevance to environmental stress on horticultural crops and management

Global climate change exerts a significant impact on sustainable horticultural crop production and quality. Rising Global temperatures have compelled the agricultural community to adjust planting and harvesting schedules, often necessitating earlier crop cultivation. Notably, climate change introduces a suite of ominous factors, such as greenhouse gas emissions (CGHs), including elevated temperature, increased carbon dioxide (CO2) concentrations, nitrous oxide (N2O) and methane (CH4) ozone depletion (O3), and deforestation, all of which intensify environmental stresses on crops. Consequently, climate change stands poised to adversely affect crop yields and livestock production. Therefore, the primary objective of the review article is to furnish a comprehensive overview of the multifaceted factors influencing horticulture production, encompassing fruits, vegetables, and plantation crops with a particular emphasis on greenhouse gas emissions and environmental stressors such as high temperature, drought, salinity, and emission of CO2. Additionally, this review will explore the implementation of novel horticultural crop varieties and greenhouse technology that can contribute to mitigating the adverse impact of climate change on agricultural crops.

Pre-Harvest Corn Grain Moisture Estimation Using Aerial Multispectral Imagery and Machine Learning Techniques

Corn grain moisture (CGM) is critical to estimate grain maturity status and schedule harvest. Traditional methods for determining CGM range from manual scouting, destructive laboratory analyses, and weather-based dry down estimates. Such methods are either time consuming, expensive, spatially inaccurate, or subjective, therefore they are prone to errors or limitations. Realizing that precision harvest management could be critical for extracting the maximum crop value, this study evaluates the estimation of CGM at a pre-harvest stage using high-resolution (1.3 cm/pixel) multispectral imagery and machine learning techniques. Aerial imagery data were collected in the 2022 cropping season over 116 experimental corn planted plots. A total of 24 vegetation indices (VIs) were derived from imagery data along with reflectance (REF) information in the blue, green, red, red-edge, and near-infrared imaging spectrum that was initially evaluated for inter-correlations as well as subject to principal component analysis (PCA). VIs including the Green Normalized Difference Index (GNDVI), Green Chlorophyll Index (GCI), Infrared Percentage Vegetation Index (IPVI), Simple Ratio Index (SR), Normalized Difference Red-Edge Index (NDRE), and Visible Atmospherically Resistant Index (VARI) had the highest correlations with CGM (r: 0.68–0.80). Next, two state-of-the-art statistical and four machine learning (ML) models (Stepwise Linear Regression (SLR), Partial Least Squares Regression (PLSR), Artificial Neural Network (ANN), Support Vector Machine (SVM), Random Forest (RF), and K-nearest neighbor (KNN)), and their 120 derivates (six ML models × two input groups (REFs and REFs+VIs) × 10 train–test data split ratios (starting 50:50)) were formulated and evaluated for CGM estimation. The CGM estimation accuracy was impacted by the ML model and train-test data split ratio. However, the impact was not significant for the input groups. For validation over the train and entire dataset, RF performed the best at a 95:5 split ratio, and REFs+VIs as the input variables (rtrain: 0.97, rRMSEtrain: 1.17%, rentire: 0.95, rRMSEentire: 1.37%). However, when validated for the test dataset, an increase in the train–test split ratio decreased the performances of the other ML models where SVM performed the best at a 50:50 split ratio (r = 0.70, rRMSE = 2.58%) and with REFs+VIs as the input variables. The 95:5 train–test ratio showed the best performance across all the models, which may be a suitable ratio for relatively smaller or medium-sized datasets. RF was identified to be the most stable and consistent ML model (r: 0.95, rRMSE: 1.37%). Findings in the study indicate that the integration of aerial remote sensing and ML-based data-run techniques could be useful for reliably predicting CGM at the pre-harvest stage, and developing precision corn harvest scheduling and management strategies for the growers.

DIGITAL-BASED ZERO WASTE MANAGEMENT IN JASMINE INTEGRATED FARMING TO IMPROVE COMMUNITY WELFARE

Jasmine Integrated Farming is a farmer group located in community unit 19, Antapani Tengah Subdistrict, Bandung City. Jasmine Integrated Farming was founded in 2019 with all its problems, namely (1) organic waste processing that is not yet optimal, (2) irregular planting and harvest schedules, (3) catfish harvests are not optimal, (4) lack of understanding about caring for seed houses and nurseries. plants, (5) Lack of partner knowledge about digital marketing, (6) Lack of knowledge about bookkeeping and financial reports. With all its problems, it is difficult for this farmer group to generate profits for the welfare of its members. The solutions offered are training and assistance on organic waste processing, training and assistance on seeding and planting schedules, training and assistance on the use of digital media for promotions along with recording digital-based financial reports using applications. The conclusion of this service is that there is an increase in partners' knowledge about organic waste processing, planting schedules, using social media as a marketing medium and recording application-based financial reports via Buku Kas.

Influence of stocking density and environmental factors on the expression of insulin-like growth factors in cage-reared butter catfish (Ompok bimaculatus, Bloch 1794) within a large reservoir ecosystem.

In vertebrates, insulin-like growth like factors (IGFs) play an important role in growth and other physiological processes. The GH-IGF axis is considered a valuable tool to monitor fish growth performance. Herein, we report the molecular characterization of igf-1, igf-2, and β-actin transcripts and relative expression of igf-1 and igf-2 in the liver and muscle tissue of cage-reared butter catfish, Ompok bimaculatus, in response to different stocking densities (T1, 15 fingerlings m-3; T2, 25 fingerlings m-3; and T3, 35 fingerlings m-3) over 180days of culture duration. The length of the partial amplified transcript sequence of Obigf-1, Obigf-2, and Obβ-actin was 325, 438, and 924bp, respectively. Phylogenetically, Obigf-1 and Obigf-2 were closely clustered with catfishes, viz., Clarias magur, Bagarius yarrelli, and Silurus asotus. The expression of igf-1 was significantly downregulated in the liver at higher densities after 120days as biomass in the cages increased, while igf-2 expression did not change with the stocking densities over the culture period. Cortisol concentration was significantly elevated in T3 groups post 150days of the culture period and correlated negatively with the expression of igf-1 (p < 0.05) and igf-2 (p > 0.05). Environmental parameters, pH, TDS, hardness, conductivity, and alkalinity showed a significant positive correlation with hepatic IGF expression. Our study indicates that the liver-derived igf-1 plays a more important role in the regulation of growth in response to culture density in the species studied, and thus, igf-1 can be used effectively as a biomarker for growth. Furthermore, this study will help in planning a proper harvest schedule and optimize the culture practices of O. bimaculatus in an open water cage system.

Impact of Climate Change on the Production of Cardamom in one of the Chiwog under Samtse Dzongkhag, Bhutan

Cardamom plantation is one of the most lucrative commercial farming, especially in southern parts of Bhutan. Cardamom is their main source of income generation. However, the production of cardamom has declined over the past years and farmers claimed that climate change is the main reason. Climate change is a global phenomenon that has far-reaching implications for agriculture, particularly in regions characterized by their sensitivity to climatic variability and Bhutan is no exception. Nevertheless, it is unclear how climate change has affected cardamom production. Therefore, it has become necessary to study how climate change hampers the production of cardamom. The aim of this study is to explore the impact of climate change on the production of cardamom (Elettaria cardamomum) in one of the Chiwogs, under Samtse Dzongkhag. Farmers of this Chiwog mainly depend on cardamom for their livelihood and it is the backbone of their livelihood. To understand the impact of climate change on cardamom growers, the study has adopted a mixed method as a research approach. The study reveals that local farmers are already experiencing the consequences of climate change like erratic temperatures and rainfall declining the yield of cardamom. The increasing number of pests and disease outbreaks in cultivated land are creating challenges for the farmers depending on cardamom for living. Adaptive measures, such as altering planting and harvesting schedules and introducing new cardamom varieties resilient to changing conditions, are essential for mitigating these impacts. Furthermore, community- based efforts, enhanced access to climate information, and support from relevant agencies are necessary to International Journal of Scientific Research in Engineering and Management (IJSREM) Volume: 07 Issue: 12 | December - 2023 SJIF Rating: 8.176 ISSN: 2582-3930 © 2023, IJSREM | www.ijsrem.com DOI: 10.55041/IJSREM27392 | Page 2 ensure the long-term sustainability of cardamom production in the region. The study highlights the urgent need for proactive climate adaptation strategies and underscores the importance of a comprehensive understanding of climate change impacts on agricultural systems, especially in vulnerable regions. The findings provide valuable insights for policymakers, researchers, and local communities to foster resilience and sustainable cardamom production in the face of a changing climate. Keywords: climate change, cardamom production, climate adaptation, pest and diseases, socio-economic impacts. Introduction Large cardamom growers have recently encountered several difficulties maintaining production, especially because of pests and diseases, the effects of climate change, and fluctuating market prices. Climate change is the main factor that leads to a decline in cardamom production in Bhutan. Cardamom farming is becoming a less reliable source of income for mountain communities due to climate change and its effects (Gurung, 2017). The study by Ghalley (2018) concluded that the crop is being destroyed by blight, and farmers are suffering because of poor production. Farmers have been observing blight infestations on their plants for more than three years. Farmers assert that their crop has been affected by four different diseases. After the roots rot, the leaves aresaid to dry. A disease that is known as "Furkey" locally has also become a common reason for reduced yield. Farmers agree that pests and diseases are spreading at a faster rate due to changes in climatic patterns. Farmer who grows cardamom in large scale said that the harvest is so low they do not have enough to sell (Ghalley, 2018). According to Rijal and Rabyal (2020) in 2016 cardamom production was 2736 tons and drop to 2245 tons in 2017. A study by Jamtsho et al. (2020) in Pemagatsel Dzongkhag also reveals that water is essential to both the overall quality and the yieldof cardamom, too much water reduces both the yield and the production. Excessive moisture in the soil mass has the tendency to rot the plant's roots and lower stem, weakening it over time and making it more susceptible to fungus pests and diseases, which reduces the output.The commercial lifespan of large cardamom was lowered by seven to eight years because of climate change (Vijayan et al., 2014). Similarly in Nepal, the production of large cardamom has declined because of climate change (Rijal, 2013). This is an indication that there is an issue with cardamom plantations and production dueto climate change. During interviews with local leaders and field visits, the researcher found a similar situation. To better understand the issues, the researcher believes that proper study is required. In the absence of evidence- based research or scholarly works, no proper planning can be made to address this in the future. Thus, it has become necessary for educatorsto do in-depth research pertaining to the decline of cardamom production due to climate change. It is hoped that the knowledge that emerged from this study would fill this knowledge gapand point out some possible strategies for improving cardamom plantations and production for better economic income.

Harvest optimization for sustainable agriculture: The case of tea harvest scheduling

To ensure sustainability in agriculture, many optimization problems need to be solved. An important one of them is harvest scheduling problem. In this study, the harvest scheduling problem for the tea is discussed. The tea harvest problem includes the creating a harvest schedule by considering the farmers' quotas under the purchase location and factory capacity. Tea harvesting is carried out in cooperation with the farmer - factory. Factory management is interested in using its resources. So, the factory capacity, purchase location capacities and number of expeditions should be considered during the harvesting process. When the farmer's side is examined, it is seen that the real professions of farmers are different. On harvest days, farmers often cannot attend to their primary professions. Considering the harvest day preferences of farmers in creating the harvest schedule are of great importance for sustainability in agriculture. Two different mathematical models are proposed to solve this problem. The first model minimizes the number of weekly expeditions of factory vehicles within the factor and purchase location capacity restrictions. The second model minimizes the number of expeditions and aims to comply with the preferences of the farmers as much as possible. A sample application was performed in a region with 12 purchase locations, 988 farmers, and 3392 decares of tea fields. The results show that the compliance rate of farmers to harvesting preferences could be increased from 52% to 97%, and this situation did not affect the number of expeditions of the factory. This result shows that considering the farmers' preferences on the harvest day will have no negative impact on the factory. On the contrary, it was concluded that this situation increases sustainability and encouragement in agriculture. Furthermore, the results show that models are effective for solving the problem.

Modelling the forest harvesting tour problem

In a globalized market, forest management plans play an important role in the sustainability of forest enterprises. Several optimization processes have therefore been developed to support decision-making in forestry operations. However, important issues remain to be addressed, such as planning the allocation of harvesting areas and scheduling the harvesting teams that are contracted for these purposes. Harvesting schedules include different time scales and natural constraints, so that finding optimal or even good quality ones constitutes a highly complex combinatorial optimization problem. Efficient planning of harvesting operations can significantly reduce the costs associated with logistics and improve the economic performance of companies in the sector. In Uruguay, almost 75\% of total forest harvesting operations for pulp production are carried out by contractor companies, so they are an important player in the supply chain. This study aims to optimize the allocation and routing of the harvesting equipment of forest contractors, which must be located at the sites to be harvested during the year. Numerical experiments over a case study based on realistic data have shown that realistic-sized instances can be resolved by standard mathematical programming software in a reasonable time. The mathematical programming model can also be useful to evaluate potential gains in joint planning by several contractors with respect to the costs incurred by separate planning; as illustrated also with numerical examples over the same case study. This model can be used to support annual forest harvest scheduling and equipment allocation for corporate contractors, leading to better quality plans and improvement opportunities.

Optimizing Carbon Sequestration in Forest Management Plans Using Advanced Algorithms: A Case Study of Greater Khingan Mountains

The Paris Agreement aims to combat climate change by reducing greenhouse gas emissions, with bioenergy identified as a potential solution. However, concerns remain about its impact on carbon stocks and the optimal timing for implementation. To address these challenges, we propose a comprehensive multi-objective optimization model for forest management that maximizes carbon sequestration and economic benefits. Our model integrates three key components: (1) a sophisticated carbon-sequestration model encompassing living plants, wood forest products, and soil and microbial carbon uptake, (2) dynamic factors such as forest fires and extreme weather events, and (3) an economic benefits model focused on wood-processing products. We optimized the forest-management strategy over ten years by leveraging the simulated annealing and Karush–Kuhn–Tucker (KKT) algorithms. Through simulations using data from China’s Greater Khingan Mountains region, we explored the optimal logging plans for maximizing carbon sequestration without external factors. Our results revealed that the optimized logging plans significantly enhance carbon sequestration compared to proportionally averaged logging plans. Next, we investigated the impact of external factors on forest management, specifically wildfires and extreme weather events. Our findings demonstrate that wildfires have a more-substantial detrimental effect on the absolute value of carbon sequestration and the extent of improvement achieved through model optimization. At the same time, extreme cold primarily affects the growth rate of carbon sequestration. We employed a linear-weighting approach and the Analytic Hierarchy Process (AHP) to address the trade-offs between carbon sequestration and economic benefits to transform the multi-objective optimization function into a single objective. The results showed that the optimized harvesting schedule can lead to improved economic benefits compared to uniformly harvesting trees. Moreover, the joint optimization approach enabled us to identify optimal solutions that balance carbon sequestration and economic benefits, offering sustainable forest management strategies. Our study provides valuable quantitative insights into forest management strategies that balance carbon sequestration and economic benefits, making it highly relevant for real-world applications.

Impact of hail-netting on Vitis vinifera L. canopy microclimate, leaf gas exchange, fruit quality, and yield in a semi-arid environment

Hail events have the potential to destroy grapevine shoots, reduce yield, and inflict economic loss upon growers. As a result, many grape growers have adopted the use of hail-netting to mitigate potential vine damage. Although hail-netting has been observed to prevent hail damage, Texas High Plains grape growers have expressed concerns regarding effects hail-netting may have on vine canopy microclimate, grapevine health, fruit maturity, fruit quality and yield. Therefore, over three growing seasons (2018 – 2020), field-grown vines (Vitis vinifera L. ‘Malbec’ and ‘Pinot gris’) were exposed to hail-netting, or grown without hail-netting. Each growing season canopy microclimate, leaf gas exchange, fruit maturity, yield parameters, and vegetative growth were monitored. Netting reduced canopy air and leaf temperature and decreased canopy vapour pressure deficit. By modifying light infiltration and leaf temperature, hail-netting altered leaf gas exchange. In addition, gas exchange differences were found between cultivars. Although fruit pH and total acidity were not different at harvest, fruit maturity measurements revealed total soluble solid development was influenced by netting and cultivar. Vine cluster numbers were greater for vines without netting and yield parameters were generally lower for ‘Malbec’ vines. Pruning weights indicate decreased vegetative growth for hail-netting and ‘Pinot gris’ vines. Results suggest grape-growers' use of hail-netting may allow growers to achieve fruit production goals. However, when using hail-netting, growers should consider possible management modifications due to changes in vine physiology, fruit maturation, and harvest schedules.

Impact of hail-netting on Vitis vinifera L. canopy microclimate, leaf gas exchange, fruit quality, and yield in a semi-arid environment

Hail events have the potential to destroy grapevine shoots, reduce yield, and inflict economic loss upon growers. As a result, many grape growers have adopted the use of hail-netting to mitigate potential vine damage. Although hail-netting has been observed to prevent hail damage, Texas High Plains grape growers have expressed concerns regarding effects hail-netting may have on vine canopy microclimate, grapevine health, fruit maturity, fruit quality and yield. Therefore, over three growing seasons (2018 – 2020), field-grown vines (Vitis viniferaL. ‘Malbec’ and ‘Pinot gris’) were exposed to hail-netting, or grown without hail-netting. Each growing season canopy microclimate, leaf gas exchange, fruit maturity, yield parameters, and vegetative growth were monitored. Netting reduced canopy air and leaf temperature and decreased canopy vapour pressure deficit. By modifying light infiltration and leaf temperature, hail-netting altered leaf gas exchange. In addition, gas exchange differences were found between cultivars. Although fruit pH and total acidity were not different at harvest, fruit maturity measurements revealed total soluble solid development was influenced by netting and cultivar. Vine cluster numbers were greater for vines without netting and yield parameters were generally lower for ‘Malbec’ vines. Pruning weights indicate decreased vegetative growth for hail-netting and ‘Pinot gris’ vines. Results suggest grape-growers' use of hail-netting may allow growers to achieve fruit production goals. However, when using hail-netting, growers should consider possible management modifications due to changes in vine physiology, fruit maturation, and harvest schedules.

Improving spatial predictions of Eucalypt plantation growth by combining interpretable machine learning with the 3-PG model

Accurate predictions of forest plantation growth provide forest managers with improved forest inventory estimates, forest valuation, and timely harvest schedules. Forest process-based models are increasingly used for quantifying current and potential productivity, yield gaps, and factors limiting growth, such as climate variability, soil characteristics, and water deficit. Improvements in the availability and resolution of spatial and temporal data combined with advancements in machine learning algorithms provide new opportunities to improve model predictions. This study shows how interpretable machine learning (ML) can be used to independently predict site soil fertility rating (FR) and incorporate these results into the 3-PG forest process-based model to accurately predict plantation growth. Four ensemble decision tree machine learning models—random forest trees, extremely randomized trees, gradient boost, and XG boost—were trained and compared using spatial cross-validation across the study area. FR predictions were estimated in relation to the influencing soil type and terrain characteristics, and interpretable ML methods were used to show how input feature permutations may relate to the soil fertility predictions. The results show the explanatory variables are similar across the selected ML models, with the strongest influencing variables being water leaching index, site aspect, and the silt and sand soil texture properties. The extremely randomized tree models showed the overall best performance, with only a small variation in performance across the four ML models. The method was applied to Eucalyptus nitens plantations covering over 63,000 ha in north-west Tasmania, Australia. The results using the predicted FR spatial grid with 3-PG show a strong correlation with observed growth for tree diameter and stand volume (R2 tree diameter at breast height = 0.97, RMSE = 0.85 m; R2 stand volume = 0.96, RMSE = 23.1 m3 ha−1) obtained from 161 permanent sample inventory plots ranging from 3 to 31 years old. This method has practical utility for other study sites to calibrate forest plantation soil fertility rating, in both the spatial and point-scale 3-PG model, where spatial data of soil characteristics are available. The derived soil fertility grid can provide valuable insights into the spatial variability of soil fertility in unknown areas.

Comparing landscape partitioning approaches to protect wildlife habitat in managed forests

Industrial forestry activities can increase landscape fragmentation, impacting wildlife populations, particularly Canada’s woodland caribou, Rangifer tarandus caribou. To protect caribou in areas with forestry activities, the province of Ontario, Canada, implemented a Dynamic Caribou Harvest Schedule (DCHS). The DCHS spatially aggregates harvest disturbance into regions and distributes them across the landscape to maintain forest patch size–age distributions consistent with a natural variation range. However, the DCHS may negatively impact the cost of timber supply. We compared the DCHS with an alternative zoning approach that assigned the harvest deferral and operational management zones within a large forest area. We compared these approaches using an optimization model that combined harvest scheduling, access road construction, and caribou protection sub-problems. We formulated the protection of caribou habitat and road construction as network flow problems, while the harvesting problem incorporated the ecological constraints prescribed by the forest management plan. We compared the DCHS and zoning approaches in the Wabadowgang Noopming Forest of Ontario, a boreal area within the caribou distribution zone. For the same volume of sustainable harvest, the zoning approach protected less total area but more habitat and old-growth stands over the long term, and yielded lower timber costs by 1.2–2.2 $·m−3 than the DCHS.

A Living Lab approach for sustainable intensification of dairy production: A case study of an organic and a conventional farm in northern Italy

Working on feed production and feeding strategies is one of the key ways of mitigating dairy impacts and of increasing dairy efficiency and the nutritional quality of milk, all of which are priorities for global dairy sector stakeholders. Feed production, both on farm and off-farm, is one of the main contributors to dairy farm greenhouse gas (GHG) emissions, and it is known that the adopted feeding strategies influence farm nutrient balance, nutrient losses to the environment, and milk fatty acid profile. This study has been aimed at improving farm efficiency, environmental sustainability, and milk nutritional quality by acting on the management of the cropping system, and by promoting the on-farm production of feeds of high nutritional quality and high fiber digestibility in order to improve the self-sufficiency of metabolizable energy (ME) and protein (CP) for animal feeding. A multi-year study was conducted to analyze the shift in cropping system management on an organic farm and on a conventional dairy farm, considered as a “lighthouses”, and which were part of a Living Lab. Two 3-year pre-change periods (PRE) and two 3-year post-change periods (POST) with 2 years of transition were considered. The main actions performed on the management of the forage system involved changes in the crop destination, in the harvest schedule and in the conservation methods. All the data necessary to determine the dry matter (DM) yield, the forage quality, in terms of crude protein (CP), the metabolizable energy (ME), the nitrogen balance, and the carbon footprint of the two farms were measured or collected on farm. One kg of fat and protein corrected milk (FPCM) was used as a functional unit. In the POST periods, the milk production intensity (t FPCM/ha) improved and the ME and CP self-sufficiency of the heard increased on both farms. Both farms showed a 9.8% reduction of the Carbon Footprint (CF), and a reduction in the nitrogen surplus at the farm level. The amounts of C18:3 n-3 and n-3 fatty acids increased on both farms in the POST period. The results of this work show that it is possible to intensify milk production and mitigate the impacts of dairy farms by acting on the management of the forage system and on the feed strategies, using a synergistic Living Lab approach to achieve sustainable intensification.

Scheduling of forest harvesting operations on multiple cut blocks using multi-task machines

ABSTRACT The modernization of forest harvesting operations has significantly increased the cost of machine ownership and has turned forest harvesting into a capital-intensive process. To increase productivity and profitability, some companies have acquired multi-task harvesting machines. While many previous papers focused on optimizing the harvest scheduling to reduce the costs of harvesting, the assignment of multi-task machines was not considered in their models. In this work, an optimization model is developed for the detailed scheduling of harvesting activities on multiple cut blocks using multi-task machines. This model is a continuation of previous work on detailed harvest scheduling. It prescribes the start time and the end time of operations of each machine at each cut block, the number of machines to be assigned for each harvesting activity at each cut block, the cut block that the machine should move to after completing its operation at a cut block, and the type of activity it should perform. It is applied to a case study of a forest company in Canada. According to the results, the total harvesting cost decreased by Can$ 25,000 when multi-task machines were used compared to exclusive machines, due to less machine movement and the need for fewer machines.

Prototype Sistem Monitoring Nutrisi dan Tingkat pH Air pada Budidaya Hidroponik Sayur Pakcoy Menggunakan Teknologi Internet of Things (IoT)

The agricultural industry is critical in supplying food and fiber demands, but there is a demand-supply imbalance due to decreased land availability. Hydroponic techniques can be utilized to alleviate agricultural land loss by using water as a growing medium. Farmers must constantly examine plant condition and harvesting schedules. The pH of the water and nutrients are important indications for hydroponic farmers to monitor. Therefore, the implementation of the Internet of Things (IoT) allows farmers to conduct real-time monitoring more easily and regularly. IoT is defined as a system that allows integrated hardware to communicate with the internet network. The IoT device is built by attaching a NodeMCU 8266 microcontroller to an adapter, which provides electrical voltage while also regulating a water pump that delivers nutrients and water to plants. The acquired data is then transmitted to the blynk program, allowing real-time monitoring of the results via a smartphone. The present research system's usefulness and performance are improved by including pH and TDS sensors. The average accuracy of pH monitoring utilizing a pH sensor and a manual pH meter was 92.09%. Meanwhile, the average accuracy of nutrient monitoring utilizing TDS sensors and a TDS meter was 73.19%.

An integrated multi-objective mathematical model for sugarcane harvesting considering cumulative degree-days

Sugarcane is one of the main commodities in the world economy. An important step in the sugarcane supply chain is the harvest stage, requiring the support of mathematical modeling and optimization tools. Integrating the process of choosing the areas for harvesting according to the sugarcane maturation with the formation of the harvesting fronts and associating them with machine relocation costs is challenging. This paper contributes with a novel integer mathematical model that considers these concerns using three conflicting objectives. The first is that the sugarcane should be harvested as close as possible to its maturity, considering the sugarcane should be harvested as close as possible to its maturity by considering a combined measure of temperature and time since planting. The second aims to minimize the number of harvest fronts. The third objective is to minimize the costs involved with the relocation of the harvesters that cut the cane. Different strategies for obtaining Pareto-optimal solutions were proposed using exact methods, including MIP start and a decomposition technique. In the worst case, solutions with tolerable integrality gaps were obtained (inferior to 3%) in less than 1.5 h. Computational tests in real case studies were conducted in the sugarcane industry in two countries, indicating strong trade-offs in the determined solutions. In the Brazilian case, harvesting as close to maturation as possible, for example, causes an increase of 158% in the number of harvest fronts and 47% in the transportation costs, considering the minimum values for these objectives. Other trade-offs were seen and discussed when one of the other objectives was prioritized. These different compromise solutions help the decision-makers choose the most appropriate harvesting schedule according to their preferences.

"How sweet are your strawberries?": Predicting sugariness using non-destructive and affordable hardware.

Global soft fruit supply chains rely on trustworthy descriptions of product quality. However, crucial criteria such as sweetness and firmness cannot be accurately established without destroying the fruit. Since traditional alternatives are subjective assessments by human experts, it is desirable to obtain quality estimations in a consistent and non-destructive manner. The majority of research on fruit quality measurements analyzed fruits in the lab with uniform data collection. However, it is laborious and expensive to scale up to the level of the whole yield. The "harvest-first, analysis-second" method also comes too late to decide to adjust harvesting schedules. In this research, we validated our hypothesis of using in-field data acquirable via commodity hardware to obtain acceptable accuracies. The primary instance that the research concerns is the sugariness of strawberries, described by the juice's total soluble solid (TSS) content (unit: °Brix or Brix). We benchmarked the accuracy of strawberry Brix prediction using convolutional neural networks (CNN), variational autoencoders (VAE), principal component analysis (PCA), kernelized ridge regression (KRR), support vector regression (SVR), and multilayer perceptron (MLP), based on fusions of image data, environmental records, and plant load information, etc. Our results suggest that: (i) models trained by environment and plant load data can perform reliable prediction of aggregated Brix values, with the lowest RMSE at 0.59; (ii) using image data can further supplement the Brix predictions of individual fruits from (i), from 1.27 to as low up to 1.10, but they by themselves are not sufficiently reliable.

Winter cereal species, cultivar, and harvest timing affect trade-offs between forage quality and yield

Volatile feed costs and extreme weather events are contributing to greater economic risk and precarity throughout much of the United States dairy industry. These challenges have prompted dairy farmers to seek ways to reduce feed imports without compromising milk production. For organic dairy farmers, the need to produce more homegrown forage is exacerbated by the high cost and limited supply of organic feed. Integrating winter cereals for forage as part of a double-cropping system is a potential solution, but increasing the amount of forage in dairy cow rations can reduce milk production if the forages are not managed for optimal quality. Organically managed field experiments in Maryland (MD) and New York (NY) were conducted to address two primary objectives: (1) determine the yield and quality of winter cereals—four cultivars each for barley (Hordeum vulgare L.), cereal rye (Secale cereale L.), and triticale (× Triticosecale Wittm. ex A. Camus.)—grown as forage and harvested at different crop growth stages, and (2) evaluate the trade-offs between yield and quality in relation to winter cereal phenology and harvest date. Mean yield at a commonly harvested growth stage, swollen boot (Zadoks 45), was 1.3, 2.2, and 2.2 Mg ha−1 in MD and 1.8, 2.5, and 2.9 Mg ha−1 in NY for barley, cereal rye, and triticale, respectively. Mean relative forage quality (RFQ) at the same growth stage was 180, 158, and 163 in MD and 179, 156, and 157 in NY for the three species. Overall, cereal rye reached swollen boot stage the earliest, barley produced the highest RFQ and retained high quality the longest, and cereal rye and triticale produced the highest yields. Based on these results, farmers should consider barley cultivars if quality is the priority and winter-hardiness is not a concern; cereal rye cultivars if an early harvest is most important; and triticale cultivars if greater harvest schedule flexibility would be most valuable. These findings can be used to better meet the needs of dairy farmers, enhance double-cropping system performance, and improve the synchronization of harvest timing with the specific needs of lactating dairy cows, dry cows, heifers, and calves.