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Here, we investigate how stochasticity and age-dependence in energy dynamics influence maternal allocation in iteroparous females. We develop a state-dependent model to calculate the optimal maternal allocation strategy with respect to maternal age and energy reserves, focusing on allocation in a single offspring at a time. We introduce stochasticity in energetic costs– in terms of the amount of energy required to forage successfully and individual differences in metabolism – and in feeding success. We systematically assess how allocation is influenced by age-dependence in energetic costs, feeding success, energy intake per successful feeding attempt, and environmentally-driven mortality. First, using stochastic dynamic programming, we calculate the optimal amount of reserves M that mothers allocate to each offspring depending on their own reserves R and age A. The optimal life history strategy is then the set of allocation decisions M(R, A) over the whole lifespan which maximizes the total reproductive success of distant descendants. Second, we simulated the life histories of 1000 mothers following the optimisation strategy and the reserves at the start of adulthood R1, the distribution of which was determined, the distribution of which was determined using an iterative procedure as described . For each individual, we calculated maternal allocation Mt, maternal reserves Rt, and relative allocation Mt⁄Rt at each time period t. The relative allocation helps us to understand how resources are partitioned between mother and offspring. Third, we consider how the optimal strategy varies when there is age-dependence in resource acquisition, energetic costs and survival. Specifically, we include varying scenarios with an age-dependent increase or a decrease with age in energetic costs (c_t), feeding success (q_t), energy intake per successful feeding attempt (y_t), and environmentally-driven extrinsic mortality rate (d_t) (Table 2). We consider the age-dependence of parameters one at a time or in pairs, altering the slope, intercept, or asymptote of the age-dependence (linear or asymptotic function). Our aim is to identify whether the observed reproductive senescence can arise from optimal maternal allocation. As such, we do not impose a decline in selection in later life as all offspring are equally valuable at all ages (for a given maternal allocation), and there are no mutations. For each scenario, we run the backward iteration process with these age-dependent functions, obtain the allocation strategy, and simulate the life history of 1000 individuals based on the novel strategy. We then fit quadratic and linear models to the reproduction of these 1000 individuals using the lme function, nlme package in R. For these models, the response variable is the maternal allocation Mt and explanatory variables are the time period t and t2 (for the quadratic fit only), with individual identity as a random term. We use likelihood ratio tests to compare linear and quadratic models using the anova function (package nlme) with the maximum-likelihood method. If the comparison is significant (p-value <0.05), we considered the quadratic model to have a better fit, otherwise the linear model is considered more parsimonious. We were particularly interested in identifying scenarios where the fit was quadratic with a negative quadratic term. For each scenario, the pseudo R2 conditional value (proportion of variance explained by the fixed and random terms, accounting for individual identity) is calculated to assess the goodness-of-fit of the lme model, on a scale from 0 to 1, using the “r.squared” function, package gabtool. All calculations and coding are done in R. Iteroparous parents face a trade-off between allocating current resources to reproduction versus maximizing survival to produce further offspring. Optimal allocation varies across age, and follows a hump-shaped pattern across diverse taxa, including mammals, birds and invertebrates. This non-linear allocation pattern lacks a general theoretical explanation, potentially because most studies focus on offspring number rather than quality and do not incorporate uncertainty or age-dependence in energy intake or costs. Here, we develop a life history model of maternal allocation in iteroparous animals. We identify the optimal allocation strategy in response to stochasticity when energetic costs, feeding success, energy intake, and environmentally-driven mortality risk are age-dependent. As a case study, we use tsetse, a viviparous insect that produces one offspring per reproductive attempt and relies on an uncertain food supply of vertebrate blood. Diverse scenarios generate a hump-shaped allocation: when energetic costs and energy intake increase with age; and also when energy intake decreases, and energetic costs increase or decrease. Feeding success and mortality risk have little influence on age-dependence in allocation. We conclude that ubiquitous evidence for age-dependence in these influential traits can explain the prevalence of non-linear maternal allocation across diverse taxonomic groups.

Aim: Climate warming and biodiversity loss both alter plant productivity, yet we lack an understanding of how biodiversity regulates the responses of ecosystems to warming. In this study, we examine how plant diversity regulates the responses of grassland productivity to experimental warming using meta-analytic techniques. Location: Global Major taxa studied: Grassland ecosystems Methods: Our meta-analysis is based on warming responses of 40 different plant communities obtained from 20 independent studies on grasslands across five continents. Results: Our results show that plant diversity and its responses to warming were the most important factors regulating the warming effects on plant productivity, among all the factors considered (plant diversity, climate and experimental settings). Specifically, warming increased plant productivity when plant diversity (indicated by effective number of species) in grasslands was lesser than 10, whereas warming decreased plant productivity when plant diversity was greater than 10. Moreover, the structural equation modelling showed that the magnitude of warming enhanced plant productivity by increasing the performance of dominant plant species in grasslands of diversity lesser than 10. The negative effects of warming on productivity in grasslands with plant diversity greater than 10 were partly explained by diversity-induced decline in plant dominance. Main Conclusions: Our findings suggest that the positive or negative effect of warming on grassland productivity depends on how biodiverse a grassland is. This could mainly owe to differences in how warming may affect plant dominance and subsequent shifts in interspecific interactions in grasslands of different plant diversity levels.

The Intergovernmental Panel on Climate Change (IPCC) report that to limit warming to 1.5 °C, Bioenergy with Carbon Capture and Storage (BECCS) is required. Integrated assessment models (IAMS) predict that a land area between the size of Argentina and Australia is required for bioenergy crops, a 3–7 time increase in the current bioenergy planting area globally. The authors pose the question of whether vertical farming (VF) technology can enable BECCS deployment, either via land sparing or supply. VF involves indoor controlled environment cultivation, and can increase productivity per unit land area by 5–10 times. VF is predominantly being used to grow small, high value leafy greens with rapid growth cycles. Capital expenditure, operational expenditure, and sustainability are challenges in current VF industries, and will affect the ability to utilise this technology for other crops. The authors argue that, whilst challenging, VF could help reach wider climate goals. Application of VF for bioenergy crops could be a game changer in delivering BECCS technologies and may reduce the land footprint required as well as the subsequent associated negative environmental impacts. VF bioenergy could allow us to cultivate the future demand for bioenergy for BECCS on the same, or less, land area than is currently used globally.

Hydrochar, carbon-rich material produced during the thermochemical processing of biomass, is receiving increased attention due to its potential value as soil amendment. It can increase agroforestry systems’ productivity through direct and indirect effects on growth and soil quality. Hydrochar may also directly help mitigate climate change by sequestering stable carbon compounds in the soil and perhaps indirectly through increased C uptake by trees. In this research, we aim to evaluate how the application of hydrochar produced by two feedstock types, Cynara cardunculus L. (Hc) residuals and sewage sludge (Hs), and in two different doses (3 and 6 kg m−2) could improve the growth and water use efficiency of Populus alba L., a fast-growing tree species largely used in agroforestry as bioenergy crops and in C sequestration. We considered five plants per treatment, and we measured apical growth, secondary growth, leaf area and intrinsic water use efficiency in each plant for the whole growing season from February to October 2022. Our results highlighted that hydrochar applications stimulate the growth and water use efficiency of plants and that the double dose (6 kg m−2) of both hydrochars, and particularly Hc, had positive effects on plant performance, especially during extremely hot periods. Indeed, the year 2022 was characterized by a heat wave during the summer period, and this condition allowed us to evaluate how plants, growing in soils amended with hydrochar, could perform under climate extremes. Our findings showed that the control plants experienced severe damage in terms of dried stems and dried leaves during summer 2022, while hydrochar applications reduced these effects.

Model projections show that production of high-value products from microalgae could be profitable nowadays and commodities will become profitable within 10 years.

Despite the great number of models developed in research projects, only a small percentage have been successfully transferred to the productive sector. The PRIMA programme supported by Horizon 2020, the European Union Framework Programme for Research and Innovation, aims to reverse this situation. The SUPROMED project funded by PRIMA sought to develop an online platform composed of several models adapted to the requirements of end users for increasing the economic and environmental sustainability of Mediterranean agricultural systems. MOPECO, in its research version, was designed to maximize the profitability of irrigated farms in water-scarce regions. A simplified version of this model (MOPECO irrigation scheduling) was included in the SUPROMED platform for improving irrigation efficiency, providing farmers with a useful irrigation scheduling software. This paper shows the approach to adapt and transfer MOPECO to the productive sector. The tool was validated in three different demosite areas across the Mediterranean, involving local stakeholders in the design, validation, and dissemination of the software. The simplified tool reached similar or higher yields than farmers using less water. Thus, the average water saved was around 16%, while the average yield increased around 10% in the plots located in the three demosites of the project (Eastern Mancha in Spain, Bekaa valley in Lebanon, and Sidi Bouzid in Tunisia). This fact decreased the water footprint and increased the profitability of farms. The high applicability of the tool has generated interest among many technicians, farmers, and advisory enterprises. Furthermore, regional and national governmental extension services have shown interest in spreading the use of the tool across their territories, validating the methodology used for adapting and transferring a scientific model to the productive sector.

In the transition from linear production systems, unsustainable from the point of view of resources, to a model that finds strength in environmental, social and economic sustainability, the circular economy paradigm is the foundation that facilitates the planetary agro-ecological transition. The European Union has taken a number of steps (including the Circular Economy Package of Directives) shaping circularity as a wide-ranging driver measure involving many sectors. The paper intends to provide a regulatory framework on the current general situation regarding circularity in European Union, in order to extrapolate and give evidence to the aspects that intersect the agri-food sector. This is not only because they are poorly addressed in the literature, but also because there is a lack of regulatory instruments on the circular economy specifically addressing this area of interest. For this purpose, the analysis focuses on waste and residue/scrap management issues, recognized by law as by-products and end-of-waste status, as they are covered by circular economy legislation and as they can be applied to the agri-food sector. The latter allow the implementation of circularity strategies in the agri-food sector and, given the numerousness of production chains and the peculiarities of each of them, various regeneration and/or reuse processes of specific resources may be depicted. The intent is to provide useful knowledge on how to implement sustainable waste management, also proposing a concrete case on a by-product of olive oil processing, through which it is possible to highlight how the correct application of regulations favors the adoption of circular economic and management models in the firms involved, as well as informing the relevant economic operators on the possible profiles of legal liability that may arise from insufficient knowledge. Furthermore, this paper delves into the European Green Deal’s Strategy as it enriches the circular economy paradigm with new facets. NextGenerationEU and the National Recovery and Resilience Plan financially support this strategy in the aftermath of the socioeconomic crisis from COVID-19 in the EU Member States. This is in order to achieve the objective of achieving the agro-ecological transition.

In the agricultural sector, climate change (CC) affects multiple weather variables at different stages of crop cycles. CC may influence the mean level or affect the distribution of events (e.g., rainfall, temperature). This work evaluates the economic impact of CC-related changes in multiple climatic components, and the resulting uncertainty. For this purpose, a three-stage discrete stochastic programming model is used to represents farm sector of an irrigated area of Italy and to examine the influence of CC on rainfall and on maximum temperature. These variables affect the availability of water for agriculture and the water requirements of irrigated crops. The states of nature, and their change, are defined more broadly than in previous analyses; this allows examining the changes of more climatic variables and crops cultivation. The effect of CC is obtained by comparing the results of scenarios that represent the climatic conditions in the current situation and in the future. The results show that the agricultural sector would seek to lower costs by modifying patterns of land use, farming practices and increasing the use groundwater. The overall economic impact of these changes is small and due primarily to the reduced availability of water in the future. The temperature increase is, in fact, largely offset by the effects of the increase in CO2 levels, which boosts the yield of main crops of the irrigated zone. Therefore, availability and water management becomes a crucial factor to offset the increase of evapotranspiration and of water stress resulting from the increase of temperature. However, the costs of CC are very high for some types of farming, which suffer a large reduction in income.