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Life in soil is a key driver of important ecosystem processes, such as the recycling of carbon and nutrients. In current intensive agricultural soils, however, richness and abundance of many groups of soil organisms are often reduced, which may threaten soil health and sustainable agriculture in the long run. Therefore, a switch to alternative agricultural practices (e.g., minimal tillage) that are less detrimental or even stimulate soil life has been suggested as a way to increase sustainable food production. Although we understand how some of these practices impact specific species or functional groups in soils, it is necessary to get a more complete overview to understand which practices can be used in agriculture to improve soil biodiversity. Here, we present a systematic literature review identifying which practices are studied as alternatives to current, intensive practices for four soil taxonomic groups encompassing a range of trophic groups and functions in the soil ecosystem: nematodes, earthworms, bacteria and fungi. Further, we review how these alternative practices impact the abundance and diversity of these four taxonomic groups, as well as for the 14 functional groups identified and retrieved from the review. We found that a total of 23 alternative agricultural practices, grouped into 10 groups of practices, were studied for the four target taxonomic groups. Three groups of practices, 'fertilization’, ‘soil cover’ and ‘tillage’ were studied for all taxa. In general, alternative agricultural practices had positive impacts on the species richness in the four taxonomic groups and on the abundance of organisms in the functional groups. However, there were some exceptions. For example, organic fertilizers reduced the abundance of epigeic earthworms, while enhancing the abundance of endogeic and anecic earthworms. There was only one alternative practice, i.e., the use of cover crops, that was neutral to positive for the abundance of all functional groups across all taxa. Our review revealed that there are gaps in the literature, as practices that are commonly studied for aboveground biodiversity, such as field margins or flower strips, are not studied well across taxonomic and functional groups and need to be further studied to improve our understanding of the impact of alternative practices on soil life. We conclude that alternative agricultural practices are promising to enhance soil biodiversity. However, as some practices have specific impacts on taxonomic groups in the soil, we may require careful application and combinations of alternative agricultural practices to stimulate multiple groups.

We present a modeling and optimization framework to design powertrains for a family of electric vehicles, focusing on the concurrent sizing of their motors and batteries. Whilst tailoring these component modules to each individual vehicle type can minimize energy consumption, it can result in high production costs due to the variety of component modules to be realized for the family of vehicles, driving the Total Costs of Ownership (TCO) high. Against this backdrop, we explore modularity and standardization strategies whereby we jointly design unique motor and battery modules to be installed in all the vehicles in the family, using a different number of these modules when needed. Such an approach results in higher production volumes of the same component module, entailing significantly lower manufacturing costs due to Economy-of-Scale (EoS) effects, and hence a potentially lower TCO for the family of vehicles. To solve the resulting one-size-fits-all problem, we instantiate a nested framework consisting of an inner convex optimization routine which jointly optimizes the modules' sizes and the powertrain operation of the entire family, for given driving cycles and modules' multiplicities. Likewise, we devise an outer loop comparing each configuration to identify the minimum-TCO solution with global optimality guarantees. Finally, we showcase our framework on a case study for the Tesla vehicle family in a benchmark design problem, considering the Model S, Model 3, Model X, and Model Y. Our results show that, compared to an individually tailored design, the application of our concurrent design optimization framework achieves a significant reduction of the production costs for a minimal increase in operational costs, ultimately lowering the family TCO in the benchmark design problem by 3.5\%. 17 pages, 17 figures, 7 tables

CONTEXT: Serious games can be used as a tool for learning, increasing coordination, supporting decision-making processes, and other purposes that can strengthen sustainability transitions. While agriculture is an important corner stone for these transitions, little research has been done on serious games on agricultural and none on the potential link with sustainability issues. OBJECTIVE: This article is a systematic review of published research articles on the use of serious games to address agricultural issues. It aimed to understand how these serious games incorporate or are likely to address sustainability issues. METHODS: The process of the review is described accord to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). After data collection, we conducted a four-step analysis: i) short bibliometric analysis of the corpus, ii) descriptive analysis of the games' characteristics, iii) comprehensive analysis on sustainability based on a framework developed to define what is sustainable agriculture, iv) analysis of assessment of the games. RESULTS AND CONCLUSIONS: Results were based on 237 articles including 182 empirical studies. We showed that the number of articles on serious games in agriculture have recently increased throughout the world. Serious games can reach different goals: i) learning, particularly on specific topics, ii) mediation and co-design, iii) research. Games can be seen as effective means to enable stakeholders to work together. In a context of sustainability transitions, serious games can be used to tackle complex issues. However, more effort must be undertaken to assess the real impact of the game. SIGNIFICANCE: This review confirmed the importance of serious games in agricultural research aiming to enhance sustainability transition. We identified gaps and proposed a research agenda to further work on i) inclusion of the diversity of games, ii) rethinking using of games with possible combinations, iii) opening to broader agricultural productions, iv) assessing the real impact of the games, v) using games for transdisciplinary research.

The required data was provided by Energex. The study received partial funding from the Ministry of Science and Innovation, Spain (Project ID: PID2020-115454GB-C21). Partial support of this work was through the LATENTIA project PID2022-140786NB-C31 of the Spanish Ministry of Science, Innovation and Universities (MICINNU) . This work presents a Temporal Convolution Network (TCN) model for half-hourly, three-hourly and daily-time step to predict electricity demand ( ) with associated uncertainties for sites in Southeast Queensland Australia. In addition to multi-step predictions, the TCN model is applied for probabilistic predictions of where the aleatoric and epistemic uncertainties are quantified using maximum likelihood and Monte Carlo Dropout methodologies. The benchmarks of TCN model include an attention-based, bi-directional, gated recurrent unit, seq2seq, encoder–decoder, recurrent neural networks and natural gradient boosting models. The testing results show that the proposed TCN model attains the lowest relative root mean square error of 5.336-7.547% compared with significantly larger errors for all benchmark models. In respect to the 95% confidence interval using the Diebold–Mariano test statistic and key performance metrics, the proposed TCN model is better than benchmark models, capturing a lower value of total uncertainty, as well as the aleatoric and epistemic uncertainty. The root mean square error and total uncertainty registered for all of the forecast horizons shows that the benchmark models registered relatively larger errors arising from the epistemic uncertainty in predicted electricity demand. The results obtained for TCN, measured by the quality of prediction intervals representing an interval with upper and lower bound errors, registered a greater reliability factor as this model was likely to produce prediction interval that were higher than benchmark models at all prediction intervals. These results demonstrate the effectiveness of TCN approach in electricity demand modelling, and therefore advocates its usefulness in now-casting and forecasting systems.

A new energy-consistent discretization of the viscous dissipation function in incompressible flows is proposed. It is implied by choosing a discretization of the diffusive terms and a discretization of the local kinetic energy equation and by requiring that continuous identities like the product rule are mimicked discretely. The proposed viscous dissipation function has a quadratic, strictly dissipative form, for both simplified (constant viscosity) stress tensors and general stress tensors. The proposed expression is not only useful in evaluating energy budgets in turbulent flows, but also in natural convection flows, where it appears in the internal energy equation and is responsible for viscous heating. The viscous dissipation function is such that a consistent total energy balance is obtained: the 'implied' presence as sink in the kinetic energy equation is exactly balanced by explicitly adding it as source term in the internal energy equation. Numerical experiments of Rayleigh-Bénard convection (RBC) and Rayleigh-Taylor instabilities confirm that with the proposed dissipation function, the energy exchange between kinetic and internal energy is exactly preserved. The experiments show furthermore that viscous dissipation does not affect the critical Rayleigh number at which instabilities form, but it does significantly impact the development of instabilities once they occur. Consequently, the value of the Nusselt number on the cold plate becomes larger than on the hot plate, with the difference increasing with increasing Gebhart number. Finally, 3D simulations of turbulent RBC show that energy balances are exactly satisfied even for very coarse grids; therefore, we consider that the proposed discretization forms an excellent starting point for testing sub-grid scale models.

# databases used for statistical analyses in manuscript WLB-2024-01345 [https://doi.org/10.5061/dryad.tht76hf7v](https://doi.org/10.5061/dryad.tht76hf7v) ## Description of the data and file structure **List of excel files used for GLMMs and other analyses in manuscript WLB-2024-01345.R1 – “Precipitation and female experience are major determinants of the breeding performance of Canarian houbara bustards”** ### Files and variables #### File: GLM2b2NestInitiatDate.xlsx **Description:** ** database for GLMM Nest Initiation Date (NIDF, see Supplementary Table S3)** ##### Variables * definitions as in other excel files #### File: GLM4aNestAttemptSuccess.xlsx **Description:** **database for GLMM Nest Attempt Success (see Supplementary Table S3)** ##### Variables * clutchOrder: order of the clutch (1 first, 2 second –replacement-, 3 third –replacement-clutch), chicksSurvived: chicks survived until productivity control (1= yes/0= no, see Methods), MeanTemp: during 23 days incubation + 2 months in nestings that have chicks on the control date, AvMaxTemp: average maximum temperature during 23 days incubation + 2 months in nestings that have chicks on the control date, AvMinTemp: average minimum temperature during 23 days incubation + 2 months in nestings that have chicks on the control date, pp: precipitation during 23 days incubation + 2 months in nestings that have chicks on the control date, other variables as defined in other excel files #### File: GLM4bFledSuccess.xlsx **Description:** **database for GLMM Fledging Success (see Supplementary Table S3)** ##### Variables * Variables:** **pp: precipitation during** **23 days since nesting start + 2 months in nestings that have chicks on the control date; 23+1 month, in nestings that do not have chicks on the control date, other variables as defined in other excel files #### File: GLM5ReClutchProb.xlsx **Description:** **database for GLMM Re-clutching Probability (see Supplementary Table S3)** ##### Variables * Variables: Reclutch: 1= has a replacement clutch/0= does not have a replacement clutch, DurationIncubation: duration of the incubation period (days), MeanTemp, AvMaxTemp, AvMinTemp, pp: measured over the incubation period, other variables as defined in other excel files #### File: Weighted\_precipitations.xlsx **Description:** **databases to calculate weighted precipitation amounts, periods of precipitation and nestings (see Methods for details)** ##### Variables * definitions as in other excel files #### File: GLM6a3FemaleProductivity.xlsx **Description:** **database for GLMM Productivity (see Supplementary Table S3)** ##### Variables * nClutches: number of clutches (1,2,3), NchicksSurvived (1,2 up to fledging), pp2: precipitation measured from one month before the first laying to the laying date of the last clutch, other variables as defined in other excel files #### File: GLM6bFemaleProductivity.xlsx **Description:** **database for GLMM Productivity (as GLM6a, but measuring precipitation over the same period for all years: from 1 September to 13 March [mean hatching start date of the latest year, which was 2022]; see Supplementary Table S3)** ##### Variables * as in GLM6a3FemaleProductivity.xlsx, but measuring precipitation over the same period for all years: from 1 September to 13 March [mean hatching start date of the latest year, which was 2022; other variables as defined in other excel files #### File: GLM7aLengthBreedSeason.xlsx **Description:** **database for GLMM Length of the Breeding Season (see Supplementary Table S3)** ##### Variables * daysBreeding: duration of the breeding season in days (see definition in Methods), temperature and precipitation (PP) measured from 1 month before the first day of incubation of that year in any female until the date of independence of the last chick (see Azar et al 2018: Total rainfall during the nesting period (the period between the first and last nest found each year). other variables as defined in other excel files #### File: GLM7bLengthBreedSeason.xlsx **Description:** **database for GLMM Length of the Breeding Season, same as GLM7aLengthBreedSeason.xlsx, but precipitation and temperature measured over an equal period for all years: from 1 September to 13 March (= average hatching starting date of the latest year, 2022) (see Supplementary Table S3)** ##### Variables * as in GLM7aLengthBreedSeason.xlsx, but precipitation and temperature measured over an equal period for all years: from 1 September to 13 March #### File: WeightedPrecipitationPeriods.xlsx **Description:** **database to calculate weighted precipitation periods and nestings (see Methods for details)** ##### Variables * as in other excel files #### File: GLM2cNestInitiatDate.xlsx **Description:** **database for GLMM Nest Initiation Date of First Clutches (NIDF2, see Supplementary Table S3)** ##### Variables * definitions as in other excel files #### File: GLM1bNestingRate.xlsx **Description:** **database for GLMM Nesting Rate (see Supplementary Table S3)** ##### Variables * indiv= individual female, year, femaleNests: 1=Yes/0=No, startNest = date when nesting started, pp30days: precipitation on the 30 days before (in mm), pp60ays: precipitation on the 60 days before (in mm), pp90days: precipitation on the 90 days before (in mm), TempMean30days: mean temperature on the 30 days before (in oC), TempMax30days: maximum temperature on the 30 days before (in oC), TempMin30days: minimum temperature on the 30 days before (in oC), TempMean60days: mean temperature on the 60 days before (in oC), TempMax60days: maximum temperature on the 60 days before (in oC), TempMean60days: mean temperature on the 60 days before (in oC), Weight: weight of the female (g), PC1p: Principal Component 1 of the PCA including weight, PC1sinP: Principal Component 1 of the PCA excluding weight, Breedingexperience: breeding experience of the female, as defined in Methods. #### File: GLM1cNestingRate2.xlsx **Description:** ##### Variables * indiv= individual female, year, femaleNests: 1=Yes/0=No, startNest = date when nesting started, pp30days: precipitation on the 30 days before (in mm), pp60ays: precipitation on the 60 days before (in mm), pp90days: precipitation on the 90 days before (in mm), TempMean30days: mean temperature on the 30 days before (in oC), TempMax30days: maximum temperature on the 30 days before (in oC), TempMin30days: minimum temperature on the 30 days before (in oC), TempMean60days: mean temperature on the 60 days before (in oC), TempMax60days: maximum temperature on the 60 days before (in oC), TempMean60days: mean temperature on the 60 days before (in oC), Weight: weight of the female (g), PC1p: Principal Component 1 of the PCA including weight, PC1sinP: Principal Component 1 of the PCA excluding weight, Breedingexperience: breeding experience of the female, as defined in Methods, pp_1sep_13mar: precipitation measured between 1st September and 13th March, T_1sep_13mar: temperature measured between 1st September and 13th March #### File: GLM2a2NestInitiatDate.xlsx **Description:** **database for GLMM Nest Initiation Date (NIDF, see Supplementary Table S3)** ##### Variables * ordinalDate: Ordinal date as defined in Methods, rest of variables: definitions as in other excel files #### File: GLM3HatchSuccess.xlsx **Description:** **database for GLMM Hatching Success (see Supplementary Table S3)** ##### Variables * : endNest: date when incubation finished, ppIncub: precipitation during incubation (23 days since start incubation), AvMaxTempIncub: average maximum temperature during incubation, AvMaxTempIncub: average maximum temperature during incubation, ppIncub: mean temperature during incubation, hatchSuccess: 1= incubation until hatching date is successful/ 0= incubation until hatching date is not successful, rest of variables: definitions as in other excel files ## Code/software data can be viewed using EXCEL; other files from the process of statistical analysis were obtained using package “lme4” (Bates et al. 2015) in R v.2.15.1 (R Development Core Team, 2015) Precipitation is one of the main triggers of reproduction in desert-breeding birds. The unpredictability of rainfall patterns in arid environments has led species to adapt their breeding effort to episodes of abundant food after rainfall. The response is not the same for all individuals in a population, and may vary especially with the age and experience of each female. Here we investigate the effects of precipitation, temperature, body size and breeding experience, among other variables, on reproductive parameters of 20 females of Canarian houbara bustard (Chlamydotis undulata fuertaventurae), an endangered desert bird endemic of the eastern Canary Islands. Precipitation and breeding experience were the main determinants of female breeding performance. Higher rainfall determined an increase in nesting rate, and earlier autumn rains caused an advancement of nesting to October, allowing the breeding season to be extended to eight months. This favoured an extraordinary increase in productivity in more rainy breeding seasons, with 15 times more females nesting in the two most rainy winters than in dry years. In addition, females with more breeding experience showed a higher tendency to breed, higher nest attempt and fledging success, and longer breeding season, which allowed them to rear more chicks. A female even double brooded successfully in the same season, which, considering that chicks remain with the mother for up to six months, indicates a great capacity to optimise reproductive investment, by adapting to highly variable rainfall regimes. In recent decades, the eastern Canary Islands have undergone a process of aridification, and climate models predict a medium-term increase in the frequency and duration of drought periods. Thus, Canarian houbaras are particularly vulnerable to climate change, so measures are urgently needed to reduce their mortality and improve the quality of their habitat, in order to favour their reproduction and prevent their extinction.  We used 5-year breeding phenology and breeding success data from 20 female houbara bustards captured in Lanzarote and equipped with backpack-mounted GSM/GPRS data loggers. The influence of predictor variables on breeding parameters was modeled by means of generalized linear mixed models (GLMMs) using package “lme4” (Bates et al. 2015) in R v.2.15.1 (R Development Core Team, 2015).

More frequently occurring droughts, related to climate change, lead to reduced growth and loss of vitality in trees. The recent drought of 2018 was extreme, long-lasting and resulted in high evaporative demands due to the concurrent high temperatures. The aim of this study was to compare the drought resilience of nine temperate tree species in the Netherlands, and to determine their responses to the severe drought of 2018 in comparison with five earlier drought events since 1970. To assess drought effects on tree species, we analysed tree-ring series of 678 trees in 45 plots throughout the Netherlands. Resilience indices were calculated based on growth reactions and growth recovery after drought. Furthermore, the impact of drought events on species productivity was quantified. We observed species-specific differences in growth responses to drought timing. All species in nearly all sites responded with growth reductions to drought, except sessile oak (Quercus petraea (Matt.) Liebl.). The most productive species in our study were found to be drought sensitive, with productivity losses of up to 30 % during drought in some sites. Productivity losses were highest on the driest soils. Resilience to the 2018 drought did not differ significantly from other drought years for six out of the nine studied species. However, 77.5 % of the individual trees of all studied species did not fully recover in growth within the following two years. Low post-drought growth remains poorly understood and should be taken into account in future studies to safeguard the health and productivity of the forest under climate change. We consider sessile oak a promising species for future forests in the Netherlands. Based on our results, we provide an outlook on future resilience and growth potential of the species studied under projected climate change for the Netherlands.

While the operating cost of electricity grids based on thermal generation was largely driven by the cost of fuel, as renewable penetration increases, ancillary services represent an increasingly large proportion of the running costs. Electric frequency is an important magnitude in highly renewable grids, as it becomes more volatile and therefore the cost related to maintaining it within safe bounds has significantly increased. So far, costs for frequency-containment ancillary services have been socialised in most countries, but it has become relevant to rethink this regulatory arrangement. In this paper, we discuss the issue of cost allocation for these services, highlighting the need to evolve towards a causation-based regulatory framework. We argue that parties responsible for creating the need for ancillary services should bear these costs. However, this would imply an important change in electricity market policy, therefore it is necessary to understand the impact on current and future investments on generation, as well as on electricity tariffs. Here we provide a mostly qualitative analysis of this issue, defining guidelines for practical implementation and further study. Published in journal Energy Policy