The productive performance of intercropping.

Crop diversification has been put forward as a way to reduce the environmental impact of agriculture without penalizing its productivity. In this context, intercropping, the planned combination of two or more crop species in one field, is a promising practice. On an average, intercropping saves land compared with the component sole crops, but it remains unclear whether intercropping produces a higher yield than the most productive single crop per unit area, i.e., whether intercropping achieves transgressive overyielding. Here, we quantified the performance of intercropping for the production of grain, calories, and protein in a global meta-analysis of several production indices. The results show that intercrops outperform sole crops when the objective is to achieve a diversity of crop products on a given land area. However, when intercropping is evaluated for its ability to produce raw products without concern for diversity, intercrops on average generate a small loss in grain or calorie yield compared with the most productive sole crop (-4%) but achieve similar or higher protein yield, especially with maize/legume combinations grown at moderate N supply. Overall, although intercropping does not achieve transgressive overyielding on average, our results show that intercropping performs well in producing a diverse set of crop products and performs almost similar to the most productive component sole crop to produce raw products, while improving crop resilience, enhancing ecosystem services, and improving nutrient use efficiency. Our study, therefore, confirms the great interest of intercropping for the development of a more sustainable agricultural production, supporting diversified diets.

Climatic and biotic factors influencing regional declines and recovery of tropical forest biomass from the 2015/16 El Niño.

The 2015/16 El Niño brought severe drought and record-breaking temperatures in the tropics. Here, using satellite-based L-band microwave vegetation optical depth, we mapped changes of above-ground biomass (AGB) during the drought and in subsequent years up to 2019. Over more than 60% of drought-affected intact forests, AGB reduced during the drought, except in the wettest part of the central Amazon, where it declined 1 y later. By the end of 2019, only 40% of AGB reduced intact forests had fully recovered to the predrought level. Using random-forest models, we found that the magnitude of AGB losses during the drought was mainly associated with regionally distinct patterns of soil water deficits and soil clay content. For the AGB recovery, we found strong influences of AGB losses during the drought and of [Formula: see text]. [Formula: see text] is a parameter related to canopy structure and is defined as the ratio of two relative height (RH) metrics of Geoscience Laser Altimeter System (GLAS) waveform data-RH25 (25% energy return height) and RH100 (100% energy return height; i.e., top canopy height). A high [Formula: see text] may reflect forests with a tall understory, thick and closed canopy, and/or without degradation. Such forests with a high [Formula: see text] ([Formula: see text] ≥ 0.3) appear to have a stronger capacity to recover than low-[Formula: see text] ones. Our results highlight the importance of forest structure when predicting the consequences of future drought stress in the tropics.

Knowledge coproduction to improve assessments of nature's contributions to people.

Sustainability science needs new approaches to produce, share, and use knowledge because there are major barriers to translating research into policy and practice. Multiple actors hold relevant knowledge for sustainability including indigenous and local people who have developed over generations knowledge, methods, and practices that biodiversity and ecosystem assessments need to capture. Despite efforts to mainstream knowledge coproduction, less than 3% of the literature on nature's contributions to people (NCP) integrates indigenous and local knowledge (ILK). Approaches and tools to better integrate scientific and ILK knowledge systems in NCP assessments are urgently needed. To fill this gap, we conducted interviews with ILK experts from Abancay and Tamburco, Peru, and convened focus groups and workshops during which participatory mapping, a serious game, a Bayesian belief network based on ILK were introduced. We inventoried 60 medicinal plants used to treat different illnesses, and analyzed the spatial distribution of the 7 plants that contribute the most to a good quality of life, and delineated their nonmedicinal uses. Based on the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services conceptual framework, we defined dimensions of a good quality of life according to indigenous and local worldviews. Medicinal plants contributed strongly to health and household security, among other contributions. Climate change and overexploitation were the main perceived threats to medicinal plants, despite the existence of formal and customary institutions to regulate trade. Our approach was flexible enough to integrate diverse forms of knowledge, as well as qualitative and quantitative information from, for example, the Bayesian belief network.

Coproducción de conocimiento para mejorar la evaluación de las contribuciones de la naturaleza para las personas Resumen La ciencia de la sostenibilidad necesita nuevos enfoques para producir, compartir y utilizar los conocimientos, ya que existen grandes obstáculos para trasladar la investigación a la política y la práctica. Varios actores poseen conocimientos relevantes para la sostenibilidad, incluidos los pueblos originarios y locales que han desarrollado conocimientos, métodos y prácticas a lo largo de generaciones, que deben reflejarse en las evaluaciones de la biodiversidad y los ecosistemas. A pesar de los esfuerzos por integrar la coproducción de conocimientos, <3% de la bibliografía sobre las contribuciones de la naturaleza a las personas (CNP) integra los conocimientos autóctonos y locales (CAL). Se necesitan urgentemente enfoques y herramientas para integrar mejor los sistemas de conocimiento científico y los conocimientos autóctonos y locales en las evaluaciones de los CNP. Para llenar este vacío, realizamos entrevistas con expertos en CAL de Abancay y Tamburco, Perú, y convocamos grupos focales y talleres durante los cuales se introdujeron el mapeo participativo, un juego serio y una red de creencia bayesiana basada en CAL. Inventariamos 60 plantas medicinales utilizadas para tratar diferentes enfermedades y analizamos la distribución espacial de las siete especies de plantas que más contribuyen a una buena calidad de vida y delineamos sus usos no medicinales. A partir del marco conceptual de la Plataforma Intergubernamental Científico-Normativa sobre Diversidad Biológica y Servicios de los Ecosistemas, definimos las dimensiones de una buena calidad de vida según las cosmovisiones autóctonas y locales. Las plantas medicinales contribuían en gran medida a la salud y a la seguridad de los hogares, entre otras aportaciones. El cambio climático y la sobreexplotación fueron las principales amenazas percibidas para las plantas medicinales a pesar de la existencia de instituciones tradicionales que regulan el mercado. Nuestra estrategia fue lo suficientemente flexible para integrar el conocimiento diverso, así como la información cualitativa y cuantitativa, como por ejemplo la red de creencia bayesiana.

Surface temperatures reveal the patterns of vegetation water stress and their environmental drivers across the tropical Americas.

Vegetation is a key component in the global carbon cycle as it stores ~450 GtC as biomass, and removes about a third of anthropogenic CO2 emissions. However, in some regions, the rate of plant carbon uptake is beginning to slow, largely because of water stress. Here, we develop a new observation-based methodology to diagnose vegetation water stress and link it to environmental drivers. We used the ratio of remotely sensed land surface to near surface atmospheric temperatures (LST/Tair ) to represent vegetation water stress, and built regression tree models (random forests) to assess the relationship between LST/Tair and the main environmental drivers of surface energy fluxes in the tropical Americas. We further determined ecosystem traits associated with water stress and surface energy partitioning, pinpointed critical thresholds for water stress, and quantified changes in ecosystem carbon uptake associated with crossing these critical thresholds. We found that the top drivers of LST/Tair , explaining over a quarter of its local variability in the study region, are (1) radiation, in 58% of the study region; (2) water supply from precipitation, in 30% of the study region; and (3) atmospheric water demand from vapor pressure deficits (VPD), in 22% of the study region. Regions in which LST/Tair variation is driven by radiation are located in regions of high aboveground biomass or at high elevations, while regions in which LST/Tair is driven by water supply from precipitation or atmospheric demand tend to have low species richness. Carbon uptake by photosynthesis can be reduced by up to 80% in water-limited regions when critical thresholds for precipitation and air dryness are exceeded simultaneously, that is, as compound events. Our results demonstrate that vegetation structure and diversity can be important for regulating surface energy and carbon fluxes over tropical regions.

Uncovering the critical soil moisture thresholds of plant water stress for European ecosystems.

Understanding the critical soil moisture (SM) threshold (θcrit ) of plant water stress and land surface energy partitioning is a basis to evaluate drought impacts and improve models for predicting future ecosystem condition and climate. Quantifying the θcrit across biomes and climates is challenging because observations of surface energy fluxes and SM remain sparse. Here, we used the latest database of eddy covariance measurements to estimate θcrit across Europe by evaluating evaporative fraction (EF)-SM relationships and investigating the covariance between vapor pressure deficit (VPD) and gross primary production (GPP) during SM dry-down periods. We found that the θcrit and soil matric potential threshold in Europe are 16.5% and -0.7 MPa, respectively. Surface energy partitioning characteristics varied among different vegetation types; EF in savannas had the highest sensitivities to SM in water-limited stage, and the lowest in forests. The sign of the covariance between daily VPD and GPP consistently changed from positive to negative during dry-down across all sites when EF shifted from relatively high to low values. This sign of the covariance changed after longer period of SM decline in forests than in grasslands and savannas. Estimated θcrit from the VPD-GPP covariance method match well with the EF-SM method, showing this covariance method can be used to detect the θcrit . We further found that soil texture dominates the spatial variability of θcrit while shortwave radiation and VPD are the major drivers in determining the spatial pattern of EF sensitivities. Our results highlight for the first time that the sign change of the covariance between daily VPD and GPP can be used as an indicator of how ecosystems transition from energy to SM limitation. We also characterized the corresponding θcrit and its drivers across diverse ecosystems in Europe, an essential variable to improve the representation of water stress in land surface models.

Positive but variable effects of crop diversification on biodiversity and ecosystem services.

Ecological theory suggests that biodiversity has a positive and stabilizing effect on the delivery of ecosystem services. Yet, the impacts of increasing the diversity of cultivated crop species or varieties in agroecosystems are still under scrutiny. The available empirical evidence is scattered in scope, agronomic and geographic contexts, and impacts on ecosystem services may depend on the type of diversification strategy used. To robustly assess the effects of crop diversification in agroecosystems, we compiled the results of 95 meta-analyses integrating 5156 experiments conducted over 84 experimental years and representing more than 54,500 paired observations on 120 crop species in 85 countries. Overall, our synthesis of experimental data from across the globe shows that crop diversification enhances not only crop production (median effect +14%) but also the associated biodiversity (+24%, i.e., the biodiversity of non-cultivated plants and animals), and several supporting and regulating ecosystem services including water quality (+51%), pest and disease control (+63%) and soil quality (+11%). However, there was substantial variability in the results for each individual ecosystem service between different diversification strategies such as agroforestry, intercropping, cover crops, crop rotation or variety mixtures. Agroforestry is particularly effective in delivering multiple ecosystem services, that is, water regulation and quality, pest and diseases regulation, associated biodiversity, long-term soil productivity and quality. Variety mixtures, instead, provide the lowest benefits, whereas the other strategies show intermediate results. Our results highlight that while increasing the diversity of cultivated crop species or varieties in agroecosystems represents a very promising strategy for more sustainable land management, contributing to enhanced yields, enhanced biodiversity and ecosystem services, some crop diversification strategies are more effective than others in supporting key ecosystem services.

Does the Latent Period of Leaf Fungal Pathogens Reflect Their Trophic Type? A Meta-Analysis of Biotrophs, Hemibiotrophs, and Necrotrophs.

We performed a meta-analysis to search for a relation between the trophic type and latent period of fungal pathogens. The pathogen incubation period and the level of resistance of the hosts were also investigated. This ecological knowledge would help us to more efficiently regulate crop epidemics for different types of pathogens. We gathered latent period data from 103 studies dealing with 51 fungal pathogens of the three major trophic types (25 biotrophs, 15 hemibiotrophs, and 11 necrotrophs), representing 2,542 mean latent periods. We show that these three trophic types display significantly different latent periods. Necrotrophs exhibited the shortest latent periods (<100 degree-days [DD]), biotrophs had intermediate ones (between 100 and 200 DD), and hemibiotrophs had the longest latent periods (>200 DD). We argue that this relation between trophic type and latent period points to two opposing host exploitation strategies: necrotrophs mount a rapid destructive attack on the host tissue, whereas biotrophs and hemibiotrophs avoid or delay the damaging phase. We query the definition of hemibiotrophic pathogens and discuss whether the length of the latent period is determined by the physiological limits inherent to each trophic type or by the adaptation of pathogens of different trophic types to the contrasting conditions experienced in their interaction with the host.

Timing of Grape Downy Mildew Onset in Bordeaux Vineyards.

Grapevine downy mildew (GDM) is a severe disease of grapevines. Because of the lack of reliable information about the dates of GDM symptom onset, many vine growers begin fungicide treatments early in the season. We evaluate the extent to which such preventive treatments are justified. Observational data for 266 untreated sites for the years between 2010 and 2017 were used to estimate the timing of GDM onset on vines and bunches of grapes in South West France (Bordeaux region) through survival analyses. The onset of GDM was not apparent on vines and bunches before early to mid-May, and the rate of GDM symptom appearance was highly variable across years. Depending on the year, 50% of the plots displayed symptoms between mid-May and late June for vines. For several years, our statistical analysis revealed that the proportion of plots with no symptoms was high in early August on vines (27.5 and 43.7% in 2013 and 2016) and on bunches (between 23 and 79% in 2011, 2013, and 2016). We found a significant effect of the amount of rainfall in spring on the date of symptom appearance. These results indicate that preventive fungicide application is unjustified in many vineyards, and that regional disease surveys should be used to adjust fungicide treatment dates according to local characteristics, in particular according to rainfall conditions in spring.

Meta-modeling methods for estimating ammonia volatilization from nitrogen fertilizer and manure applications.

Accurate estimations of ammonia (NH3) emissions due to nitrogen (N) fertilization are required to identify efficient mitigation techniques and improve agricultural practices. Process-based models such as Volt'Air can be used for this purpose because they incorporate the effects of several key factors influencing NH3 volatilization at fine spatio-temporal resolutions. However, these models require a large number of input variables and their implementation on a large scale requires long computation times that may restrict their use by public environmental agencies. In this study, we assess the capabilities of various types of meta-models to emulate the complex process-based Volt'Air for estimating NH3 emission rates from N fertilizer and manure applications. Meta-models were developed for three types of fertilizer (N solution, cattle farmyard manure, and pig slurry) for four major agricultural French regions (Bretagne, Champagne-Ardenne, Ile-de-France, and Rhône-Alpes) and at the national (France) scale. The meta-models were developed from 106,092 NH3 emissions simulated by Volt'Air in France. Their performances were evaluated by cross-validation, and the meta-models providing the best approximation of the original model were selected. The results showed that random forest and ordinary linear regression models were more accurate than generalized additive models, partial least squares regressions, and least absolute shrinkage and selection operator regressions. Better approximations of Volt'Air simulations were obtained for cattle farmyard manure (3% < relative root mean square error of prediction (RRMSEP) < 8%) than for pig slurry (17% < RRMSEP < 19%) and N solution (21% < RRMSEP < 40%). The selected meta-models included between 6 and 15 input variables related to weather conditions, soil properties and cultural practices. Because of their simplicity and their short computation time, our meta-models offer a promising alternative to process-based models for NH3 emission inventories at both regional and national scales. Our approach could be implemented to emulate other process-based models in other countries.

The important but weakening maize yield benefit of grain filling prolongation in the US Midwest.

A better understanding of recent crop yield trends is necessary for improving the yield and maintaining food security. Several possible mechanisms have been investigated recently in order to explain the steady growth in maize yield over the US Corn-Belt, but a substantial fraction of the increasing trend remains elusive. In this study, trends in grain filling period (GFP) were identified and their relations with maize yield increase were further analyzed. Using satellite data from 2000 to 2015, an average lengthening of GFP of 0.37 days per year was found over the region, which probably results from variety renewal. Statistical analysis suggests that longer GFP accounted for roughly one-quarter (23%) of the yield increase trend by promoting kernel dry matter accumulation, yet had less yield benefit in hotter counties. Both official survey data and crop model simulations estimated a similar contribution of GFP trend to yield. If growing degree days that determines the GFP continues to prolong at the current rate for the next 50 years, yield reduction will be lessened with 25% and 18% longer GFP under Representative Concentration Pathway 2.6 (RCP 2.6) and RCP 6.0, respectively. However, this level of progress is insufficient to offset yield losses in future climates, because drought and heat stress during the GFP will become more prevalent and severe. This study highlights the need to devise multiple effective adaptation strategies to withstand the upcoming challenges in food security.

Sampling Strategies for Evaluating the Rate of Adventitious Transgene Presence in Non-Genetically Modified Crop Fields.

According to E.U. regulations, the maximum allowable rate of adventitious transgene presence in non-genetically modified (GM) crops is 0.9%. We compared four sampling methods for the detection of transgenic material in agricultural non-GM maize fields: random sampling, stratified sampling, random sampling + ratio reweighting, random sampling + regression reweighting. Random sampling involves simply sampling maize grains from different locations selected at random from the field concerned. The stratified and reweighting sampling methods make use of an auxiliary variable corresponding to the output of a gene-flow model (a zero-inflated Poisson model) simulating cross-pollination as a function of wind speed, wind direction, and distance to the closest GM maize field. With the stratified sampling method, an auxiliary variable is used to define several strata with contrasting transgene presence rates, and grains are then sampled at random from each stratum. With the two methods involving reweighting, grains are first sampled at random from various locations within the field, and the observations are then reweighted according to the auxiliary variable. Data collected from three maize fields were used to compare the four sampling methods, and the results were used to determine the extent to which transgene presence rate estimation was improved by the use of stratified and reweighting sampling methods. We found that transgene rate estimates were more accurate and that substantially smaller samples could be used with sampling strategies based on an auxiliary variable derived from a gene-flow model.

Spatially explicit estimates of N2 O emissions from croplands suggest climate mitigation opportunities from improved fertilizer management.

With increasing nitrogen (N) application to croplands required to support growing food demand, mitigating N2 O emissions from agricultural soils is a global challenge. National greenhouse gas emissions accounting typically estimates N2 O emissions at the country scale by aggregating all crops, under the assumption that N2 O emissions are linearly related to N application. However, field studies and meta-analyses indicate a nonlinear relationship, in which N2 O emissions are relatively greater at higher N application rates. Here, we apply a super-linear emissions response model to crop-specific, spatially explicit synthetic N fertilizer and manure N inputs to provide subnational accounting of global N2 O emissions from croplands. We estimate 0.66 Tg of N2 O-N direct global emissions circa 2000, with 50% of emissions concentrated in 13% of harvested area. Compared to estimates from the IPCC Tier 1 linear model, our updated N2 O emissions range from 20% to 40% lower throughout sub-Saharan Africa and Eastern Europe, to >120% greater in some Western European countries. At low N application rates, the weak nonlinear response of N2 O emissions suggests that relatively large increases in N fertilizer application would generate relatively small increases in N2 O emissions. As aggregated fertilizer data generate underestimation bias in nonlinear models, high-resolution N application data are critical to support accurate N2 O emissions estimates.

How do various maize crop models vary in their responses to climate change factors?

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models diverge on grain yield responses to changes in climatic factors, or whether they agree in their general trends related to phenology, growth, and yield. With the goal of analyzing the sensitivity of simulated yields to changes in temperature and atmospheric carbon dioxide concentrations [CO2 ], we present the largest maize crop model intercomparison to date, including 23 different models. These models were evaluated for four locations representing a wide range of maize production conditions in the world: Lusignan (France), Ames (USA), Rio Verde (Brazil) and Morogoro (Tanzania). While individual models differed considerably in absolute yield simulation at the four sites, an ensemble of a minimum number of models was able to simulate absolute yields accurately at the four sites even with low data for calibration, thus suggesting that using an ensemble of models has merit. Temperature increase had strong negative influence on modeled yield response of roughly -0.5 Mg ha(-1) per °C. Doubling [CO2 ] from 360 to 720 µmol mol(-1) increased grain yield by 7.5% on average across models and the sites. That would therefore make temperature the main factor altering maize yields at the end of this century. Furthermore, there was a large uncertainty in the yield response to [CO2 ] among models. Model responses to temperature and [CO2 ] did not differ whether models were simulated with low calibration information or, simulated with high level of calibration information.

Mitigating nitrogen losses with almost no crop yield penalty during extremely wet years.

Climate change-induced precipitation anomalies during extremely wet years (EWYs) result in substantial nitrogen losses to aquatic ecosystems (Nw). Still, the extent and drivers of these losses, and effective mitigation strategies have remained unclear. By integrating global datasets with well-established crop modeling and machine learning techniques, we reveal notable increases in Nw, ranging from 22 to 56%, during historical EWYs. These pulses are projected to amplify under the SSP126 (SSP370) scenario to 29 to 80% (61 to 120%) due to the projected increases in EWYs and higher nitrogen input. We identify the relative precipitation difference between two consecutive years (diffPr) as the primary driver of extreme Nw. This finding forms the basis of the CLimate Extreme Adaptive Nitrogen Strategy (CLEANS), which scales down nitrogen input adaptively to diffPr, leading to a substantial reduction in extreme Nw with nearly zero yield penalty. Our results have important implications for global environmental sustainability and while safeguarding food security.

Drivers of natural gas use in U.S. residential buildings.

Natural gas is the primary fuel used in U.S. residences, yet little is known about its consumption patterns and drivers. We use daily county-level gas consumption data to assess the spatial patterns of the relationships and the sensitivities of gas consumption to outdoor air temperature across U.S. households. We fitted linear-plus-plateau functions to daily gas consumption data in 1000 counties, and derived two key coefficients: the heating temperature threshold (Tcrit) and the gas consumption rate change per 1°C temperature drop (Slope). We identified the main predictors of Tcrit and Slope (like income, employment rate, and building type) using interpretable machine learning models built on census data. Finally, we estimated a potential 2.47 million MtCO2 annual emission reduction in U.S. residences by gas savings due to household insulation improvements and hypothetical behavioral change toward reduced consumption by adopting a 1°C lower Tcrit than the current value.

A dataset for analyzing the climate change response of grain quality of 48 Japanese rice cultivars with contrasting levels of heat tolerance.

Climate change has a significant impact on rice grain appearance quality; in particular, high temperatures during the grain filling period increase the rate of chalky immature grains, reducing the marketability of rice. Heat-tolerant cultivars have been bred and released to reduce the rate of chalky grain and improve rice quality under high temperatures, but the ability of these cultivars to actually reduce chalky grain content has never been demonstrated due to the lack of integrated datasets. Here, we present a dataset collected through a systematic literature search from publicly available data sources, for the quantitative analysis of the impact of meteorological factors on grain appearance quality of various rice cultivars with contrasted heat tolerance levels. The dataset contains 1302 field observations of chalky grain rates (%) - a critical trait affecting grain appearance sensitive to temperature shocks - for 48 cultivars covering five different heat-tolerant ranks (HTRs) collected at 44 sites across Japan. The dataset also includes the values of key meteorological variables during the grain filling period, such as the cumulative mean air temperature above the threshold temperature (TaHD), mean solar radiation, and mean relative humidity over 20 days after heading, obtained from a gridded daily meteorological dataset with a 1-km resolution developed by the National Agriculture and Food Research Organization. The dataset covers major commercial rice cultivars cultivated in Japan in different environmental conditions. It is a useful resource for analyzing the climate change impact on crop quality and assess the effectiveness of genetic improvements in heat tolerance. Its value has been illustrated in the research article entitled "Effectiveness of heat tolerance rice cultivars in preserving grain appearance quality under high temperatures - A meta-analysis", where the dataset was used to develop a statistical model quantifying the effects of high temperature on grain quality as a function of cultivar heat tolerance.

Global spatially explicit yield gap time trends reveal regions at risk of future crop yield stagnation.

Yield gaps, here defined as the difference between actual and attainable yields, provide a framework for assessing opportunities to increase agricultural productivity. Previous global assessments, centred on a single year, were unable to identify temporal variation. Here we provide a spatially and temporally comprehensive analysis of yield gaps for ten major crops from 1975 to 2010. Yield gaps have widened steadily over most areas for the eight annual crops and remained static for sugar cane and oil palm. We developed a three-category typology to differentiate regions of 'steady growth' in actual and attainable yields, 'stalled floor' where yield is stagnated and 'ceiling pressure' where yield gaps are closing. Over 60% of maize area is experiencing 'steady growth', in contrast to ∼12% for rice. Rice and wheat have 84% and 56% of area, respectively, experiencing 'ceiling pressure'. We show that 'ceiling pressure' correlates with subsequent yield stagnation, signalling risks for multiple countries currently realizing gains from yield growth.

Global critical soil moisture thresholds of plant water stress.

During extensive periods without rain, known as dry-downs, decreasing soil moisture (SM) induces plant water stress at the point when it limits evapotranspiration, defining a critical SM threshold (θcrit). Better quantification of θcrit is needed for improving future projections of climate and water resources, food production, and ecosystem vulnerability. Here, we combine systematic satellite observations of the diurnal amplitude of land surface temperature (dLST) and SM during dry-downs, corroborated by in-situ data from flux towers, to generate the observation-based global map of θcrit. We find an average global θcrit of 0.19 m3/m3, varying from 0.12 m3/m3 in arid ecosystems to 0.26 m3/m3 in humid ecosystems. θcrit simulated by Earth System Models is overestimated in dry areas and underestimated in wet areas. The global observed pattern of θcrit reflects plant adaptation to soil available water and atmospheric demand. Using explainable machine learning, we show that aridity index, leaf area and soil texture are the most influential drivers. Moreover, we show that the annual fraction of days with water stress, when SM stays below θcrit, has increased in the past four decades. Our results have important implications for understanding the inception of water stress in models and identifying SM tipping points.

Risk assessment of Retithrips syriacus for the EU.

Following a request from the European Commission, the EFSA Panel on Plant Health performed a quantitative risk assessment for the EU of Retithrips syriacus (Mayet) (Thysanoptera: Thripidae), a polyphagous thrips, regarded as a tropical/subtropical pest occurring in several countries of Africa, South America, Asia and in the EU in Cyprus. The current risk assessment focused on potential pathways for entry, the climatic conditions allowing establishment, the expected spread capacity and the impact considering a time horizon of 10 years (2023-2032). The Panel identified the import of cut roses, persimmons, table grapes, as well as plants for planting of the genera Acalypha and Terminalia from third countries and those of Persea americana (avocado) from Israel as the most relevant entry pathways to consider. Over the next 10 years, an annual median estimate of 95 (90% Certainty Range, CR, ranging from 13 to 1832) potential R. syriacus founder populations per year are expected to successfully transfer to a suitable host in the EU NUTS2 regions where the climatic conditions are predicted as suitable for establishment; this value drops to a median of 4.6 founder populations per year (90% CR: 1 every 1.9 years - 85.6 per year) after considering the actual probability of establishment of a potential founder population. The estimated number of founder population per year is mostly driven by the import of cut roses and plants for planting. If such founder populations were to establish, R. syriacus is estimated to spread at a median rate of 0.05 km/year (90% CR 0.02-2.30 km/year) after a median lag phase of 1.1 years (90% CR 0.3-3.3 years). The overall impact on yield (expressed as % of the total agricultural production) directly attributable to R. syriacus when considering: (i) the main R. syriacus hosts in the EU, (ii) the areas of the EU where establishment is possible, (iii) the current agricultural practices and (iv) the evidence of impact from the countries where the pest is established for a long time, was estimated at 0.065% as the median value of the uncertainty distribution (90% CR 0.001%-0.571%). Options for risk reduction are discussed, but the effectiveness was not quantified.

An experimental dataset on yields of pulses across Europe.

Future European agriculture should achieve high productivity while limiting its impact on the environment. Legume-supported crop rotations could contribute to these goals, as they request less nitrogen (N) fertilizer inputs, show high resource use efficiency and support biodiversity. However, legumes grown for their grain (pulses) are not widely cultivated in Europe. To further expand their cultivation, it remains crucial to better understand how different cropping and environmental features affect pulses production in Europe. To address this gap, we collected the grain yields of the most cultivated legumes across European countries, from both published scientific papers and unpublished experiments of the European projects LegValue and Legato. Data were integrated into an open-source, easily updatable dataset, including 5229 yield observations for five major pulses: chickpea (Cicer arietinum L.), faba bean (Vicia faba L.), field pea (Pisum sativum L.), lentil (Lens culinaris Medik.), and soybean (Glycine max (L.) Merr.). These data were collected in 177 field experiments across 21 countries, from 37° N (southern Italy) to 63° N (Finland) of latitude, and from ca. 8° W (western Spain) to 47° E (Turkey), between 1980 and 2020. Our dataset can be used to quantify the effects of the soil, climate, and agronomic factors affecting pulses yields in Europe and could contribute to identifying the most suitable cropping areas in Europe to grow pulses.