Harvest Decisions Research Articles

Optimization Model for Fresh Fruit Supply Chains: Case-Study of Dragon Fruit in Vietnam

We present an optimization model for dragon fruit plantations in Vietnam. The timing of cultivating and harvesting decisions are taken into account as the dragon fruit plant has an approximately ten-year life cycle with maximum average yield in the fourth year. Another consideration also included is the prevalence of forward-buying contracts with locked-in prices. The dragon fruit supply chain faces several difficulties as yield, price, and demand are highly sensitive to weather conditions and global uncertainty factors. The risk factors in the dragon fruit supply chain also depend on species—for example, the red varieties, while more profitable than the white varieties, also have higher export risk because they are subject to global prices and adverse geopolitical conditions.

Implications of the reservation price strategy on the optimal harvest decision and production of nontimber goods in an even-aged forest stand

This study analyzes the effects of adopting an adaptive harvest strategy in even-aged forest management under timber price uncertainty on the production of nontimber goods. We use the reservation price strategy (“harvest when the observed timber prices are higher than the reservation prices”) on a longleaf pine (Pinus palustris Mill.) stand and employ the Faustmann–Hartman model as a benchmark. We assume that a longleaf pine stand can be managed for timber production, water production, carbon sequestration, and pine straw raking, depending on the planting density. Our results indicate that the reservation price strategy leads to longer expected harvest age when planting density is high. The reservation price strategy does not lead to increases in water production and carbon sequestration with low planting density. With high planting density, the reservation price strategy leads to increases in the amount of in situ carbon sequestered by 14.4–24.7 Mg·ha–1. Our findings suggest that managing longleaf pine forests in good- or poor-quality sites is a profitable alternative and enables water production and carbon sequestration.

Impacts of forest tax under timber price uncertainty

We examine the effect of forest taxes on the optimal harvest decision and the forest land value using an adaptive optimization framework– the stand is harvested when the stumpage price is equal or higher than an age-dependent reservation price. We prove that the site value tax or site productivity tax does not change the optimal reservation prices; the timber value tax reduces the optimal reservation prices; the yield tax increases the optimal reservation prices when the non-timber benefits are increasing with stand age, or when they are independent of the stand age and the regeneration cost is positive; and the unit harvest tax increases the reservation prices when stand age is relatively high and could reduce the reservation price when stand age is low. Numerical results show that the optimal harvest age is only sensitive to the unit and timber value tax. The tax burden is reduced for all types of taxes except the site value tax. Under a taxation budget constraint, recognition of stumpage price uncertainty may change forest owners’ preferences for different types of taxes.

Meta-analysis of Queensland’s coastal Indigenous fisheries: Examining the archaeological evidence for geographic and temporal patterning

Marine fisheries have been a critical part of Aboriginal and Torres Strait Islander people’s connection to land and sea country in Queensland, Australia for millennia. However, no archaeological studies have examined regional variability in the role of fish within subsistence regimes or the distribution of targeted fish species throughout the Holocene. We utilised a meta-analysis approach to conduct the first comprehensive assessment of Indigenous fisheries along the eastern Queensland coast. Data from 44 archaeological sites were grouped according to marine bioregion to facilitate broad comparison between sites across the study area. These sites were predominantly associated with mid-to-late Holocene occupation, and provided an assemblage of 45,052 recovered fish bones, of which 6606 were identified most commonly to family-level. Results indicate clear geographic patterning in the ubiquity of fish species captured, and for some marine bioregions an increase through time in the range of species targeted. Archaeological data indicate mixed species fisheries, with a complex range of habitats and diverse fish species harvested by people in relative proximity to the sites. These harvesting decisions were mediated by local ecological knowledge, awareness of fish behaviour, and cultural preference for certain species. These outcomes support existing models for the region, which document a shift in subsistence regimes during the mid-to-late Holocene, particularly an increased reliance on marine resources and expansion in diet breadth. Future research needs to address geographic gaps in data availability and implement globally recognised ichthyoarchaeological quantification and identification protocols to comprehensively examine geographic and temporal variability in Queensland’s Holocene Indigenous fisheries, and contribute to regional models of long-term subsistence change.

Bayesian information updating procedures for Pacific salmon run size indicators: evaluation in the presence and absence of auxiliary migration timing information

Preseason forecasts of Pacific salmon run size are notoriously uncertain and are thus often updated using various abundance indices collected during the run. However, interpretation of these in-season indices is confounded by uncertainty in migration timing. We assessed the performance of two Bayesian information-updating procedures for Kuskokwim River Chinook salmon (Oncorhynchus tshawytscha), one that uses auxiliary run timing information and one that does not, and compared the performance with methods that did not involve updating. We found that in-season Bayesian updating provided more accurate run size estimates during the time when harvest decisions needed to be made, but that the incorporation of run timing forecasts had little utility in terms of providing more accurate run size estimates. The latter finding is conditional on the performance of the run timing forecast model we used; a more accurate timing forecast model might yield a different conclusion. The Bayesian approach we developed provided a probabilistic expression of run size beliefs, which could be useful in a transparent risk-assessment framework for setting and altering harvest targets during the season.

Sliding mode dynamics on a prey–predator system with intermittent harvesting policy

Unsustainable fishing has been identified as one of the most important direct drivers of the degradation of coral reef ecosystems. Herbivorous reef fish, which prevent excessive accumulation of coral-suffocating algae, are in a steady decline due to the presence of invasive predators together with overfishing in many coral reef systems. Here, a two-dimensional predator–prey model is proposed that enables exploration of intermittent harvesting policies, where a control function in the model is evoked that signals when harvesting should cease and commence to maintain a herbivorous fish density above a critical threshold necessary for population stability. We analyze the model by using Filippov’s regularization method, which is ideal for discontinuous dynamic systems, such as the system explored here with intermittent harvesting. We obtain the sliding segment of the Filippov’s system and its domains together with the conditions for the existence and stability of the regular, virtual and pseudo-equilibria. We show that regular equilibria and pseudo-equilibrium can coexist. Further, we show that intermittent harvesting, with time lags between fishery harvesting decisions and implementations considered, can lead to a bounded oscillation about pseudo-equilibria, or in other words, a sustainable herbivorous fishery. Finally, using a selective harvesting policy, where the invasive predator fish is targeted (here lionfish), we show that a longer herbivorous fish harvesting period can be achieved without changing the critical threshold for harvesting.

Market Adaptive Control Function Optimization in Continuous Cover Forest Management

Economically optimal management of a continuous cover forest is considered here. Initially, there is a large number of trees of different sizes and the forest may contain several species. We want to optimize the harvest decisions over time, using continuous cover forestry, which is denoted by CCF. We maximize our objective function, the expected present value, with consideration of stochastic prices, timber quality variations and dynamically changing spatial competition. The problem is solved using an adaptive control function. The parameters of the control function are optimized via the first order optimum conditions based on a multivariate polynomial approximation of the objective function. The second order maximum conditions are investigated and used to determine relevant parameter ranges. The procedure is described and optimal results are derived for a general function multi-species CCF forest. Concrete examples from Germany, with beech, and from Sweden, with Norwegian spruce, are used to illustrate the methodology and typical numerical results. It is important to make market adapted harvest decisions. If the stochastic price variations are not considered when the harvest decisions are taken, the expected present value is reduced by 23%.

Farmer harvest decisions and vegetable loss in primary production

The topic of food loss and waste has risen in importance since the revelation that an estimated 40% of food in America is never consumed. Losses at the field level, however, are not well understood, and economic and growing conditions that dictate decisions made by fruit and vegetable growers can determine how much food is left unharvested. Many strategies have been suggested to reduce food loss and waste, but their development has been informed by concerns at the consumer level, and may not motivate growers to reduce losses. This study sought to understand how growers make decisions regarding when to end the harvest, and explores growers' perceptions of strategies that would incentivize them to reduce losses. The authors conducted seventeen semi-structured interviews with mid-sized to large commercial vegetable growers in North Carolina. The resulting findings clarify the primary decision-making drivers affecting food loss in the field, including whether growers have an interested buyer, the quality of the produce, the available price, the financial risk of product rejection, and the priority of another field becoming mature and ready to harvest. Growers did not perceive losses to be of high enough volume or value to measure crops that were left unharvested in the field, though research indicates that the volume is actually significant. We also asked growers about their perceptions of strategies for reducing farm level losses that have been promoted in industry reports on the subject. These strategies include facilitating donation and supporting emerging markets that focus on imperfect produce. Neither of these aligned well with strategies that growers perceived as important, such as increasing demand, providing processing infrastructure, and facilitating a consistent market and prices. While some growers donate produce or participate in gleaning, these activities can be limited by continued negative perceptions. Findings from this research suggest that, in order to effectively reduce the loss of edible food at the farm level, growers must be included in the development of strategies, and those strategies must incentivize their participation in order to be effective.

A conceptual model for private forest owners' harvest decisions: A qualitative study in southwest Germany

Forest development and wood supply projections can inform policy makers, industry and the public about consequences of today's actions for future forests and resource availabilities. Due to complex factor interactions, harvest decisions within the non-industrial private sector are difficult to retrace. This research develops a conceptual model capturing the harvest decision-making processes of owners of small- to medium-scale private forest properties through to the occurrence or non-occurrence of harvests on forest stands. The conceptual model was developed on the basis of 32 semi-structured qualitative interviews with forest owners. Interviews were restricted to forest property sizes up to around 100 ha located within southwest Germany. Data analysis was based on grounded theory and involved open, sensitized, selective and theoretical coding. The derived conceptual model includes the forest owner as principal agent and the forest stand embedded in the forest property as an object of interest. The forest owners' internal drivers are needs (dependencies) and wants (objectives) related to their personalities. Harvest decisions are driven by interactions of these internal drivers with phenomena related to external drivers and obstacles. Components of the system shift as a consequence of transformations (e.g. inheritance and life cycle) which create new drivers and obstacles. In contrast to owner typologies, the conceptual model explains harvest decisions as the outcome of interrelations between the involved elements. It is especially suited to supporting data selection and model building for harvest decision predictions, which can be used to inform political decisions.

Control function optimization for stochastic continuous cover forest management

Economically optimal management of continuous cover forests can be obtained via a new approach to adaptive control function optimization We maximize our objective function the expected present value with consideration of stochastic prices timber quality variations and dynamically changing spatial competition The parameters of the control function are optimized via the first order optimum conditions based on a multivariate polynomial approximation of the objective function The second order maximum conditions are investigated and used to determine relevant parameter ranges The procedure is described and optimal results are derived for general function multi species CCF forests A numerically specified model with empirical data showed that if the stochastic price variations are not considered when the harvest decisions are taken the expected present value is reduced by

Dry matter production, nutrient accumulation, and nutrient partitioning of barley

AbstractTotal dry matter (TDM) and nutrient accumulation, nutrient partitioning, and cumulative growing degree days at the time of maximum nutrient accumulation for two‐row spring barley (Hordeum vulgare L.) are not well quantified under high‐yielding irrigated conditions common in the semi‐arid western United States. Thus, five cultivars of barley were grown under irrigated conditions on a loam soil in the 2015 and 2016 growth seasons to determine these factors. Total nutrient accumulation was greatest at either the soft dough or maturity stage where specific nutrients were greater at one stage as compared to the other. Mean N accumulation was greatest at the soft dough stage (256 kg ha−1) where the regression model accounted for 80% of the variation in the data. Additionally, spike N increased from 91 to 105 kg ha−1 from soft dough to maturity. Specific nutrients (e.g., K) had significantly greater plant (i.e., culms plus leaves) accumulation between soft dough and maturity, 253 and 172 kg ha−1, respectively, where the spike at the same growth stages had an accumulation of 37 and 42 kg ha−1, respectively. In contrast, other nutrients (e.g., P) were remobilized to the spike as noted by the increase from 14 kg ha−1 at soft dough to 26 kg ha−1 at maturity. In addition to nutrient partitioning, linear regressions resulted in well‐correlated models between TDM and total nutrient accumulation (R2 = 0.35–0.88) for measured nutrients. Results from the current study provide critical data on nutrient accumulation as well as regression models for two‐row barley under high‐yielding conditions. This information can be used to improve harvest decisions as well as more accurately predict nutrient cycling in barley cropping systems.

Combined effects of cropping system and harvest date determine quality and nutritional value of pomegranate fruits (Punica granatum L. cv. Gabsi)

The oasis environment, where Tunisian pomegranates are cultivated simultaneously with several crops, was shown to fulfill ecological functions. Few studies related to the effects of environmental factors on pomegranate pre-harvest quality have been carried out, and even less have focused on the interaction between oasis environment and harvest date. This study aims to evaluate the physico-chemical and nutritional properties of pomegranate fruits cultivated under and outside oasis in order to determine the most accurate harvest indices and harvesting date in relation with cropping system. Results revealed that oasis environment was more favorable to produce large sized-fruits with high aril yield and high juice content compared to full sun conditions. Oasis pomegranates exhibited high total anthocyanin content, high hydrophilic antioxidant activity, high level of magnesium, potassium and manganese content. The visual colour of fruits and arils as well as TSS/TA ratio were found the most reliable maturity indices for taking harvest decision of pomegranate fruits. A better red coloration of fruit and arils associated with a balance between sugar content and acidity as well as high nutritional value (high mineral contents particularly Mg, K, Mn, high flavonoid and anthocyanin contents) of pomegranates harvested from different orchards at mid-october (D3) indicated their optimal use at this stage. Nevertheless, the full sun exposed fruits harvested at beginning of October (D2), characterized by pink/pink reddish colour, high TSS content and (TSS/TA) ratio and high calcium content, were considered as matured (commercial maturity) and would be ready for harvesting. Final fruit quality depends on both environmental factors and maturity stage. Based on the identified indices, Gabsi pomegranate harvesting could be started in regular full sun exposed plantations, then in orchards under date palm trees and at least one week later in traditional oases.

Optimal harvest strategy for even‐aged stands with price uncertainty and risk of natural disturbances

AbstractWe present a reservation price model to examine the joint impacts of natural disturbances and stumpage price uncertainty on the optimal harvesting decision for even‐aged forest stands. We consider a landowner who manages a loblolly pine stand to produce timber and amenities, under age‐dependent risk of wildfires and uncertainty in future timber prices. We show that the incorporation of risk of wildfires decreases the optimal reservation prices. The inclusion of risk of wildfires leads to lower land values and reduces the mean harvest age compared with the case of no‐risk of wildfires. Higher economic gains are obtained with the reservation price strategy compared with the deterministic rotation age model—a difference in the land value of $2,326 ha−1 (21%) between the two approaches.Recommendations for Resource Managers Our adaptive harvest strategy shows that the incorporation of risk of wildfires decreases the optimal reservation prices compared with the case of no‐risk of wildfires. Low reservation prices—a price that makes the landowner indifferent between harvesting or waiting longer—result in lower economic benefits for landowners and potential conversions of lands to nonforest use. Forest management practices oriented to reduce the effects of catastrophic disturbances, for example, creating a more complex forest structure with different stand densities, become imperative to ensure the sustainability of forestlands in the US South. Our analysis also suggests that the valuation of forestry investments should consider not only the risk of catastrophic events but also uncertainty in future timber prices. Higher appraisals of land value are obtained when timber price uncertainty is explicitly recognized, providing financial incentives for landowners to invest in forestry.

Remote Image Capture System to Improve Aerial Supervision for Precision Irrigation in Agriculture

Due to the limitations of drones and satellites to obtain aerial images of the crops in real time, the time to flight delay, the problems caused by adverse weather conditions and other issues, the use of fixed cameras placed on the regions of interest is essential to get closer, periodic and on-demand images. Water management in agriculture is one of the most important applications of these images. Top view images of a crop can be processed for determining the percentage of green cover (PGC), and 2D images from different viewing angles can be applied for obtaining 3D models of the crops. In both cases, the obtained data can be managed for calculating several parameters such as crop evapotranspiration, water demand, detection of water deficit and indicators about solute transport of fertilizers in the plant. For this purpose, a remote image capture system has been developed for an application in lettuce crops. The system consists of several capture nodes and a local processing base station which includes image processing algorithms to obtain key features for decision-making in irrigation and harvesting strategies. Placing multiple image capture nodes allows obtaining different observation zones that are representative of the entire crop. The nodes have been designed to have autonomous power supply and wireless connection with the base station. This station carries out irrigation and harvesting decisions using the results of the processing of the images captured by the nodes and the information of other local sensors. The wireless connection is made using the ZigBee communication architecture, supported by XBee hardware. The two main benefits of this choice are its low energy consumption and the long range of the connection.

Optimization Modeling Analysis for Grain Harvesting Management

HighlightsAn optimization model, called BioGrain, was developed to optimize grain harvesting decisions.The results highlight the tradeoffs between grain losses and drying costs for profit maximization.The optimization model can provide decision support for individual farms in different regions. Abstract. Appropriate farm management practices can improve agricultural productivity and reduce grain losses. An optimization model, called BioGrain, was developed to maximize farmers’ profits by optimizing critical grain harvesting decisions including agricultural machinery selection and harvesting schedules. This model was applied to 18 representative farms of varied sizes in Illinois, Iowa, and Minnesota. Our optimization showed that understanding crop moisture dynamics is critical for maximizing profits at the farm scale. Our results highlight the tradeoffs between grain losses and drying costs when considering profit maximization. By optimizing harvesting dates and machinery size, large farms can reduce the grain loss rate to 10%, and small farms can achieve a 5% grain loss rate. Large farms outperformed small farms on unit profits despite their higher grain loss rate. The model considers both revenue and cost related factors in harvesting decisions and quantifies the tradeoffs among corn yield, drying, and equipment selection. The model can be used to provide decision support for individual farms in different regions considering their local crop and market information. Keywords: Grain losses, Harvesting schedule, Machinery selection, Optimization, Profits.

Date Fruit Classification for Robotic Harvesting in a Natural Environment Using Deep Learning

An accurate vision system to classify and analyze fruits in real time is critical for harvesting robots to be cost-effective and efficient. However, practical success in this area is still limited, and to the best of our knowledge, there is no research in the area of machine vision for date fruits in an orchard environment. In this work, we propose an efficient machine vision framework for date fruit harvesting robots. The framework consists of three classification models used to classify date fruit images in real time according to their type, maturity, and harvesting decision. In the classification models, deep convolutional neural networks are utilized with transfer learning and fine-tuning on pre-trained models. To build a robust vision system, we create a rich image dataset of date fruit bunches in an orchard that consists of more than 8000 images of five date types in different pre-maturity and maturity stages. The dataset has a large degree of variations that reflects the challenges in the date orchard environment including variations in angles, scales, illumination conditions, and date bunches covered by bags. The proposed date fruit classification models achieve accuracies of 99.01%, 97.25%, and 98.59% with classification times of 20.6, 20.7, and 35.9 msec for the type, maturity, and harvesting decision classification tasks, respectively.

Generating Tree-Level Harvest Predictions from Forest Inventories with Random Forests

Wood supply predictions from forest inventories involve two steps. First, it is predicted whether harvests occur on a plot in a given time period. Second, for plots on which harvests are predicted to occur, the harvested volume is predicted. This research addresses this second step. For forests with more than one species and/or forests with trees of varying dimensions, overall harvested volume predictions are not satisfactory and more detailed predictions are required. The study focuses on southwest Germany where diverse forest types are found. Predictions are conducted for plots on which harvests occurred in the 2002–2012 period. For each plot, harvest probabilities of sample trees are predicted and used to derive the harvested volume (m³ over bark in 10 years) per hectare. Random forests (RFs) have become popular prediction models as they define the interactions and relationships of variables in an automatized way. However, their suitability for predicting harvest probabilities for inventory sample trees is questionable and has not yet been examined. Generalized linear mixed models (GLMMs) are suitable in this context as they can account for the nested structure of tree-level data sets (trees nested in plots). It is unclear if RFs can cope with this data structure. This research aims to clarify this question by comparing two RFs—an RF based on conditional inference trees (CTree-RF), and an RF based on classification and regression trees (CART-RF)—with a GLMM. For this purpose, the models were fitted on training data and evaluated on an independent test set. Both RFs achieved better prediction results than the GLMM. Regarding plot-level harvested volumes per ha, they achieved higher variances explained (VEs) and significantly (p < 0.05) lower mean absolute residuals when compared to the GLMM. VEs were 0.38 (CTree-RF), 0.37 (CART-RF), and 0.31 (GLMM). Root means squared errors were 138.3, 139.9 and 145.5, respectively. The research demonstrates the suitability and advantages of RFs for predicting harvest decisions on the level of inventory sample trees. RFs can become important components within the generation of business-as-usual wood supply scenarios worldwide as they are able to learn and predict harvest decisions from NFIs in an automatized and self-adapting way. The applied approach is not restricted to specific forests or harvest regimes and delivers detailed species and dimension information for the harvested volumes.

Integrating harvesting decisions in the design of agro-food supply chains

The inefficiencies observed in current agro-food supply chains, and recent trends in agro-food industry such as consolidation, increase the need for further studies on supply chain management in this field. Models are required for the complex task of determining the optimal supply chain configuration in order to improve their economic and environmental performance, while taking into account the specific characteristics of agro-food supply chains. This paper provides a general description of the supply chain design problem in agro-food industrial chains, considering the role of seasonality and harvesting decisions, perishability, and processing. A general model formulation is presented, which accommodates for these characteristics and for forward and backward flows along the chain. The general model is applied to a case study of the sugar beet processing chain in the Netherlands. The pareto-efficient frontier between maximizing the total gross margin and minimizing the global warming potential in CO2-eq is explored. Uncertainties in demand and harvest yield are taken into account using a stochastic version of the model. Results show that a supply chain design model tailored to the specific characteristics of an agro-food supply chain with its uncertainties leads to identifying better performing supply chain configurations. In the case study, supply chain configurations can be found in which the performance on both the economic and the environmental objective is better than the modelled current performance. Additionally, we observe that supply chain configurations with decentralized processing or pre-processing are an interesting topic for future research in the context of agro-food industry.

Change in the dominance structure of two marine-fish assemblages over three decades.

Marine fish are an irreplaceable resource, but are currently under threat through overfishing and climate change. To date, most of the emphasis has been on single stocks or populations of economic importance. However, commercially valuable species are embedded in assemblages of many species and there is only limited understanding of the extent to which the structure of whole communities has altered in recent years. Most assemblages are dominated by one or a few species, with these highly abundant species underpinning ecosystem services and harvesting decisions. This paper shows that there have been marked temporal changes in the dominance structure of Scottish marine-fish assemblages over the past three decades, where dominance is measured as the proportional numerical abundance of the most dominant species. We report contrasting patterns in both the identity of the dominant species and shifts in the relative abundance of the dominant in assemblages to the east and west of Scotland, UK. This result highlights the importance of multi-species analyses of harvested stocks and has implications not only for fisheries management but also for consumer choices.

Modelling relationships between visible winegrape berries and bunch maturity

Background and Aims Rapid and non-destructive techniques for measuring grape composition have the potential to improve harvest decisions and to allow the batching of fruit into parcels of similar composition to improve processing efficiency. If such techniques are to be based on the non-destructive assessment of visible berries, then relationships between the composition of visible berries and that of the whole bunch will need to be described. Methods and Results Total soluble solids, pH, TA and the concentration of anthocyanin, phenolic substances and tannin of visible berries and associated entire bunches of Shiraz and Chardonnay were measured over three seasons. Good correlations were obtained between the composition of visible berries and that of the entire bunch. Most relationships were not 1:1 but were consistent and stable across seasons. Conclusions The accuracy and stability across the seasons of relationships between the composition of visible berries and that of the entire bunch for both Chardonnay and Shiraz were evaluated. Significance of the Study The approaches used in this study to predict bunch composition from visible berry composition could aid the development of non-destructive techniques based on sensing visible berries to assess grape composition during ripening.