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Over the last few years, the governments' fiscal revenue and expenditure have been significantly affected by the rapid growth of global digital economy. Despite the significant role that digital economy plays in improving the ability of the government to generate fiscal revenue, there is relatively little empirical evidence of this. Therefore, this study aims to reveal the impact of digital economy on the urban fiscal effort of China by analyzing the data recorded from 2011 to 2019. According to the empirical findings, digital economy can significantly improve the level of fiscal effort. Meanwhile, digital economy has a significant nonlinear effect on fiscal effort. As suggested by the results of expansion analysis, the effect of digital economy on fiscal effort shows significant regional heterogeneity and the spatial spillover effect is positive. Based on the findings of this study, it is recommended to strengthen the construction of digital infrastructure, optimize the practice of fiscal revenue management under the context of digital economy, and improve the governments' fiscal situation by paying attention to the coordinated development of digital economy among various regions. [ABSTRACT FROM AUTHOR]

In today's hotel industry, 'book now, pay later' practice is widespread. However, despite the prevalence of this phenomenon, the psychological and behavioral influences of 'book now, pay later' transactions on consumers have received almost no research attention. Using an experimental design, this research examined how deferred payments influence consumers' perceived price, perceived risks, and purchase intention according to the time of booking in the leisure travel context. The results demonstrated that people perceived less risks and had a greater purchase intention when a hotel offered the 'pay later' option compared to the 'pay now' option. However, individuals planning a trip in the near future did not show any significant differences in perceived risks and purchase intention between the options of 'pay later' and 'pay now'. [ABSTRACT FROM AUTHOR]

Motivated by operations research applications, such as inventory control and real-time bidding, we consider undiscounted reinforcement learning in Markov decision processes under model uncertainty and temporal drifts. In this setting, both the latent reward and state transition distributions are allowed to evolve over time, as long as their respective total variations, quantified by suitable metrics, do not exceed certain variation budgets. We first develop the sliding window upper confidence bound for reinforcement learning with confidence-widening (SWUCRL2-CW) algorithm and establish its dynamic regret bound when the variation budgets are known. In addition, we propose the bandit-over-reinforcement learning algorithm to adaptively tune the SWUCRL2-CW algorithm to achieve the same dynamic regret bound but in a parameter-free manner (i.e., without knowing the variation budgets). Finally, we conduct numerical experiments to show that our proposed algorithms achieve superior empirical performance compared with existing algorithms. Notably, under nonstationarity, historical data samples may falsely indicate that state transition rarely happens. This thus presents a significant challenge when one tries to apply the conventional optimism in the face of uncertainty principle to achieve a low dynamic regret bound. We overcome this challenge by proposing a novel confidence-widening technique that incorporates additional optimism into our learning algorithms. To extend our theoretical findings, we demonstrate, in the context of single-item inventory control with lost sales, fixed cost, and zero lead time, how one can leverage special structures on the state transition distributions to achieve improved dynamic regret bound in time-varying demand environments. This paper was accepted by J. George Shanthikumar, data science. Funding: The authors acknowledge support from the Massachusetts Institute of Technology (MIT) Data Science Laboratory and the MIT–IBM partnership in artificial intelligence. W. C. Cheung acknowledges support from the Singapore Ministry of Education [Tier 2 Grant MOE-T2EP20121-0012]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4704. [ABSTRACT FROM AUTHOR]

In this paper, we study a family of sales-based rebate mechanisms as an effective tool to implement price discrimination across a population of buyers with correlated heterogeneous valuations on indivisible goods and services. In order to implement such sales-based rebate mechanisms, the seller charges each buyer a fixed price at the time of purchase contingent on a rebate that is a function of the ex post sales volume to be realized at the end of the sales period. The seller declares both a price and a menu of rebates as a function of sales. We show that, when there is a common component of uncertainty in consumers' valuations (to which we refer as the quality of the product), such rebates enable a seller to effectively induce different expected net prices at different valuations. Importantly, this effective price discrimination over valuations is achieved keeping both the base price and the rebate uniform across all buyers. This uniformity of price and rebate across buyers is a key advantage of our proposed rebate mechanism, thereby providing a new mechanism for price discrimination in crowd-based markets. We use tools and techniques from game theory and variational optimization to provide insight into the economics of such mechanisms. In particular, we identify two mechanisms that are monotone functions of the sales volume that are easy to implement in practice and perform well when compared with the much more complex optimal mechanism. We provide a rigorous analysis of the optimal mechanism and discuss practical limitations in implementing the globally optimal design, further demonstrating the efficacy of our proposed monotone mechanisms. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Funding: This research was supported by a Vannevar Bush Fellowship from the Office of Secretary of State and Army Research Office [MURI Project W911NF-19-1-0217]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4691. [ABSTRACT FROM AUTHOR]

Historical data are typically limited. We study the following fundamental data-driven pricing problem. How can/should a decision maker price its product based on data at a single historical price? How valuable is such data? We consider a decision maker who optimizes over (potentially randomized) pricing policies to maximize the worst-case ratio of the garnered revenue compared to an oracle with full knowledge of the distribution of values, when the latter is only assumed to belong to a broad nonparametric set. In particular, our framework applies to the widely used regular and monotone nondecreasing hazard rate (mhr) classes of distributions. For settings where the seller knows the exact probability of sale associated with one historical price or only a confidence interval for it, we fully characterize optimal performance and near-optimal pricing algorithms that adjust to the information at hand. The framework we develop is general and allows to characterize optimal performance for deterministic or more general randomized mechanisms and leads to fundamental novel insights on the value of data for pricing. As examples, against mhr distributions, we show that it is possible to guarantee 85% of oracle performance if one knows that half of the customers have bought at the historical price, and if only 1% of the customers bought, it still possible to guarantee 51% of oracle performance. This paper was accepted by David Simchi-Levi, revenue management and market analytics. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4683. [ABSTRACT FROM AUTHOR]

We consider a platform facilitating trade between sellers and buyers with the objective of maximizing consumer surplus. Even though in many such marketplaces, prices are set by revenue-maximizing sellers, platforms can influence prices through (i) price-dependent promotion policies that can increase demand for a product by featuring it in a prominent position on the web page and (ii) the information revealed to sellers about the value of being promoted. Identifying effective joint information design and promotion policies is a challenging dynamic problem as sellers can sequentially learn the promotion value from sales observations and update prices accordingly. We introduce the notion of confounding promotion policies, which are designed to prevent a Bayesian seller from learning the promotion value (at the expense of the short-run loss of diverting some consumers from the best product offering). Leveraging these policies, we characterize the maximum long-run average consumer surplus that is achievable through joint information design and promotion policies when the seller sets prices myopically. We then construct a Bayesian Nash equilibrium, in which the seller's best response to the platform's optimal policy is to price myopically in every period. Moreover, the equilibrium we identify is platform optimal within the class of horizon-maximin equilibria, in which strategies are not predicated on precise knowledge of the horizon length and are designed to maximize payoff over the worst-case horizon. Our analysis allows one to identify practical long-run average optimal platform policies in a broad range of demand models. This paper was accepted by David Simchi-Levi, revenue management and market analytics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.4677. [ABSTRACT FROM AUTHOR]

Airfares evolve dynamically, giving rise to a so-called price path. This price path is controlled via two levers: (i) a fare ladder, which defines a set of airfares before the selling season, and (ii) revenue management algorithms, which control how fares evolve along the ladder during the season. We hypothesize that the current policies to control both levers—which do not account for quality differences between competing airlines—give rise to an inefficient price path and, accordingly, a loss of potential revenue. We substantiate this hypothesis via a field experiment. By partnering with an airline, we introduced quality considerations in the design of fare ladders, across 5,000 itineraries, to show that current ladder-design policies indeed lead to a suboptimal price path. We also show that this inefficiency can be mitigated by incorporating quality differences between competing airlines. This creates a smoother (and more profitable) price path. This paper was accepted by Vishal Gaur, operations management. [ABSTRACT FROM AUTHOR]

We study the problem when a firm sets prices for products based on the transaction data, that is, which product past customers chose from an assortment and what were the historical prices that they observed. Our approach does not impose a model on the distribution of the customers' valuations and only assumes, instead, that purchase choices satisfy incentive-compatible constraints. The uncertainty set of potential valuations of each past customer can then be encoded as a polyhedral set, and our approach maximizes the worst case revenue, assuming that new customers' valuations are drawn from the empirical distribution implied by the collection of such polyhedra. We study the single-product case analytically and relate it to the traditional model-based approach. Then, we show that the optimal prices in the general case can be approximated at any arbitrary precision by solving a compact mixed-integer linear program. We further design three approximation strategies that are of low computational complexity and interpretable. In particular, the cutoff pricing heuristic has a competent provable performance guarantee. Comprehensive numerical studies based on synthetic and real data suggest that our pricing approach is uniquely beneficial when the historical data has a limited size or is susceptible to model misspecification. This paper was accepted by Omar Besbes, revenue management and market analytics. Funding: This work was supported by the Rotman School of Management (TD-MDAL Research Grant) and the Natural Sciences and Engineering Research Council of Canada [Grants RGPIN-2015-06757, RGPIN-2020-04038, RGPIN-2020-06054, RGPIN-2021-04295]. Supplemental Material: The e-companion and data files are available at https://doi.org/10.1287/mnsc.2022.4651. [ABSTRACT FROM AUTHOR]

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Cauliflower and tomato are two most important cash crops of the North-Western part of Himalayas often grown in sequence and bring remunerative returns to the farmers of mid-hill region. The study was undertaken in order to evaluate the effect of application of various nutrient combinations through organic and inorganic inputs on the post harvest soil fertility status, crop quality parameters, nutrient use efficiencies, nutrient harvest indices, and economics of the cauliflower–tomato production system. The total of 11 treatment combinations, comprising sole organics, graded doses of inorganics and substitution of inorganics with organic inputs were studied. The study concluded that the application of 75% NPK + 100% FYM + 25% recommended N through FYM and VC on 50:50 N-equivalence basis significantly improved most of the quality and yield parameters in both the crops simultaneously giving highest N (197.8 kg ha−1) and K (167.4 kg ha−1) uptake, NUE (46.3%), AE (57.0 kg kg−1), P harvest index (50.7%) and net returns (5476 US$ ha−1) in cauliflower. This treatment was also accorded with significantly highest N (141.9 kg ha−1), P (30.6 kg ha−1), and K (185.0 kg ha−1) uptake, NUE (33.6%), AE (115.4 kg kg−1), and net returns (7858 US$ ha−1) in tomato. Hence, it is concluded that 75% NPK + 100% FYM + 25% recommended N through FYM and VC on 50:50 N-equivalence basis may be adopted as cost-effective nutrient module for farmers following cauliflower–tomato cropping sequence to achieve higher crop production on sustainable basis. [ABSTRACT FROM AUTHOR]

Various transportation services exist, such as ride-sharing or shared taxis, in which customers receive services in a batch of flexible sizes and share fees. In this study, we conducted an equilibrium analysis of a variable batch service model in which customers who observe no waiting customers in an incomplete batch can strategically select a batch size to maximize the individual utilities. We formulated this model as a three-dimensional Markov chain and created a book-type transition diagram. To consider the joining/balking dilemma of customers for this model, we proposed an effective algorithm to construct a necessary and sufficient size of state space for the Markov chain provided that all customers adopt the threshold-type equilibrium strategy. Moreover, we proved that the best batch size is a non-decreasing function for i if the reward for the completion of batch service with size l is an increasing function of l assuming that a tagged customer observes i complete batches in the system upon arrival; in other words, the fee decreases as the batch becomes larger. We then derive several performance measures, such as throughput, social welfare, and monopolist's revenue. Throughout the numerical experiment, a comparison between the present variable batch service model and regular batch service model in which customers were served in a constant batch, was discussed. It was demonstrated that the three performance measures can be optimized simultaneously in the variable batch service model, as long as the fee was set relatively high. [ABSTRACT FROM AUTHOR]

The development potentials of decentralised local governance notwithstanding, subnational structures remain fiscally challenged. Given revenue indispensability, local citizens' apathy, and fiscal obligation nonfulfillment, this study probes rate evasion behavior in Ghana using the theory of planned behavior. Data was drawn from 507 respondents using questionnaires. Hypothesis test using the structural equation modeling technique confirmed TPB's predictive potency—as evidenced in increased variance from 20 to 46% (∆ R2 = 26%, p <.001). The study concludes by entreating public revenue management organizations and subnational states to countervail evasion behavior by emphasising transparency and good fiscal governance. [ABSTRACT FROM AUTHOR]

This study examined the existing legal framework for mobilizing, distributing, and utilizing customary land revenue in Ghana. It combined a review of the regulatory framework on land revenue management and face-to-face interviews with Officers of the Administrator of Stool Lands with the view to identifying the gaps inherent in these regulations. The study found that the lack of legislative clarity on the mobilization and application of stool land revenue has been responsible for the misappropriation of funds by beneficiaries. Legislative reform is recommended to remedy the inherent gaps in the land revenue management regime in Ghana. [ABSTRACT FROM AUTHOR]

A study was conducted during the kharif (June-September) season of 2018 at G.B.P.U.A.&T., Pantnagar, Uttarakhand, India to calibrate and validate the CROPGRO-soybean model for Terai region and determine the optimal management practices for soybean variety PS1347. The experiment followed a Two Factorial Randomized Block Design, involving two fertilizer treatments with three sowing dates replicated thrice. The CROPGRO model was calibrated and validated using the field data of 2018 and 2017, respectively. The RMSE% between the observed and simulated values of different crop stages and yield indicators were between 3.24% and 14.22%. Subsequently, the crop yield for different management practices was also simulated. The model encompassed yield simulations for various management practices viz., six tillage methods, five fertilizer treatments, nine sowing dates, and six irrigation levels. To achieve error minimization, soybean yield was simulated for four consecutive years (2015-2018) under various management practices, and the data was averaged. The simulation results indicated that the optimal management practices for achieving the highest soybean yield include sowing the seeds on the 20th June after implementing a tillage regimen involving three ploughings and two harrowing operations and further nourishing the crop with fertilizer dose of N:P:K:S::25:60:40:20 and applying 90 mm of irrigation. These findings will aid in optimizing management practices and developing sustainable and efficient approaches to achieve higher soybean yields with minimal inputs. [ABSTRACT FROM AUTHOR]

When the number of tasks is large, how should a firm design reward and penalty schemes to incentivize its employees? In "Dynamic Mechanism Design with Capacity Constraint," He studies the role of capacity constraint in a project assignment problem, where a principal needs to assign multiple tasks to an agent. The author fully characterizes the optimal mechanism via a sequence of deadlines. This characterization is used to show that the presence of the capacity constraint reduces the principal's payoff and delays the completion of projects. It further illustrates that the widely adopted no-capacity constraint framework may provide inaccurate results in various dynamic problems. We study a project assignment problem, where a principal needs to assign multiple projects to an agent. The agent is privately informed about the cost, which could be high or low. The agent's type evolves stochastically over time. We fully characterize the optimal mechanism via a sequence of deadlines and show that the presence of the capacity constraint reduces the principal's payoff and delays the assignment of projects. In particular, as the number of projects increases, the limit optimal contract may be strictly bounded away from the optimal contract when there are infinitely many projects, and the principal's payoff may be strictly below that in the setting without the capacity constraint. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72122023]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.2449. [ABSTRACT FROM AUTHOR]

Using novel data on the pricing for hotel rooms in Paris during the summer of the 2016 European Football Cup, we document the co-existence of uniform and bespoke pricing within the same industry and even within the same firm. Although stay dates have heterogenous potential demand, hotels initially price them uniformly. However, as time goes by, they progressively set customized prices. We show that standard revenue management techniques can explain the shift and find evidence that the inability to form expectations about future demand was a determinant of the initial choice of uniform prices. Finally, we find that firm characteristics have a role in shaping the transition. [ABSTRACT FROM AUTHOR]

Problem definition: Motivated by several practical selling scenarios that require previous purchases to unlock future options, we consider a multistage assortment optimization problem, where the seller makes sequential assortment decisions with commitment and the customer makes sequential choices to maximize her expected utility. Methodology/results: We start with the two-stage problem and formulate it as a dynamic combinatorial optimization problem. We show that this problem is polynomial-time solvable when the customer is fully myopic or fully forward-looking. In particular, when the customer is fully forward-looking, the optimal policy entails that the assortment in each stage is revenue-ordered, and a product with higher revenue always leads to a wider range of future options. Moreover, we find that the optimal assortment in the first stage must be smaller than the optimal assortment when there is no second stage and the optimal assortment in the second stage must be larger than the optimal assortment when there is no first stage. When the customer is partially forward-looking, we show that the problem is NP-hard in general. In this case, we establish the polynomial-time solvability under certain conditions. In addition, we propose a 2-approximation algorithm in the general setting. We further extend these results to the multistage problem with an arbitrary number of stages, for which we derive generalized structural properties and efficient algorithms. Managerial implications: Firms can benefit from our study and improve their sequential assortment strategies when their interaction with each customer consists of multiple stages. Funding: This work was supported by the National Science Foundation of China [Grant 72150002] and Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1224. [ABSTRACT FROM AUTHOR]

This study's objective was to investigate the corporate governance, company performance, and corporate social responsibility factors that influence earnings management in the food and beverage sector listed on the Indonesia Stock Exchange. This study employs an exploratory descriptive approach to identify indications of the impact of corporate governance, corporate performance, and corporate social responsibility on corporate earnings management. The research sample comprises as many as 34 food and beverage manufacturing companies. This study employs panel data regression for its data analysis method. Using E-Views 10.0, the available data was examined. Results indicated there was no effect of corporate governance on firm performance, no effect of corporate governance on earnings management, an effect of corporate responsibility on corporate responsibility, a negative effect of firm performance on earnings management, a positive influence of corporate responsibility on earnings management, and a negative effect of corporate governance on earnings management that was statistically significant. Implications of the research: corporate governance should focus on earnings management by evaluating company performance, corporate governance, and corporate responsibility. This study can be used as a foundation for developing prior research in order to obtain empirical evidence regarding the factors that influence earnings management. [ABSTRACT FROM AUTHOR]

Weather is one of the critical factors that influence tourists' destination choices and activities. Apart from ambient temperature anomaly, rain anomaly is also an important factor considered by tourists when they plan and modify their vacation and holiday trips. This study confirms the important role of abnormal weather conditions in explaining hotel performance, such as occupancy, average daily rate, and revenue per available room. Moreover, operational performance indicators are observed to exhibit dynamic patterns in response to abnormal weather conditions in accordance with different types/classes of hotels. Evidence indicates that tourists prefer to stay at full-service hotels with complete facilities rather than at hotels with limited facilities and services during an abnormally heavy rain situation. Therefore, the findings of this research suggest a useful determinant (i.e., weather changes) of revenue management practices for hotel firms to maximize their operating performance. [ABSTRACT FROM AUTHOR]

Strategic decisions are increasingly delegated to algorithms. We extend previous results of the algorithmic collusion literature to the context of dynamic optimization with imperfect monitoring by analyzing a setting where a limited number of agents use simple and independent machine-learning algorithms to buy and sell a storable good. No specific instruction is given to them, only that their objective is to maximize profits based solely on past market prices and payoffs. With an original application to battery operations, we observe that the algorithms learn quickly to reach seemingly collusive decisions, despite the absence of any formal communication between them. Building on the findings of the existing literature on algorithmic collusion, we show that seeming collusion could originate in imperfect exploration rather than excessive algorithmic sophistication. We then show that a regulator may succeed in disciplining the market to produce socially desirable outcomes by enforcing decentralized learning or with adequate intervention during the learning process. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.4623. [ABSTRACT FROM AUTHOR]

In markets, such as those for airline tickets and hotel accommodations, firms sell time-dated products and have private information about unsold capacities. We show that competition under private information may explain observed phenomena, such as increased price dispersion and higher expected prices toward the deadline. We also show that private information severely limits the market power of firms and that information exchange about capacity increases firms' profits. Finally, we inquire into the incentives to unilaterally disclose information or to engage in espionage about rival's capacity and show that they increase firms' profits compared with the private information setting. This paper was accepted by Omar Besbes, revenue management and market analytics. Funding: This work was partially funded by the Austrian Science Foundation FWF under [Project FG 6]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4613. [ABSTRACT FROM AUTHOR]

Purpose: Integrated and organic nutrient management has recently become the focus in Sri Lanka for seeking better perspectives on food quality and environmentally friendly production. This study was conducted to understand the magnitude of yield and yield components under selective nutrient management systems in major cropping seasons within the transitional period of a conventional rice-based cropping system. Research Method: Rice yield components and yield were measured with different nutrient management systems; conventional, integrated, and organic from Yala 2019 to Maha 2020/21. An ANOVA was carried out using the Repeated Measures MIXED model to determine the effect of nutrient management systems on yield and yield components for four continuous cropping seasons. Findings: Total tillers per hill and productive tillers per hill significantly varied with conventional, integrated, and organic systems in descending order. The number of filled spikelets per panicle (43) was significantly increased, and the number of hollow spikelets per panicle (8) and thousand-grain weight (21.5g) were significantly decreased with an organic system in the Yala 2019 season only. Although the biological and expected grain yields of the Yala 2019 season were significantly higher with the conventional and integrated systems, these did not change significantly with the organic system in the last two seasons. Research Limitations: Yield parameters fluctuated due to weather changes in different seasons; thus specific impacts of different nutrient managements have been masked to a certain degree. Originality/Value: The attempt to convert conventional crop production systems in Sri Lanka to organic can be effectively achieved using the integrated use of nutrients and crop rotations. [ABSTRACT FROM AUTHOR]

The impact of sporting events on local economies has been a focus of academic research for many years. Sporting events create externalities within the local economies in the form of spillover effects. This study investigates the role of Southeastern Conference collegiate football games on local hotel demand from 2003 to 2017. Fixed effects models are used to expand upon previous research by incorporating six data sources to analyze the impact of team, game, hotel, and market characteristics on hotel performance. Results indicate that the demand for hotels varies greatly according to team and opponent quality. A number of sport marketing, sport economics, hospitality, and tourism management implications are discussed for universities and industry in their communities regarding scheduling and the potential for revenue growth. [ABSTRACT FROM AUTHOR]

When selling multiple products with asymmetric uncertainty, should the seller disclose product information so that customers do not have to incur any cost to resolve their uncertainties; if so, which product should the seller choose? To address these questions, we consider a monopolist selling two substitutable products to a group of consumers. Each consumer has asymmetric uncertainty regarding the two products. A total of four different information provision structures are considered based on whether the seller discloses information about each product with the aim of determining which strategy provides the seller with the greatest revenue. We derive several interesting results. First, the optimal information provision strategy depends on the magnitude of uncertainty in relation to the product with lower uncertainty. Specifically, if the uncertainty regarding the product with lower uncertainty is sufficiently small, it is optimal for the seller to provide information about the product with higher uncertainty, otherwise, the seller should provide information about both products. Second, when only one product's information should be revealed, it is optimal for the seller to choose the product with higher uncertainty and charge a higher price. Third, withholding information on both products is never optimal for the seller. Finally, our main model is extended by examining the Mean-Preserving Spread setting, and the robustness of our main results is confirmed. Furthermore, we examine the situation in which a monopolist sells a single product with two main attributes. We find that each of the four information provision strategies can be optimal under various scenarios.

The Revenue Management (RM) problem in airlines for a fixed capacity, single resource and two classes has been solved before by using a standard formalism. In this paper we propose a model for RM by using the semi-classical approach of the Quantum Harmonic Oscillator. We then extend the model to include external factors affecting the people’s decisions, particularly those where collective decisions emerge.

Sustainable intensification schemes that increase crop production and soil fertility, such as integrated soil fertility management (ISFM), are a proposed strategy to close yield gaps and achieve food security in sub-Saharan Africa while maintaining soil fertility. However, field trials are insufficient to estimate the potential impact of such technologies at the regional or national scale. Upscaling via biogeochemical models, such as DayCent, from the field-scale to a larger region can be a suitable and powerful way to assess the potential of such agricultural management practices at scale, but they need to be calibrated to new environments and their reliability needs to be assured. Here, we present a robust calibration of DayCent to simulate maize productivity under ISFM, using data from four long-term field experiments. The experimental treatments consisted of the addition of low- to high-quality organic resources to the soil, with and without mineral N fertilizer. We assess the potential of DayCent to represent the key aspects of sustainable intensification, including 1) yield, 2) changes in soil carbon, and 3) global warming potential. The model was calibrated and cross-evaluated with the probabilistic Bayesian calibration technique. The standard parameters of DayCent led to poor simulations of maize yield (Nash-Sutcliffe modeling efficiency; EF 0.33) and changes in SOC (EF -1.3) for different ISFM treatments. After calibration of the model, both the simulation of maize yield (EF 0.51) and the change in SOC (EF 0.54) improved significantly compared to the model with the standard parameter values. A leave-one-site-out cross-evaluation indicated the robustness of the approach for spatial upscaling (i.e., the significant improvement, described before, was achieved by calibrating with data from 3 sites and evaluating with the remaining site). The SOC decomposition parameters were altered most severely by the calibration. They were an order of magnitude higher compared to the default parameter set. This confirms that the decomposition of SOC in tropical maize cropping systems is much faster than in temperate systems and that the DayCent temperature function is not suitable to capture this with a single parameter set. Finally, the global warming potential simulated by DayCent was highest in control -N treatments (0.5–2.5 kg CO2 equivalent per kg grain yield, depending on the site) and could be reduced by 14 to 72 % by combined application of mineral N and manure at a medium rate. In three of the four sites, the global warming potential was largely (> 75 %) dominated by SOC losses. In summary, our results indicate that DayCent is suitable for estimating the impact of ISFM from the site to the regional level, that trade-offs between yields and global warming potential are stronger in low-fertility sites, and that the reduction of SOC losses is a priority for the sustainable intensification of maize production in Kenya. [ABSTRACT FROM AUTHOR]

The lower common wheat productivity and quality are major constraints in Afghanistan. The objectives of this study were to (1) quantify the effect of soil and climatic parameters on the yield and quality of common wheat and (2) investigate the response of different wheat varieties to different N and P fertilization rates, to improve the yield and quality of common wheat. Three wheat varieties (DLN7, ZRDN, and KBL13), three phosphorus levels (PL) at 60, 90, and 120 kg P2O5 ha−1, and three nitrogen ratios (NP) at 1:1, 1.25:1, and 1.5:1, respectively, in four locations (L), were evaluated. The higher average grain yield (GY), straw yield (SY), and starch yield (STY) were obtained with DLN7, followed by KBL13 and ZRDN, for all locations. As PL increased, GY, SY, protein yield (PY), and STY significantly increased in all locations. The PL significantly affected protein content (PC), gluten content (GC), and dough strength (W). The NP significantly improved PC, GC, and PY. Starch (ST), STY, and amylopectin (AP) increased significantly with increasing PL. The amylose to AP ratio increased significantly with increasing NP ratios. The findings show that at NP1/PL120, GY, SY, ST, and AP improved significantly, while at NP1.5:1/PL120, PC and GC improved significantly. [ABSTRACT FROM AUTHOR]

Yield and rice quality indicators of crops are a direct reflection of the rational irrigation and fertilizer strategy. However, the effects of controlled irrigation (CI) combined with the split application of fertilization managements (straw returning, organic fertilizer, and conventional fertilizer) on rice quality are not clear in southeast China. This study aims at exploring the effects of three fertilization managements applied under CI or flooding irrigation on rice yield, quality, enzyme activity, and soluble sugar content including 43 indicators, to determine the optimal comprehensive evaluation model, management, and representative indexes. The results showed that compared with CF (CI + conventional fertilizer), CS (CI + straw returning) significantly increased yield (27.65%), irrigation water use efficiency (6.20%), chalky grain rate (9.67%), chalkiness (1.83%), protein content (4.29%), and amylose content (0.33%), indicating that CS improved yield and milling quality but decreased cooking and appearance quality. This was mainly because CS promoted the activities of alpha-amylase, ADPG (ADP-glucose pyrophosphorylase), and GBSS (granule-bound starch synthase) and reduced the soluble sugar content in rice. Grey relational degree analysis (GRD), the entropy method (ETM), and TOPSIS (the technique for order preference by similarity to an ideal solution) were used to comprehensively evaluate the rice quality and determined that CS treatments could synergistically improve yield and rice quality. The five indexes (adhesive strength, HPV, ADPG, soluble sugar (leaf), yield) and TOPSIS model can be used as the best indexes and model to evaluate the rice quality. These results could provide scientific management and evaluate practices for high-yield and high-quality rice cultivation, which may be promising for a cleaner production strategy. [ABSTRACT FROM AUTHOR]

China is the largest soybean-consuming country in the world, but its self-sufficiency rate (SSR) of 16% is very low and it therefore has to heavily rely on imports. To solve the soybean dilemma in China, it is necessary to examine the maximum amount of soybean that could be grown on the land currently used, how much land could reasonably be used to expand soybean acreage, and whether China could sustainably increase soybean self-sufficiency to reduce the risks of import reliance. To answer these questions, our paper presents a high-resolution spatial analysis of potential soybean production in China using primary data of weather and crop production practices that govern this potential. We employed a "bottom-up" scaling protocol to estimate gaps between potential yield with optimal management and current yields in three major soybean-planting regions, namely, Northeast China, Central China, and South China. We found that current soybean yield gap (Yg) in China is 49% and 45% of potential yield under irrigated and rainfed cropping systems, respectively. By closing the yield gap, Northeast China could provide additional soybean production equivalent to 32% of the current national total. Our results show that SSR could only be increased to 21–23% in 2030 by Yg closure alone but could be increased to a maximum of 52% by combining Yg closure and a reasonable area expansion. Even so, at least 61.08 million tons of soybean accounting for 38% of global soybean trade would still need to be imported to meet future domestic demand. We discuss strategies for soybean production increase based on Yg closure in the most valuable areas and cropland expansion in a sustainable manner in order to increase SSR as well as lessen the import pressure on the global market. [ABSTRACT FROM AUTHOR]

A persistent paradox in restaurant literature reveals that most studies tend to combine different restaurants in one data sample prior to the estimation of efficiency which blurs efficiency. Therefore, the purpose of this study was to provide further validation on the factors influencing restaurant efficiency using a meta-frontier approach. This study endeavour applied the meta-frontier concept to account for the different environmental and technological factors by ensuring that heterogeneous restaurants are compared based on one homogeneous technology. To test the model, the study used a panel data sample of 84 South African restaurants, which operated from 2015 to 2018. The findings revealed that uncontrollable factors, namely, location, size and restaurant type significantly and positively influence (p<0.05) restaurant efficiency. Empirical evidence supports the view that traditional efficiency techniques tend to underestimate efficiency scores resulting in biased efficiency comparisons between different restaurants. Although there are many other explanatory variables (e.g., restaurant ownership) that influence restaurant efficiency, however, these factors were not taken into account in this analysis due to lack of availability of data associated with those variables. The findings could enhance the service data and revenue management in the restaurant industry. [ABSTRACT FROM AUTHOR]

Revenue management (RM) on chain hotels versus independent hotels intends to create value for the activity, manage revenue, and control costs for profit efficiency. The article presents a simplified analysis of its management that intends to benefit suppliers, hotels, and hotels' level of service and how it is being seen nowadays. There was drawn a research question in this study: to understand how it is perceived the impacts of RM on supply chain management (SCM)? This essay aims to bring a two-ways input (from and to), hotel managers, hotel professional revenue managers, and supply chain managers. Due to the pandemic, all data for forecasting and analysis have been reconsidered because they cannot be related to further implications caused by the market changes and guests' behaviors, making this research relevant at this period. [ABSTRACT FROM AUTHOR]

The success of revenue management models depends to a large extent on the quality of historical data used to forecast future bookings. Several theoretical models and best practices of handing historical data have been developed over the years, that all rely on assumptions about underlying distribution and seasonality in the historical data. In this paper, we describe a novel method that compares the fingerprints of the departure to optimise and selects historical departures without making assumptions on data distribution or seasonality. By evaluating the method at the departure level and using the Nemenyi rank test, we show the method's application in the ferry transportation business and discuss its advantages. [ABSTRACT FROM AUTHOR]

We study a classic problem in revenue management: quantity-based, single-resource revenue management with no-shows. In this problem, a firm observes a sequence of T customers requesting a service. Each arrival is drawn independently from a known distribution of k different types, and the firm needs to decide irrevocably whether to accept or reject requests in an online fashion. The firm has a capacity of resources B and wants to maximize its profit. Each accepted service request yields a type-dependent revenue and has a type-dependent probability of requiring a resource once all arrivals have occurred (or be a no-show). If the number of accepted arrivals that require a resource at the end of the horizon is greater than B, the firm needs to pay a fixed compensation for each service request that it cannot fulfill. With a clairvoyant that knows all arrivals ahead of time, as a benchmark, we provide an algorithm with a uniform additive loss bound, that is, its expected loss is independent of T. This improves upon prior works achieving Ω (T) guarantees. Funding: The authors gratefully acknowledge generous support by the Sloan School of Management at MIT. Supplemental Material: The online appendix is available at https://doi.org/10.1287/moor.2022.1293. [ABSTRACT FROM AUTHOR]

We study the design of a decentralized two-sided matching market in which agents' search is guided by the platform. There are finitely many agent types, each with (potentially random) preferences drawn from known type-specific distributions. Equipped with knowledge of these distributions, the platform guides the search process by determining the meeting rate between each pair of types from the two sides. Focusing on symmetric pairwise preferences in a continuum model, we first characterize the unique stationary equilibrium that arises given a feasible set of meeting rates. We then introduce the platform's optimal directed search problem, which involves optimizing meeting rates to maximize equilibrium social welfare. We first show that incentive issues arising from congestion and cannibalization make the design problem fairly intricate. Nonetheless, we develop an efficiently computable search design whose corresponding equilibrium achieves at least 1 / 4 the social welfare of the optimal design. In fact, our construction always recovers at least 1 / 4 the first-best social welfare, where agents' incentives are disregarded. Our search design is simple and easy to implement, as its corresponding bipartite graph consists of disjoint stars. Furthermore, our design implies the platform can substantially limit choice and yet induce an equilibrium with approximately optimal welfare. We offer alternative search designs with improved approximation factors for markets with certain special structures. Finally, we show that approximation is likely the best one can hope for by establishing that the problem of designing optimal directed search is NP -hard to even approximate beyond a certain constant factor. This paper was accepted by Omar Besbes, revenue management and market analytics. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2022.4601. [ABSTRACT FROM AUTHOR]

Although variable pricing enables sports teams to respond to changing demand across games, reports point to somewhat limited implementation. In this paper, we study the implications of a switch to variable pricing on the primary and resale ticket markets using quasi-experimental data from the National Football League. Applying a difference-in-differences technique with propensity score weighting, we first show that the adoption of variable pricing increases primary market ticket sales by 1.59% per game. We then explore the mechanism behind this positive impact. We find that lower prices for the less attractive games are well received by customers, and customers do not have an unfavorable response to higher prices for the more attractive games. Our analysis of the customers' activities in the resale market shows that the minimum resale market prices increase for the more attractive games following the switch to variable pricing. This analysis provides evidence for the increase in the option value created by the resale market and explains the positive response to higher prices for the more attractive games. Finally, we investigate when variable pricing is likely to be the most effective. We find that variable pricing has greater success in team hometowns with lower income levels and higher income diversity, and creating more game tiers in variable pricing leads to higher sales. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4588. [ABSTRACT FROM AUTHOR]

The increasing use of nanofertilizers in modern agriculture and their impact on crop yield and pest management require further research. In this study, the effects of nano-Fe, -Zn, and -Cu (which are synthesized based on nanochelating technology), and urea (N) fertilizers on the antioxidant activities of wheat plants (cv. Chamran), and the wheat green aphid Schizaphis graminum (Rondani) are investigated. The authors observed the highest levels of phenolics in non-infested nano-Zn-treated plants (26% higher compared with control). The highest H2O2 levels are in the infested and non-infested nano-Zn-treated and infested nano-Fe-treated plants (in infested nano-Zn and nano-Fe treated plants, 18% and non-infested nano-Zn-treated plants, 28% higher compared with control). The highest peroxidase (POX) activity is observed in the infested and non-infested N-treated and non-infested water-treated plants (almost 14%, 37%, and 46% higher than control, respectively). The lowest activity is in the infested plants' nano-Zn and -Fe treatments (almost 7 and 5 folds lower compared to the control, respectively). The highest and lowest catalase (CAT) activity are in the infested N-treated plants (almost 42% higher than control) and water-treated plants, respectively. The infested nano-Zn, -Fe, -Cu and Hoagland-treated plants showed the highest superoxide dismutase (SOD) activity. Regarding the antioxidant enzyme activities of S. graminum, the highest POX activity is in the nano-Cu treatment (more than two folds higher compared with control); the highest CAT and SOD activities are in the nano-Cu and -Zn treatments. It can be concluded that the application of nanofertilizers caused increasing effects on the wheat plant's antioxidant system and its resistance to S. graminum. [ABSTRACT FROM AUTHOR]

The global production of plant-based foods is a significant contributor to greenhouse gas emissions. Indoor vertical farms (IVFs) have emerged as a promising approach to urban agriculture. However, their environmental performance is not well understood, particularly in relation to operational choices where global warming potentials (GWP) can vary between 0.01–54 kg CO2e/kg−1 of leafy greens produced. We conducted a life cycle assessment (LCA) of a building-integrated IVF for microgreen production to analyse a range of operational conditions for cultivation: air temperature, CO2 concentration, and photoperiod. We analyzed a dynamic LCA inventory that combined a process-based plant growth model and a mass balance model for air and heat exchange between the chamber and the outside. Results showed that the GWP of IVFs can vary greatly depending on the operation conditions set, ranging from 3.3 to 63.3 kg CO2e/kg−1. The optimal conditions for minimizing GWP were identified as 20 ℃, maximum CO2 concentration in the chamber, and maximum photoperiod, which led to a minimum GWP of 3.3 kg CO2e/kg−1 and maximum production of 290.5 kg fresh weight week-1. Intensification of production thus led to lower impacts because the marginal increase in yield due to increased resource use was larger than the marginal increase in impact. Therefore, adjusting growing conditions is essential for the sustainability of urban food production. [ABSTRACT FROM AUTHOR]

With the rapid development of O2O, offline experience and online purchase have become a method of purchase for more and more customers. Through offline experience, consumers can feel the quality of products directly. Such channel switching behavior of consumers will produce a "showroom" effect and affect the return rate of online channels. This study adopts the multinomial logit model to maximize profits by considering the difference in quality between online and offline products, quality defects, and offline service. Then, a pricing decision model is developed to analyze the influence of returning goods due to quality problems on the retailers' optimal pricing and profit. The result shows that retailers can obtain the optimal profit when the offline service is maintained at a certain level. As the return rate increases, the optimal pricing increases, but the maximum profit decreases. The optimal pricing decreases with the increase in online product quality, but the maximum profit increases accordingly. In the omni-channel environment, customers can freely switch between channels according to utility and preference when purchasing products. Based on customer returns, retailers can dynamically adjust their service, control product quality, and set optimal product pricing, thus achieving maximum profits. This study can provide a theoretical basis and decision support for omni-channel retailers in platform operation and revenue management. [ABSTRACT FROM AUTHOR]

Supplemental irrigation under a ridge-furrow (RF) cropping system is a valuable cropping practice that balances resource efficiency and high crop yield. However, the effects of nitrogen management on crop growth, yield formation, and economic benefits under RF systems have not been clearly investigated. In this study, the experiment was designed with three experimental factorials, including three cropping systems (RF, RF cropping with 80 mm irrigation; TF1, traditional flat cropping with 200 mm irrigation; and TF2, traditional flat cropping with 80 mm irrigation), two nitrogen application rates (NL, 180 kg N ha−1; NH, 240 kg N ha−1), and two fertilizer application models (B, all nitrogen fertilizers were applied basally at the pre-sowing stage; BT, nitrogen fertilizer was applied at both the pre-sowing and jointing stages at a ratio of 1:1). A two-year field experiment was conducted to investigate the effects of nitrogen fertilizer management on wheat yield, water and nitrogen utilization, and economic benefits under the RF cropping system. The results showed that the RF system significantly increased the soil moisture content and improved the water productivity (WP) and grain yield of wheat. Nitrogen reduction (NL) under the RF system did not affect the water use of the wheat compared with traditional high nitrogen application (NH) but increased the nitrogen uptake and fertilizer productivity of the wheat. Although NL led to a reduction in aboveground dry matter accumulation, it did not significantly affect the yield of wheat but increased the net income of wheat cultivation. Under NL conditions, the BT nitrogen application model promoted nitrogen uptake in wheat and ameliorated the reduction in grain protein content due to plastic film mulching, and this model is an integrated planting practice that trades off wheat yield and quality. These findings suggest that NLBT is a promising and recommendable cropping practice under RF systems considering resource utilization, high yield and quality, and economic efficiency. [ABSTRACT FROM AUTHOR]

The experiment was conducted during 2019 and 2020 (March to August) at Regional Research Station, Bidhan Chandra Krishi Viswavidyalaya, Jhargram, West Bengal, India to find out the response of phosphorus management on growth and yield of groundnut varieties and its residual effect on succeeding baby corn. The three main plot treatments (variety- TAG 24, TG 51, TG 37A) and six subplot treatments of phosphorus dose were laid out in split plot design with three replications. Highest plant height (70.3 cm), number of branches plant-1 (8.5), crop growth rate (3.67 g m-2 day-1), pod yield (2007 kg ha-1), haulm yield (2876.22 kg ha-1), kernel yield (1360.76 kg ha-1) and oil yield (689.36 kg ha-1) were obtained with groundnut variety TG 51. Whereas phosphorus dose (100% RDF+PSB @ 25 g kg-1 of seed+FYM @ 2 t ha-1) provided highest value for the above characters. Interaction effect was significant for pod yield and kernel yield. The residual effect on succeeding baby corn was found maximum for variety TAG 24 and treatment (100% RDF+PSB @ 25 g kg-1 of seed+FYM @ 2 t ha-1) applied to previous groundnut. So, application of phosphorus @ 60 kg ha-1 along with P.S.B @ 25 g kg-1 of seed and F.Y.M @ 2 t ha-1 may be successful for groundnut variety TG 51 alone and TAG 24 in sequence with baby corn. [ABSTRACT FROM AUTHOR]

BACKGROUND: Direct‐seeded rice has been developed rapidly because of labor savings. Changes in rice cultivation methods put forward new requirements for nitrogen (N) fertilizer management practices. Field experiments with five different fertilizer ratios of basal, tillering and panicle fertilizer, namely N1 (10:0:0), N2 (6:2:2), N3 (4:3:3), N4 (2:4:4) and N5 (0:5:5), were conducted to investigate the effects of different N fertilizer management practices on yield formation, N uptakes, and ammonia (NH3) volatilization from paddy fields in direct‐seeded rice. RESULTS: The results showed that the N4 treatment improved grain yield by 5.1% while decreasing NH3 volatilization by 20.4% compared with that of conventional fertilizer treatment (N2). The panicle number per unit area was the key factor to determine the yield of direct‐seeded rice (72%). Excessive N application of basal fertilizer (N1) reduced seedling emergence, N use efficiency, and yield by 45.3%, 160.6%, and 6.9% respectively and increased NH3 volatilization by 28.1% compared with that of the N4 treatment. Removal of basal N fertilizer (N5) N reduced spike number and yield by 13.0% and 6.9% respectively, minimizing NH3 volatilization while affecting the construction of high‐yielding populations compared with that of the N4 treatment. CONCLUSION: Optimized N fertilizer management achieved delayed senescence (maintenance of higher leaf Soil Plant Analysis Development meter values in late reproduction), higher canopy photoassimilation (suitable leaf area), higher N fertilizer use efficiency, and less N loss (lower cumulative NH3 volatilization). © 2023 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

This study was aimed at examining the effect of tax revenue management on growth dynamics in the Nigerian economy, with specific emphasis on the contributions of Petroleum Profit Tax, (PPT), Company income Tax(CIT), Value Added Tax, (VAT) and Custom and Excise Duty(CED). Using secondary data spanning 1997 -2020, compiled from the World Bank and the Organization for Economic Co-operation and Development (OECD). Time-series data and the econometric model was subjected to a number of diagnostic tests, including the unit root test (using Augmented Dickey-Fuller (ADF) tests and the co-integration test In order to examine different tax productivity strategies, panel regression and error correction modeling were equally employed. The study revealed that PPT, CIT, VAT and CED have a positive significant effects on growth dynamics index in Nigeria, it is therefore recommended that the for the current tax reforms embarked on by the Federal Government to yield its intended results, it should be vigorously pursued and faithfully implemented by the relevant agencies to increase the tax net and block revenue leakages which invariably increases the disposable income of government for developmental projects. [ABSTRACT FROM AUTHOR]