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Through a meta-analysis, Mupepele et al. (BMC Ecol Evol 21:1–193, 2021) assessed the effects of European agroforestry systems on biodiversity, estimated by species richness or species diversity. They showed that the effects of silvoarable and silvopastoral systems depend on the systems they are compared to and the taxa studied. Further, they found that only silvoarable systems increased species richness or diversity, compared to cropland. The authors conclude that agroforestry systems have weak effects on biodiversity and that landscape context or land-use history are probably more important than the practice of agroforestry in itself. However, we draw attention to important shortcomings in this meta-analysis, which downplay the potential of agroforestry for biodiversity conservation in agricultural landscapes. We hope that the meta-analysis by Mupepele et al. (BMC Ecol Evol 21:1–193, 2021), and our comments, will contribute to improving the quality of research on agroforestry systems and biodiversity conservation.

In modern-day manufacturing Electric Discharge Machining (EDM) process has successfully placed itself in the domain of precision machining and generating complex geometries where secondary machining processes are eliminated. In this research paper, a die sinking EDM is applied to machine mild steel in order to measure the different multi-objective results like Material Removal Rate (MRR) and Over Cut (OC). This contradictory objective is accomplished by using the control parameters like a pulse on time, duty factor, gap current and spark gap employing copper tool with lateral flushing. Here the individual objective function of the responses is created through regression analysis. Primarily the contradictory objectives are optimized by employing Taguchi Methodology, then Regression analysis is done on the test results. Additionally, the experimental results are optimized using Response Surface Methodology (RSM). It is followed by a multi-objective optimization through Overlaid contour plots and Desirability functions to ascertain the best parametric combination amongst the set of feasible alternatives.

Fault diagnosis is crucial for realizing safe process operation when a fault occurs. Multivariate statistical process control (MSPC) has widely been adopted for fault detection in real processes, and contribution plots based on MSPC are a well-known fault diagnosis method, but it does not always correctly diagnose the causes of faults. This study proposes a new fault diagnosis method based on the causality between process variables and a monitored index for fault detection, which is referred to as a causal plot. The proposed causal plot utilizes a linear non-Gaussian acyclic model (LiNGAM), which is a data-driven causal inference algorithm. LiNGAM estimates a causal structure only from data. In the proposed causal plot, the causality of a monitored index of fault detection methods, in addition to process variables, is estimated with LiNGAM when a fault is detected with the monitored index. The process variables having significant causal relationships with the monitored indexes are identified as causes of faults. In this study, the proposed causal plot was applied to fault diagnosis problems of a vinyl acetate monomer (VAM) manufacturing process. The application results showed that the proposed causal plot diagnosed appropriate causes of faults even when conventional contribution plots could not do the same. In addition, we discuss the effects of the presence of a recycle flow on fault diagnosis results based on the analysis result of the VAM process. The proposed causal plot contributes to realizing safe and efficient process operations.

In the current age, the increased need for the restoration of forest ecosystems necessitates a better understanding of natural processes. Forest stands that are affected only by natural processes and disturbances can serve as references and controls for comparison with cut or otherwise managed forests. Such a comparison may help us determine, whether our sylvicultural practices actually pursue the goal of sustainable development. It is also important to use uniform terminology across the world to facilitate sharing of experiences and results. Creating reference and control stands in every ecoregion will provide a rich scientific basis for comparison with managed forests and allow us to design and apply restoration methods more effectively.

As urbanization accelerates, megacities are facing challenges such as inefficient land use and traffic congestion, particularly in the context of rail transit-oriented development, where land use optimization remains a significant research gap. Current urban planning still relies heavily on the experience and intuition of government planning departments, without achieving quantitative, intelligent, and scientific decision making. This study takes Panda Avenue Subway Station as a case study to analyze the evolution of land use patterns around subway stations and explore optimization strategies to enhance land development efficiency and spatial utilizationTo fill this research gap, this paper proposes a CNN-AIMatch model based on machine learning algorithm and an enhanced PLUS-Markov prediction model using the increase and decrease of floor area ratio as a control measure, which adopts an increase in plot ratio as a control measure to improve the accuracy of the Kappa coefficient in different plot ratio scenarios and the prediction of 3D urban spatial growth trends. The model effectively overcomes the limitations of the conventional 2D perspective in predicting urban expansion. By simulating urban renewal and ecological preservation scenarios, it provides an innovative solution for land use pattern optimization and plot ratio control at the block level in subway station areas. The goal of this study is to optimize land use and floor area ratio control strategies through the application of this model, intelligently respond to the challenges of high-density development and quality of life assurance, achieve the best use of land, and promote sustainable urban development and the construction of smart cities.

Early generation selection experiments typically involve several hundred to thousands of lines. Various systematic and statistical techniques have been developed to increase effectiveness and efficiencies in such experiments, including the development and application of spatial statistical models. In this study, mixed model equations were used to provide least squares means (LSMEANs) and best linear unbiased predictors (BLUPs) and compare selection effectiveness and efficiencies to observed (Y) and true values in simulated experiments varying in size (10 x 10, 20 x 20 and 30 x 30 grids), control plots densities (0, 5, 10, 20, and 50%), control plot arrangements (high, medium, and low A-optimality), and spatial range of influence (short and long). Results were similar for all grid sizes. In experiments in which the simulated land areas were highly variable (short range), none of the predictors, Y, LSMEAN, or BLUP, were very effective in identifying the true superior genotypes. When the simulated land areas were less variable (long range), use of BLUPs consistently resulted in the highest proportion of true top ranking genotypes identified across all control plot densities, while using the observed values consistently resulted in identification of the lowest proportion of the true top ranking genotypes. Effectiveness of LSMEANs was dependent on control plot density and arrangements. Use of BLUPs for early generation germplasm screening experiments should result in a high effectiveness in selecting truly superior germplasm and high efficiency because of the ability to account for spatial variability with the use of few or no control plots.

Abstract Purpose This chapter examines how Maragoli women farmers’ plot-level crop control, individual, and household variables affect yields. This chapter contributes to a holistic understanding of the ramifications of quantitative and qualitative factors informing women farmers’ plot-level undertakings and yields as well as their innovative and creative strategies for optimizing output. It broadens the existing debate in the sub-Saharan African agricultural production literature by suggesting a composite measure of plot-level crop control as one factor influencing women farmers’ yields even in situations where land is owned by someone else. It also provides a rich discussion of the various and interlocking qualitative factors distorting women farmers’ incentive structures, efforts to increase plot-level yields and their strategies for minimizing the detrimental effects of the same. Methodology/approach A multimethod quantitative and qualitative ethnographic case study approach was used in this study. Findings This chapter demonstrates that women strategically bargained and invested more of their productive resources on the plots where they anticipated the greatest individual gains. Practical implications This chapter underscores women farmers’ ability to boost agricultural output when there are appropriate incentives for them to do so and suggests the theoretical and practical relevance of secure control and property rights over the products of the land not for the household (head), but for the cultivator. The chapter demonstrates and reaffirms that Africa women farmers respond appropriately to incentives and suggests that there is need for a customized, renewed, and sustained emphasis on women farmers’ empowerment and inclusion in all levels in the agricultural sector in order to actualize increased yields. Investing in women farmers and implementing policies that narrow existing gender disparities in African agricultural production systems is holistically beneficial.

This chapter contains sections titled: Histogram Pareto Charts Scatter Plots Control Chart Check Sheet Cause‐and‐Effect Diagram Defect Concentration Diagram The Seven Newer Tools Software Summary References Exercises

This chapter contains sections titled: Bibliography