Productivity Model Research Articles

A Study on the Influence of Natural Fractures in Tight Sandstone Reservoirs on Hydraulic Fracture Propagation Behavior and Post-Fracture Productivity

Tight sandstone gas reservoirs are rich in reserves and are an important part of unconventional oil and gas resources. However, natural fractures’ impact on hydraulic fracture propagation behavior and network formation mechanisms remain unclear. Exploring how to optimize fracturing parameters to maximize post-fracturing productivity requires further investigation. Therefore, this study focused on the characteristics of tight sandstone gas reservoirs and established a three-dimensional numerical simulation model for hydraulic fracture propagation and post-fracturing productivity using production history matching to validate the reliability of the model. Based on this model, this study investigated the influence mechanisms of natural fracture angles, density, and lengths on hydraulic fracture propagation behavior and network formation. The spatial distribution of hydraulic fracture widths in three dimensions is also explored. When natural fracture angles are lower, a greater number of natural fractures are activated, leading to more developed secondary hydraulic fractures and the formation of complex fracture networks. Hydraulic fractures tend to penetrate directly through high-angle natural fractures. Single-well cumulative gas production increases initially with increasing natural fracture angles, then decreases, but increases with higher natural fracture density and length. Optimal fracturing in areas with longer natural fractures, lower angles, and higher density distribution enhances single-well productivity effectively.

Time series of phytoplankton net primary production reveals intense interannual variability and size‐dependent chlorophyll‐specific productivity on a continental shelf

AbstractPhytoplankton community size structure influences the production and fate of organic carbon in marine food webs and can undergo strong seasonal shifts in temperate regions. As part of the Northeast US Shelf (NES) Long‐Term Ecological Research program, we measured net primary production (NPP) rates and chlorophyll a (Chl a) concentrations in three phytoplankton size classes (< 5, 5–20, and > 20 μm) during winter and summer for 3 yr along a coastal‐to‐offshore transect. Mean depth‐integrated NPP was 37% higher in summer than winter, with limited cross‐shelf differences because of significant interannual variability. When averaged across the shelf, depth‐integrated NPP was dominated by the > 20 μm size class in winter and generated equally by the three size fractions in summer because of substantial contributions from cells > 20 μm at the Chl a maximum depth. Furthermore, the relationship between Chl a and NPP, in terms of relative contributions, varied by size class. Variations in this relationship have implications for models of primary productivity on the NES and beyond. In comparison to historical NPP data, we identified equivalent levels of winter NPP but observed a 25% decrease in summer NPP, suggesting a potential reduction in the seasonality of NPP on the NES. Together, our results highlight seasonal shifts in NPP rates of different phytoplankton size classes, with implications for food web structure and export production. These data emphasize the importance of quantifying size‐fractionated NPP over time to constrain its variability and better predict the fate of organic carbon in coastal systems under environmental change.

Studying the intensity of the energy footprint of Chinese provinces based on the Net Primary Productivity model

Rees W.E. Ecological Footprint and Appropriated Carrying Capacity: What Urban Economics Leaves out. Environment and Urbanization, 1992, vol. 4, iss. 2, pp. 121–130. URL: Link Sausheva O.S. [Ecological footprint of modern socio-economic systems: Measurement and trends]. Nauchnyi zhurnal NIU ITMO. Seriya: Ekonomika i ekologicheskii menedzhment, 2020, no. 3. (In Russ.) URL: Link Kuhn T., Pestow R., Zenker A. On the axiomatic foundation of carbon and energy footprints. Energy, Sustainability and Society, 2020, vol. 10, iss. 1. URL: Link Tetior A.N. [Town-planning development of territories: Ecological restrictions]. Prirodoobustroistvo = Environmental Engineering, 2010, no. 2, pp. 9–16. URL: Link (In Russ.) Artamonov G.E., Gutnikov V.A., Vasenev I.I. [Environmental assessment of the thermal power plants nitrogen footprint in the Russian Federation]. Problemy regional'noi ekologii = Regional Environmental Issues, 2022, no. 4, pp. 5–15. URL: Link (In Russ.) Tao Guofang, Jiang Zhaoheng. Partial matching analysis of water resources in Jilin Province based on Gini coefficient. Anhui Agricultural Sciences, 2012, no. 40(15), pp. 8694–8697. Song Zhenyang, Li Bin. Research on the Gini coefficient of resources and environment in Taiyuan City. Shanxi Chemical Industry, 2013, no. 3(4), pp. 40–42. Qin Shan Rong. Calculation of watershed ecological compensation standards based on water environment capacity. Water Conservancy Planning and Design, 2024, no. 5, pp. 26–29. Yan Min, Wang Ran. Research on spatial non-equilibrium and dynamic evolution of tourism economic contribution in Xinjiang – based on Dagum Gini coefficient and Markov chain estimation. Journal of Yili Normal University, 2024, no. 1, pp. 82–92. Shi Xingmin, Wen Wenjuan. Analysis of Gini coefficient of ecological security in Shaanxi based on water resources. Agricultural Research in Arid Areas, 2010, no. 4, pp. 233–236. Liu Yan, Li Xuan. Analysis of Gini coefficient of water resources ecological security in seven districts and counties of Shangluo City. Hubei Agricultural Sciences, 2020, no. 2, pp. 74–79. Hou Huali, Wu Shangkun, Wang Chuanjun et al. Analysis of uneven distribution of important mineral resources in China based on Gini coefficient. Resource Science, 2015, no. 5, pp. 915–920. Druckman A., Jackson T. Measuring resource inequalities: The concepts and methodology for an area-based Gini coefficient. Ecological Economics, 2008, vol. 65, iss. 2, pp. 242–252. URL: Link Wackernagel M., Onisto L., Bello P. et al. National natural capital accounting with the ecological footprint concept. Ecological Economics, 1999, vol. 29, iss. 3, pp. 375–390. URL: Link90063-5 Rees W., Wackernagel M. Urban Ecological Footprints: Why Cities Cannot be Sustainable – and Why They are a Key to Sustainability. In: Marzluff J.M. et al. Urban Ecology. Springer, Boston, MA, 2008.

The subseafloor crustal biosphere: Ocean’s hidden biogeochemical reactor

Underlying the thick sediment layer in ocean basins, the flow of seawater through the cracked and porous upper igneous crust supports a previously hidden and largely unexplored active subsurface microbial biome. Subseafloor crustal systems offer an enlarged surface area for microbial habitats and prolonged cell residence times, promoting the evolution of novel microbial lineages in the presence of steep physical and thermochemical gradients. The substantial metabolic potential and dispersal capabilities of microbial communities within these systems underscore their crucial role in biogeochemical cycling. However, the intricate interplay between fluid chemistry, temperature variations, and microbial activity remains poorly understood. These complexities introduce significant challenges in unraveling the factors that regulate microbial distribution and function within these dynamic ecosystems. Using synthesized data from previous studies, this work describes how the ocean crustal biosphere functions as a continuous-flow biogechemical reactor. It simultaneously promotes the breakdown of surface-derived organic carbon and the creation of new, chemosynthetic material, thereby enhancing element recycling and ocean carbon productivity. Insights gained from the qualitative analysis of the extent of biogeochemical microbial activity and diversity across the temperature and chemical gradients that characterize these habitats, as reviewed herein, challenge traditional models of global ocean carbon productivity and provide the development of a new conceptual framework for understanding the quantitative metabolic potential and broad dispersal of the crustal microbial biome.

Prediction of Oil Palm Plantation Block Productivity Based On Canopy Area And Vegetation Index Using Multispectral Aerial Photographs

The demands of productivity and efficiency of oil palm plantations can be met by implementing Precision Farming (PF), where production analysis is carried out on a narrower area. Currently, yield evaluation or harvest results are carried out based on block area (25-100 Ha). The diverse information in one block is standardized, potentially eliminating detailed information that is useful for management. This study aims to develop an empirical estimation model of oil palm plantation productivity per harvest day through the canopy area approach and oil palm vegetation index with multispectral camera technology. The oil palm productivity estimation method is carried out by comparing the processing results using the Normalized Difference Vegetation Index and Atmospherically Resistant Vegetation Index transformation algorithms. The basis for estimation is per Harvesting Collection Point, namely an area of ± 0.3 ha. The results showed: 1) NDVI, NDRE are strongly and very strongly correlated with FFB production; 2) canopy area has a strong relationship to FFB production; 3) NDVI vegetation index values are strongly correlated with FFB production. The productivity estimation equation from NDRE value: y = 3621.9x – 1153.8, the productivity estimation equation based on canopy area: y = 0.5479x – 717.08, and the productivity estimation equation based on NDVI: y = 11.639x – 9709.2

Application of Machine Learning in Construction Productivity at Activity Level: A Critical Review

There are two crucial resources (i.e., labor and equipment) of productivity in the construction industry. Productivity modeling of these resources would aid stakeholders in project management and improve construction scheduling and monitoring. Hence, this research aims to review machine learning (ML) applications in the process of construction productivity modeling (CPM) for construction labor productivity (CLP) and construction equipment productivity (CEP) from dataset acquisition to data analysis and evaluation, which includes their trends and applicability. An extensive analysis of 131 journals focused on the application of machine learning in construction productivity (ML-CP) from 1990 to 2024 via a mixed review methodology (bibliometric analysis and systematic review) was conducted. It can be concluded that despite the rise in automated dataset collection, the traditional method has its advantages. The review further found that the selection of ML models relies on each particular application, available data, and computational resources. Noticeably, artificial neural networks, convolutional neural networks, support vector machines, and even deep learning demonstrating have been adopted due to their effectiveness in different functionalities and processes in CPM. This study will supplement the insights gained in the review with a comprehensive understanding of how ML applications operate at each stage of CPM, enabling researchers to make future improvements.

Predicting Wheat Potential Yield in China Based on Eco-Evolutionary Optimality Principles

Accurately predicting the wheat potential yield (PY) is crucial for enhancing agricultural management and improving resilience to climate change. However, most existing crop models for wheat PY rely on type-specific parameters that describe wheat traits, which often require calibration and, in turn, reduce prediction confidence when applied across different spatial or temporal scales. In this study, we integrated eco-evolutionary optimality (EEO) principles with a universal productivity model, the Pmodel, to propose a comprehensive full-chain method for predicting wheat PY. Using this approach, we forecasted wheat PY across China under typical shared socioeconomic pathways (SSPs). Our findings highlight the following: (1) Incorporating EEO theory improves PY prediction performance compared to current parameter-based crop models. (2) In the absence of phenological responses, rising atmospheric CO2 concentrations universally benefit wheat growth and PY, while increasing temperatures have predominantly negative effects across most regions. (3) Warmer temperatures expand the window for selecting sowing dates, leading to a national trend toward earlier sowing. (4) By simultaneously considering climate impacts on wheat growth and sowing dates, we predict that PY in China’s main producing regions will significantly increase from 2020 to 2060 and remain stable under SSP126. However, under SSP370, while there is no significant trend in PY during 2020–2060, increases are expected thereafter. These results provide valuable insights for policymakers navigating the complexities of climate change and optimizing wheat production to ensure food security.

An improved instantaneous gross primary productivity model considering the difference in contributions of sunlit and shaded leaves to canopy sun-induced chlorophyll fluorescence

Sun-induced chlorophyll fluorescence (SIF) from plants offers an effective proxy for estimating gross primary productivity (GPP) by modeling SIF-GPP relationships, a widely used method to evaluate the global carbon sink. However, most SIF-GPP models ignore SIF differences between shaded and sunlit leaves, resulting in GPP underestimation, particularly in dense vegetation. This study aims to partition the contributions of sunlit and shaded leaves to canopy SIF and GPP to refine the SIF-GPP estimation model. Data from 40 eddy covariance (EC) sites representing eight major biomes and TROPOMI SIF satellite data were used for site-specific and global-scale analyses. Our results showed that the contributions of sunlit and shaded leaves to canopy SIF were 80 % and 20 %, and to canopy GPP were 55 % and 45 %, respectively. For site-specific or satellite data, the SIF-GPP relationships were the strongest for sunlit leaves (R2 > 0.51, RMSE = 4.03 μmol m−2 s−1, p < 0.001). The new SIF-GPP model, including sunlit-shaded SIF separation, can improve the accuracy of GPP estimation (R2 = 0.53, RMSE = 4.38 μmol m−2 s−1, p < 0.001). Compared with the model established with observed data, R2 was increased by 0.1, and RMSE decreased by 13.26 μmol m−2 s−1, indicating that the ‘two-leaf’ model could notably improve the SIF-GPP model. This study confirms the different contributions of sunlit and shaded leaves to canopy SIF and GPP, and ignoring this disparity would induce systematic bias in GPP estimation. Our methods and findings on sunlit-shaded SIF separation can be referenced by other studies to enhance GPP estimation accuracy.

Development of a Productivity Model at a Pharmacy Consolidated Service Center

Health systems have implemented pharmacy consolidated service centers to address increased patient volume, elevated drug costs, and decreased reimbursements. Assessing pharmacy productivity remains a challenge as metrics have historically been determined by calculations of variables that do not capture the actual work. Several investigators have demonstrated improved labor outcomes in health-system pharmacy with the use of novel productivity models. However, the utility of a novel productivity model at a pharmacy consolidated service center has not been assessed. To develop a productivity model with validation by comparison to past time periods to represent work at a Pharmacy Consolidated Service Center. The amount of time needed to complete work was determined by performing time studies. A Modified Delphi process was used to ensure appropriate perception of workload. Time standards for each category were averaged to determine the specific relative value units which were then multiplied by total biweekly orders and combined with fixed activities to determine the unit of service. Actual hours worked were obtained for six pay periods prior to compare tool productivity to actual productivity. Time studies were performed over a three-month period. Total average hours per pay period calculated by the tool for repackaging was 167.4 or 2.1 full-time equivalents (FTEs) and for warehousing was 176.8 or 2.2 FTEs. While tool productivity followed the same trends as historical calendar day productivity, it was consistently higher per pay period over the 12-week comparison. By performing time studies, a productivity model was developed for a Pharmacy Consolidated Service Center that generated productivity data that correlates with 12 weeks of data using a historical model. This study provides the ability to assess trends over time with more precise evaluation of work leading to the discussion that this tool is superior to historical productivity models.

Technological development and eco-efficiency: Drivers of total factor productivity in OECD countries

This study delves into the total factor productivity growth in OECD countries, focusing on the crucial role of technological advancement and environmental management. By utilizing the Malmquist-Luenberger index, the paper encompasses both positive and negative outputs, such as pollution, providing a comprehensive productivity analysis by breaking it down into efficiency and technical change. Data from 36 OECD countries from 2000 to 2021 were examined to uncover trends and patterns in productivity growth and its unintended environmental consequences. The results emphasize the dominant influence of technological progress, particularly after 2006, as the primary driver of productivity growth, surpassing improvements in technical efficiency. A significant increase in technical change (1.56 in 2021) compared to technical efficiency (1.05) underscores the importance of sustained investment in research and development (R&amp;amp;amp;D), which correlates positively with patent generation and technological advancement. The study also illustrates that OECD countries have effectively integrated eco-efficient practices, aligning with global trends in environmentally conscious productivity analyses. By integrating environmental outputs such as PM2.5 pollution, the analysis demonstrates that countries mitigating these adverse effects achieve higher productivity growth. These findings challenge conventional productivity models, where productivity diminishes when environmental aspects are considered. The analysis emphasizes the necessity for tailored policy approaches to address disparities in R&amp;amp;amp;D investments, technological adoption, and eco-efficiency among countries. Countries with more significant R&amp;amp;amp;D investments consistently demonstrate superior technological advancement in patents (0.745). Policymakers are urged to prioritize long-term strategies that foster technological innovation and environmental sustainability to ensure sustained productivity growth and economic resilience.

Research on the Development, Measurement, and Role of Universities in New Productive Forces

As the global economy undergoes rapid transformation, traditional productivity models based on material inputs, such as labor and capital, are increasingly replaced by new qualitative productivity, driven by knowledge, technology, and innovation. This study explores the development and measurement of new qualitative productivity, focusing on the role of universities. Key components of new qualitative productivity, including innovation, technological progress, human capital, and information resources, are examined. The Entropy Weight Method (EWM) is introduced as a tool for measuring new qualitative productivity, dynamically adjusting the weights of multiple indicators based on their variability. While current applications of EWM provide objective static measurements, the need for dynamic frameworks to capture temporal changes and the long-term impacts of innovation is emphasized. The study also evaluates the Total Factor Productivity (TFP) model as a macro-level measurement tool. Furthermore, the role of universities in advancing new qualitative productivity is highlighted through their contributions to scientific research, technology transfer, and talent development. The paper concludes by proposing the adoption of advanced dynamic measurement frameworks to better assess the sustained effects of innovation on productivity growth.

Analysis of the Impact of Clusters on Productivity of Multi-Fracturing Horizontal Well in Shale Gas

Shale gas reservoirs with nanoporous media have become one of the primary resources for natural gas development. The nanopore diameters of shale reservoirs range from 5 to 200 nm, with permeability ranging from 1 × 10−9 to 1 × 10−6 μm2. The natural gas production from shale gas reservoirs is low, necessitating the use of multi-stage hydraulic fracturing in horizontal wells. Segmented multi-cluster perforation fracturing is an effective method for shale gas extraction in these wells. The number of clusters significantly impacts the productivity of horizontal wells. Therefore, it is essential to analyze the impact of cluster numbers on fracture productivity in shale gas reservoir development. In this study, the equivalent flow resistance method was applied to establish a productivity model for multi-stage hydraulic fracturing horizontal wells in shale gas reservoirs considering diffusion and slip. An approximate analytical solution was obtained, and the effects of cluster length, diffusion coefficient, and fracture network permeability on productivity were analyzed. The results show that gas production gradually increases with the increase in the number of clusters and cluster length. However, as the number of clusters increases, the interference between clusters leads to a decrease in the productivity of individual clusters. As the fracture permeability, fracture network permeability, and diffusion coefficient increase, shale gas production also gradually increases. The permeability of the fracture network has the greatest impact on productivity. These research results are beneficial for the design of clusters in horizontal well fracturing and are of great importance for the development and production of shale gas reservoirs.

Probing an LSTM-PPO-Based reinforcement learning algorithm to solve dynamic job shop scheduling problem

With the growth of personalized demand and the continuous improvement in social productivity, the large-scale and few-variety centralized production model is gradually transitioning towards a personalized model of small batches and multiple varieties, which makes the manufacturing process of the job shop increasingly complex. Furthermore, disruptive events such as machinery failures and rush orders in the job shop increase the uncertainty and variability of the production environment. Traditional scheduling methods are usually based on fixed rules and heuristic algorithms, which are difficult to adapt to constantly changing production environments and demands. This may lead to inaccurate scheduling decisions and hinder the optimal allocation of job shop resources. To solve the dynamic job shop scheduling problem (JSP) more effectively, this paper proposes a Reinforcement Learning (RL) optimization algorithm integrating long short-term memory (LSTM) neural network and proximal policy optimization (PPO). It can dynamically adjust scheduling strategies according to the changing production environment, achieving comprehensive status awareness of the job shop environment to make optimal scheduling decisions. First, a state-aware network framework based on LSTM-PPO is proposed to achieve real-time perception of job shop state changes. Then, the state and action space of the job shop are described within the context of the state-aware network framework. Finally, an experimental environment is established to verify the algorithm’s effectiveness. Training the LSTM-PPO algorithm makes it feasible to achieve better performance than other scheduling methods. By comparing the initial planning time with the actual completion time of the rescheduling decision under different dynamic disturbances, the efficiency of the proposed algorithm is verified for the dynamic JSP.

Tracking orbital and suborbital climate variability in the westernmost Mediterranean over the past 13,000 years: New insights from paleoperspectives on marine productivity responses

This study presents a comprehensive analysis of a sediment record from the Western Alboran Basin (core GP04PC), utilizing palynological and geochemical tools to investigate marine productivity responses to orbital and suborbital climate variability over the past 13,000 years. High productivity during the Younger Dryas humid phase (∼12.4–11.7 ka) and the Holocene humidity optimum (∼10.5–8.5 ka) was driven by increased local river discharges resulting from rapid mountain glaciers melting and enhanced regional precipitation. During the late Holocene, frequent flood events linked to negative North Atlantic Oscillation (NAO) incursions potentially led to multicentennial-scale productivity increases. The findings indicate that periods characterized by wet regional conditions and increased river run-off, influenced by orbital (e.g., insolation cycles) and suborbital factors (e.g., NAO and Atlantic Meridional Overturning Circulation changes), consistently enhanced marine productivity in the Western Alboran Basin. The study also reveals that the current high productivity and carbon export in the Western Alboran Basin are maintained by active upwelling and downwelling systems driven by a persistent positive NAO phase following the southward migration of the Intertropical Convergence Zone (ITCZ) that occurred around 6.5 ka. Furthermore, geochemical proxies support a strong detrital influence on trace metal concentrations, including barium (Ba), in deep Western Alboran sediments during the Holocene. This limits the use of Ba/Al ratios for accurately reconstructing productivity changes and highlights the importance of dinocyst analysis as a complementary tool for robust marine productivity reconstructions in this region. These observations provide valuable paleoperspectives on marine ecosystem responses to climate variability, contributing to the development of robust long-term productivity models essential for adapting to ongoing environmental changes in the region, and demonstrating the strong influence of North Atlantic climate and ocean dynamics on centennial-scale productivity oscillations in this region.

Effects of winter soil warming on crop biomass carbon loss from organic matter degradation

Global warming poses an unprecedented threat to agroecosystems. Although temperature increases are more pronounced during winter than in other seasons, the impact of winter warming on crop biomass carbon has not been elucidated. Here we integrate global observational data with a decade-long field experiment to uncover a significant negative correlation between winter soil temperature and crop biomass carbon. For every degree Celsius increase in winter soil temperature, straw and grain biomass carbon decreased by 6.6 ( ± 1.7) g kg-1 and 10.2 ( ± 2.3) g kg-1, respectively. This decline is primarily attributed to the loss of soil organic matter and micronutrients induced by warming. Ignoring the adverse effects of winter warming on crop biomass carbon could result in an overestimation of total food production by 4% to 19% under future warming scenarios. Our research highlights the critical need to incorporate winter warming into agricultural productivity models for more effective climate adaptation strategies.

Regional differences and catch-up analysis of energy efficiency in China’s manufacturing industry under environmental constraints

For coordinated regional growth and the development of high-quality manufacturing, China must narrow its regional energy efficiency gap and catch up inter-regionally. This paper focuses on whether China’s inter-provincial manufacturing energy efficiency has technological diffusion and a catch-up effect and explores its possible influencing factors, which are important for narrowing the differences in China’s manufacturing energy efficiency and promoting the improvement of the overall level of efficiency. Between 2011 and 2020, 30 Chinese manufacturing industries will be evaluated using a non-radial distance function model under environmental conditions. By employing the Dagum Gini coefficient method, regional disparities were analyzed, with hyper-variable density and efficiency discrepancies between regions making a noteworthy contribution. This paper evaluated a catch-up effect by constructing a frontier productivity model that considered the influence of China’s manufacturing energy efficiency. Results show a general rise in energy efficiency, particularly in coastal regions, higher than inland ones. The Gini coefficient of energy efficiency in manufacturing experienced a slight increase; however, when comparing it to the regional efficiency frontier, the catch-up effect and technology diffusion effect of China’s provincial manufacturing energy efficiency become more pronounced when taking into account the national efficiency frontier; the sub-regional manufacturing energy efficiency catch-up effect has different performances; the catch-up and technology diffusion effect is more evident after controlling for Economic development, innovation levels, the environmental regulation, and the proportion of high-energy-consumption output value and other influencing factors.

Comparison and Optimization of Light Use Efficiency-Based Gross Primary Productivity Models in an Agroforestry Orchard

The light-use efficiency-based gross primary productivity (LUE-GPP) model is widely utilized for simulating terrestrial ecosystem carbon exchanges owing to its perceived simplicity and reliability. Variations in cloud cover and aerosol concentrations can affect ecosystem LUE, thereby influencing the performance of the LUE-GPP model, particularly in humid regions. In this study, the performance of six big-leaf LUE-GPP models and one two-leaf LUE-GPP model were evaluated in a humid agroforestry ecosystem from 2018–2020. All big-leaf LUE-GPP models yielded GPP values consistent with that derived from the eddy covariance system (GPPEC), with R2 ranging from 0.66–0.73 and RMSE ranging from 1.81–3.04 g C m−2 d−1. Differences in model performance were attributed to the differences in the quantification of temperature (Ts) and moisture constraints (Ws) and their combination forms in the models. The Ts and Ws algorithms in the eddy covariance-light-use efficiency (EF-LUE) model well characterized the environmental constraints on LUE. Simulation accuracy under the common limitation of Ts and Ws (Ts × Ws) was higher than the maximum limitation of Ts or Ws (Min (Ts, Ws)), and the combination of the Ts algorithm in the Carnegie–Ames–Stanford Approach (CASA) and the Ws algorithm in the EF-LUE model was optimized in combination forms, thereby constraining LUE for GPP estimates (GPPBLO, R2 = 0.76). Various big-leaf LUE-GPP models overestimated or underestimated GPP on sunny or cloudy days, respectively, while the two-leaf LUE-GPP model, which considered the transmission of diffuse radiation and the difference in photosynthetic capacity of canopy leaves, performed well (R2 = 0.72, p &lt; 0.01). Nevertheless, the underestimation/overestimation for shaded/sunlit leaves remained under different weather conditions. Then, the clearness index (Kt) was introduced to calculate the dynamic LUE in the big-leaf and two-leaf LUE-GPP models in the form of exponential or power functions, resulting in consistent performance even in different weather conditions and an overall higher simulation accuracy. This study confirmed the potential applicability of different LUE-GPP models and emphasized the importance of dynamic LUE on model performance.

Research of productivity and interlayer interference mechanism of multilayer co-production in reservoirs with multi-pressure systems

Abstract Due to the differences of physical and fluid properties in vertical oil layers, the interlayer contradictions are prominent and affect the reservoir utilization degree seriously. Therefore, the coordination between reasonable bottom-hole flow pressure and optimal production, as well as the reduction of interlayer interference are the primary concerns during the co-production of multi-layer oil reservoirs. Based on the reservoir engineering and fluid mechanics in porous media theory, this work adopt the comprehensive pressure system and consider the interlayer interference to establish a multi-branch horizontal well productivity model and interlayer interference mathematical model. By analyzing the main controlling factors and interference mechanisms, this article establishes a pattern of interlayer interference, and improves the quantitative characterization interlayer interference theory in multi-layer combined mining. The study has shown that: (1) The interlayer interference is beneficial for balancing the production of different layers and improving development efficiency, it is greatly affected by interlayer heterogeneity; (2) When the number of layers exceeds 2 layers, the interference coefficient increases; With the increase of the layer thickness, the thicker oil layers have higher productivity, and the thickness of layer has a significant effect on the production of low-pressure layer; When the production pressure difference increases, the interlayer interference can be reduced; (3) The interlayer interference mathematical model constructed in this paper has high prediction accuracy and strong practicality, which has theoretical guidance significance for the division of strata in comprehensive adjustment of reservoirs.

Simulation model for electrical and agricultural productivity of an olive hedgerow Agrivoltaic system

Given the need to promote a more sustainable energy model that contributes to the fight against Climate Change and the consequent advancement of photovoltaics, Agrivoltaics is presented as a solution to the conflict over land use, since it makes it possible to reconcile agricultural production and photovoltaic power simultaneously on the same piece of land. However, it is a new production model and there are still many aspects to research. This work presents a mathematical model to simulate the spatial distribution of the solar radiation in the canopy of the crop of an agrivoltaic plant in which N-S solar trackers are alternately combined with olive groves in hedgerows. In this way, simulation has the advantage of predicting the expected behaviour of one or more configurations of agrivoltaic installations and, on the basis of this simulated behaviour, making decisions during the design phase to optimize their performance. To do this, various models have been integrated to simulate both the behaviour of irradiance on its way from the Sun to the crop, using geometric-vector analysis, and the irradiance absorption processes on its way through the canopy of the crop. Thus, the global model presented allows estimating the electrical and oil production of an agrivoltaic plant such as the one described. From this global model, electrical and oil productions have been systematically simulated for a set of agrivoltaic plants whose design parameters range around the intermediate values of an agrivoltaic facility with olive groves in hedgerows spaced 10 m apart and alternated with 3 m wide and 3 m hight N-S solar trackers. For this intermediate facility, the annual productions of oil and electricity are 789kg/year·ha and 891MWh/year·ha, respectively. Finally, the oil and energy productions of all the simulated agrivoltaic facilities and their design parameters have been interrelated obtaining mathematical equations to determine in a simple way the influence on the productions of the geometrical design variables of the agrivoltaic installation (height of the solar trackers, width of the collectors and distance between hedgerows). Therefore, the model presented makes it possible to advance in the evaluation of the possible integration of olive groves in agrivoltaic systems and their profitability, constituting an important benefit for the implementation of agrivoltaics in the Mediterranean regions where olive groves play a significant role in agricultural production systems.

Evaluating variation of respiration:photosynthesis ratio in sagebrush species: Implications for carbon flux modeling

AbstractPlant respiration and photosynthesis are the two main processes influencing carbon (C) flux balance at leaf‐to‐ecosystem scales. The ratio of respiration to photosynthesis (R:A) or carbon use efficiency (CUE) is considered an important trait for determining global carbon storage in the near future. One school of thought assumes that R:A is constant in terrestrial productivity models, irrespective of biomass, climate, and species. Others believe it is variable, although within a limited range. Semiarid systems dominated by woody vegetation, such as sagebrush steppe, have been recognized as potentially important C sinks on regional to global scales in the context of future climate scenarios. Therefore, there is a critical need to study R:A over different organizational scales (i.e., at the leaf, whole plant, and ecosystem scales) to use this approach for future C flux predictions under climate change scenarios. The objective of this study was to compare leaf‐, shrub‐, and ecosystem‐scale R:A among three sagebrush (Artemisia spp.) communities, and to determine how R:A varies throughout the growing season (i.e., early, mid‐, and late summer) among these communities. We measured photosynthesis and respiration monthly in three sagebrush communities spanning a 685‐m elevation gradient at the Reynolds Creek Experimental Watershed and Critical Zone Observatory in southwestern Idaho. Consistent with our expectations, we found large seasonal variations in R and A at all scales, but with differences in A among the three sagebrush communities significant only at the leaf scale. The R:A ratio was not significantly different among the three species at all organizational scales. However, the R:A ratio did vary among months at the leaf level and there was a statistical interaction between species and month at both leaf and shrub levels. Our study indicates that the R:A ratio is generally conservative, although not tightly constrained (range: 0.12–0.77) among three sagebrush species. Therefore, approaches that assume conservative R:A ratios in terrestrial productivity models need to be considered carefully to evaluate the impact of projected climatic changes on future C cycling in shrub‐dominated rangeland ecosystems.