Productivity and Management of Renewable Resources: Why More Efficient Fishing Fleets Should Fish Less

This article analyses the effect of productivity improvements on optimal fisheries management. It is shown that when harvest costs are independent of resource stock and the stock is below its steady state level, then for any given stock it is optimal to reduce harvest levels in response to a productivity increase unless optimal harvest rate is already zero. If harvest costs are stock dependent this result is modified; for stock dependent harvest costs there exists an interval of stock sizes below the steady state where it is optimal to reduce the harvest rate for any given stock size whereas if the harvest rate is close to an economically optimal steady state it is optimal to increase the harvest rate.

Heuristics for the sustainable harvest of wildlife in stochastic social-ecological systems

Sustainable wildlife harvest is challenging due to the complexity of uncertain social-ecological systems, and diverse stakeholder perspectives of sustainability. In these systems, semi-complex stochastic simulation models can provide heuristics that bridge the gap between highly simplified theoretical models and highly context-specific case-studies. Such heuristics allow for more nuanced recommendations in low-knowledge contexts, and an improved understanding of model sensitivity and transferability to novel contexts. We develop semi-complex Management Strategy Evaluation (MSE) models capturing dynamics and variability in ecological processes, monitoring, decision-making, and harvest implementation, under a diverse range of contexts. Results reveal the fundamental challenges of achieving sustainability in wildlife harvest. Environmental contexts were important in determining optimal harvest parameters, but overall, evaluation contexts more strongly influenced perceived outcomes, optimal harvest parameters and optimal harvest strategies. Importantly, simple composite metrics popular in the theoretical literature (e.g. focusing on maximizing yield and population persistence only) often diverged from more holistic composite metrics that include a wider range of population and harvest objectives, and better reflect the trade-offs in real world applied contexts. While adaptive harvest strategies were most frequently preferred, particularly for more complex environmental contexts (e.g. high uncertainty or variability), our simulations map out cases where these heuristics may not hold. Despite not always being the optimal solution, overall adaptive harvest strategies resulted in the least value forgone, and are likely to give the best outcomes under future climatic variability and uncertainty. This demonstrates the potential value of heuristics for guiding applied management.

Morus alba L. Leaves – Integration of Their Transcriptome and Metabolomics Dataset: Investigating Potential Genes Involved in Flavonoid Biosynthesis at Different Harvest Times

The mulberry leaf is a classic herb commonly used in traditional Chinese medicine. It has also been used as animal feed for livestock and its fruits have been made into a variety of food products. Traditionally, mulberry (Morus alba L.) leaf harvesting after frost is thought to have better medicinal properties, but the underlying mechanism remains largely unsolved. To elucidate the biological basis of mulberry leaves after frost, we first explored the content changes of various compounds in mulberry leaves at different harvest times. Significant enrichment of flavonoids was observed with a total of 224 differential metabolites after frost. Subsequently, we analyzed the transcriptomic data of mulberry leaves collected at different harvest times and successfully annotated 22,939 unigenes containing 1,695 new genes. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed 26, 20, and 59 unigenes related to flavonoids synthesis in three different groups harvested at different times. We found that the expression levels of flavonoid biosynthesis-related unigenes also increased when harvested at a delayed time, which was consistent with the flavonoid accumulation discovered by the metabolomic analysis. The results indicated that low temperature may be a key trigger in flavonoid biosynthesis of mulberry leaves by increasing the expression of flavonoid biosynthesis-related genes. This study also provided a theoretical basis for the optimal harvest time of mulberry leaves.

The Optimal Harvest Decisions for Natural and Artificial Maturation Mangoes under Uncertain Demand, Yields and Prices

This study focuses on the decisions of picking, inventory, ripening, delivering, and selling mangoes in a harvesting season. Demand, supply, and prices are uncertain, and their probability density functions are fitted based on actual trading data collected from the largest spot market in Taiwan. A stochastic programming model is formulated to minimize the expected cost under the considerations of labor, storage space, shelf life, and transportation restrictions. We implement the sample-average approximation to obtain a high-quality solution of the stochastic program. The analysis compares deterministic and stochastic solutions to assess the uncertain effect on the harvest decisions. Finally, the optimal harvest schedule of each mango variety is suggested based on the stochastic program solution.

Effect of root and butt rot uncertainty on optimal harvest schedules and expected incomes at the stand level

• Key message Root and rot (RBR) caused byHeterobasidion parviporumNiemelä & Korhonen andHeterobasidion annosum(Fr.) Bref. damages Fennoscandian spruce stands. In case the rot infection and its severity are unknown, the mere risk of infection should seldom affect the harvest timing. When it does, the gains by harvesting earlier are minimal. • Context It has been suggested that stands infected by RBR should be harvested earlier than the healthy ones. Yet, we must decide on harvest timing decisions without reliable information on the infection. • Aims We studied if harvesting earlier pays off under RBR uncertainty. • Methods We structured the uncertainty with a decision tree and calculated the optimal rotations based on expected net present values. We compared rotation lengths to those of healthy stands and calculated gains from earlier harvesting. • Results The inclusion of RBR-related uncertainty in the model changed the rotation length of only 14–23% of the stands. The average reduction was 1.3–4.7 years. Yet, the gain from harvesting earlier was too low to be considered. • Conclusion In the absence of information on the extent and severity of RBR, it seldom pays off to advance harvests. The value growth in healthy trees tends to compensate for the value reduction due to rot.

Data-based prediction of microbial contamination in herbs and identification of optimal harvest parameters

The quality of freshly harvested herbs is affected by several crucial factors, such as weather, tillage, fertilization, drying, and the harvesting process, e.g. Our main goal is to learn models that are able to predict spore contaminations in different types of herbs on the basis of information about the harvesting process, transport conditions, drying, and storage conditions. This shall enable us to identify optimal processing parameters, which will allow more effective and cost efficient contamination prevention. Using machine learning, we have generated ensembles of models that predict the risk for spore contamination on the basis of harvest processing parameters. The training information about contamination in herbs is given as results of laboratory analysis data. We applied different modeling algorithms (random forests, gradient boosting trees, genetic programming, and neural networks). In this paper we report on modeling results for yeast and mold contaminations in peppermint and nettle; e.g., for yeast contamination in peppermint we obtained models with 78.13% accuracy. Additionally, we use descriptive statistics to identify those parameters that have a statistically significant influence on the contamination; for example, our analysis shows that there seems to be a relationship between mold in peppermint and information about harrowing and the growth height (p = 0.001).

Modeling °Brix and pH in Wine Grapes from Satellite Images in Colchagua Valley, Chile

To monitor the ripeness and composition of wine grape berries and establish an optimal harvest date, the determination of °Brix and pH is vital. This research studies two harvest seasons of Cabernet Sauvignon wine grapes: 2017 and 2018. Field data were periodically collected to follow the phenological state of the fruits. In parallel, eight bands and four spectral indices from Sentinel-2 image time series were used, which are directly related to the foliage properties and activity, and indirectly to the fruit evolution. They were related to the variables measured from field samples: °Brix and pH. The °Brix models obtained with the spectral indices presented an R2 of 69% and 73% in the 2017 and 2018 seasons, respectively. In pH modeling, the 2017 season had low R2 results, reaching 43%, improving considerably in the 2018 season, reaching 63.8%. Estimated Brix and pH maps were obtained, expressing the spatial variability in the evolution of the fruit, which is useful for zoning the plots and to improve the sampling task prior to harvest. They are therefore a valuable tool to monitor the maturation, to improve the efficiency of harvest and subsequently, the quality of the wine.