National Institute for Agro-Environmental Sciences in Japan

The increase in forest wood biomass is an important carbon sink in the biogeochemical carbon cycle. The effect of forest succession on carbon sequestration is essential for predicting the impact of future global warming. However, there are limited studies considering the temporal changes of net primary production (NPP) with secondary succession in cool-temperate forests based on long-term data. Therefore, we monitored changes in forest structures over 23 years in a cool-temperate secondary forest (Takayama Forest), which is dominated by Quercus crispula, Betula ermanii, and B. platyphylla var. japonica, to determine how individual stem growth and wood NPP changes with succession and thereby affect biomass accumulation. For Q. crispula and B. ermanii, the population size reduction (71% and 66% of the initial number, respectively) exhibited density-dependent mortality in the self-thinning process. Conversely, B. platyphylla var. japonica decreased from 125 to 51 stems ha⁻¹, or a reduced to 41%, due to interspecific competition and/or short longevity of the species. The individual stem growth of the smaller shaded trees was suppressed, and the mean individual biomass did not increase notably with decreasing density compared to that of Q. crispula and B. ermanii. The aboveground wood NPP for this forest averaged 2.44 Mg ha⁻¹ year⁻¹ over 23 years and exhibited no significant temporal trend. The increased mean individual stem growth of Q. crispula and B. ermanii with decreasing density compensated for the total wood NPP. As a result, the aboveground biomass of the forest increased by 21% over 23 years from 132.4 to 160.2 Mg ha⁻¹, and the rate of biomass accumulation increased with decreasing mortality. It is unlikely that the wood NPP of these pioneer secondary forests will decrease with forest development after canopy closure because the individual stem growth of the long-lived pioneers, such as Q. crispula and B. ermanii, is maintained for extended durations.

Simulating gross primary production (GPP) is a key objective of terrestrial ecosystem models, and many studies have shown that solar‐induced chlorophyll fluorescence (SIF) is a reliable proxy for GPP. This study combines SIF data with a process‐based vegetation integrative simulator for trace gases (VISIT‐SIF) model to enhance GPP simulations in the Mase rice paddy field in Tsukuba, Japan. Using data assimilation techniques (Bayesian optimization) with both ground‐based SIF data and satellite‐derived SIF (CSIF) (both from 29 June 2019 to 10 September 2020), we optimized key model parameters and improved the simulation of GPP. Sensitivity analysis via SHapley Additive exPlanations (SHAP) revealed that the maximum rate of carboxylation (Vcamx)‐related parameters significantly influence GPP, while the absorbed photosynthetically active radiation (APAR)‐related parameters are more critical for SIF modeling. Model optimization resulted in substantial performance improvements, particularly in simulating GPP at half‐hourly and daily scales. For half‐hourly results, the R² values of SIF improved from 0.37 to 0.60, and the relative error decreased from 124.21% to 63.39%, but the model went from underestimation to overestimation; for GPP, R² values improved from 0.47 to 0.68, relative error decreased from 150.00% to 47.85%, and the model's tendency to underestimate has been mitigated. At the daily scale, model simulations demonstrated higher R² values and lower relative errors than observations. Using the CSIF data set also improved the model but was less effective than densely measured ground SIF. Further, we explored the relationship between SIF and GPP on half‐hourly scale, daily scale, and weekly scale and found that the larger the time scale, the stronger the linear relationship of SIF‐GPP. Overall, using SIF as a proxy for GPP and optimizing key parameters through data assimilation significantly enhanced the simulation accuracy of the VISIT model. However, challenges remain, such as model biases under cloudy conditions and SIF overestimation during specific stages. This research demonstrates the value of assimilating SIF data into the VISIT model and highlights the potential of satellite‐derived SIF for improving GPP estimations, though at a small scale.

Understanding the current status of biodiversity is crucial to preventing its loss in a changing world. We examined changes in the geographical range size and abundance of 165 bird species breeding in Japan during the past 40 years, as well as temperature niche changes in the past 20 years. Higher temperatures were recorded within the ranges of non-native species than in those of native species, and we detected range-size expansion and increased abundance among non-native species. Although open-land species exhibited range reductions from the 1970s to the 1990s, many recovered and the ranges of only a few species declined after this period. Nevertheless, the abundance of open-land species did decline, despite range-size recovery; similar inconsistencies were detected for waterbirds and raptors. Analysis of long-term temperatures suggested that species left warmest areas within their distributions while maximum temperatures experienced by species during the survey years did not change systematically. Birds in warm regions may be facing a crisis, with attrition of native bird communities and expansion of non-native species. It is necessary to establish efficient measures to prevent further expansions of non-native species and conservation measures of native species within managed areas in warm regions with few intact habitats.

Sri Lanka’s rice systems are subject to low yield events that threaten national food security. Extreme climate events during the cropping season are the main cause, but whether human-induced climate change has contributed to low yield events is an open question. Here, we present an impact attribution analysis that quantifies the effect of climate change to the average yield in 1981–2019 and the low yield event that occurred in 2017 using factual and counterfactual climate model simulations as inputs to three process-based crop models, DSSAT, APSIM and CYGMA. All of the crop models consistently show that climate change has decreased average yield by − 4.99% to − 0.20%, compared to that without climate change. However, the effect of climate change to the 2017 event is mixed in the sign across the crop models. When using a multi-model ensemble average (MME) of the three crop models, a significant negative impact on the Yala season is detected. The large uncertainties associated with the use of different crop models also make it inconclusive whether the 2017-level low yield events would become more frequent and severer by mid-century (2031–2069) under projected climates than under the present-day climate. The same result was derived even when MME is used. These results underscore the need for improved impact attribution to inform climate negotiations on the development of climate-resilient agri-food systems in low-income countries through the Loss and Damage mechanism. Supplementary Information The online version contains supplementary material available at 10.1038/s41598-025-00262-5.

Soil particles in plant rooting zones are largely clustered to form porous structural units called aggregates where highly diverse microorganisms inhabit and drive biogeochemical cycling. The complete extraction of microbial cells and DNA from soil is a substantial task as certain microorganisms exhibit strong adhesion to soil surfaces and/or inhabit deep within aggregates. However, the degree of aggregate dispersion and the efficacy of extraction have rarely been examined, and thus, adequate cell extraction methods from soil remain unclear. We aimed to develop an optimal method of cell extraction for single-cell genomics (SCG) analysis of single soil aggregates by focusing on water-stable macroaggregates (diameter: 5.6–8.2 mm) from the topsoil of cultivated Acrisol. We postulated that the extraction of microorganisms with distinct taxonomy and functions could be achieved depending on the degree of soil aggregate dispersion. To test this idea, we used six individual aggregates and performed both SCG sequencing and amplicon analysis. While both bead-vortexing and sonication dispersion techniques improved the extractability of bacterial cells compared to previous ones, the sonication technique led to more efficient dispersion and yielded a higher number and more diverse microorganisms than the bead technique. Furthermore, the analyses of nitrogen cycling and exopolysaccharides-related genes suggested that the sonication-assisted extraction led to the greater recovery of microorganisms strongly attached to soil particles and/or inhabited the aggregate subunits that were more physically stable (e.g., aggregate core). Further SCG analysis revealed that all six aggregates held intact microorganisms holding the genes (potentials) to convert nitrate into all possible nitrogen forms while some low-abundance genes showed inter-aggregate heterogeneity. Overall, all six aggregates studied showed similarities in pore characteristics, phylum-level composition, and microbial functional redundancy. Together, these results suggest that water-stable macroaggregates may act as a functional unit in soil and show potential as a useful experimental unit in soil microbial ecology. Our study also suggests that conventional methods employed for the extraction of cells and DNA may not be optimal. The findings of this study emphasize the necessity of advancing extraction methodologies to facilitate a more comprehensive understanding of microbial diversity and function in soil environments.

Rice paddies are substantial sources of methane emissions, with CH4 released into the atmosphere through three pathways: molecular diffusion of dissolved methane across the atmosphere-water boundary, ebullition of gas bubbles, and diffusive transport through the aerenchyma tissue of rice plants. This study aimed to analyze seasonal variations in CH4 fluxes separately via ebullition and rice plants and to explore the potential relation between in situ gas volume and CH4 emissions. Field monitoring of CH4 emissions, gas-filled porosity (that is, bubble volume), and soil temperature was conducted in a rice paddy with four different treatments: plots with/without rice plants (Oryza sativa ‘Koshihikari’) and with/without straw application. Results indicated that both total CH4 flux and CH4 ebullition were higher during the ripening stage for plots without straw application, and over 60% of total CH4 flux was attributed to ebullition. Rice straw application enhanced both fluxes during the early vegetative stages. Seasonal trends in the total CH4 flux corresponded to those of the CH4 flux via ebullition. Gas-filled porosity increased during the vegetative stage, particularly in plots with straw application, reaching maximum values during the late reproductive stage. The methane flux via rice plants and ebullition correlated well with gas-filled porosity during the vegetative and reproductive stages. This study suggests that the size of the gaseous CH4 pool is a good measure for estimating the flux intensities of both the ebullition- and rice-mediated pathways.

Prey–predator interaction is a phenomenon important to our understanding of community dynamics. Mantises and web-weaving spiders are predators that belong to the same guild, and they can be each other’s predator and prey. However, their relationship is generally asymmetrical, with spiders often being the prey of the mantises. Here, we report a rare opposite case in which an adult female mantis, Hierodula chinensis Werner, 1929, was preyed upon by the orb-web weaving spider Gibbaranea abscissa (Karsch, 1879), without using a web, in a late autumn field in Japan. We suggest that differences in cold tolerance allowed the small spider to hunt a mantis prey that was approximately eight times its size.

Agricultural research and development (R&D) has increased crop yields, but little is known about its ability to increase yield stability in the context of increasingly frequent extreme weather events. Using a grid yield dataset, we show that from 2000 to 2019, the standard deviation (SD) of yield anomalies for maize, rice, wheat and soybean, increased in 20% of the global harvested area. Based on random forest models relating yield anomaly to climate, soil, management and public R&D expenditure, we show that cumulative agricultural R&D expenditure, proportion of growing season exposed to optimal hourly temperatures, and dry and very wet days are key factors explaining crop yield variability. An attribution analysis based on large ensemble climate simulations with and without human influence on the global climate shows that unfavorable agro-climatic conditions due to climate change has increased SD, while higher R&D expenditure has led to more contrasting trends in SD over 2000-2019. Although R&D has continued steadily in most countries, this study indicates that the progress made in R&D since 2000 may have lagged behind the unfavorable effect of climate change on yield variability.

Agricultural intensification is a leading cause of biodiversity loss worldwide. However, the traditional agroecosystems are often associated with high avian diversity because of their landscape heterogeneity, offering available niches to different bird species. Here, we focused on the temporal changes in taxonomic, functional, and phylogenetic diversity of avian communities from Satoyama traditional agricultural landscapes of Japan. We found significant temporal trends (e.g. increasing) in overall species richness, forest specialist species richness, phylogenetic diversity, and phylogenetic relatedness within avian assemblages, regardless of the land use composition surrounding the sites. The simultaneous increase in species richness and phylogenetic relatedness could highlight a process of biotic homogenization, typical of anthropized environments. Avian diversity was also significantly affected by the proportion of water bodies (e.g. increasing functional richness and dispersion, but decreasing functional evenness or redundancy) and other land use types (e.g. a negative association between species richness and the proportion of fields). The proportion of paddy fields affected each type of bird richness differently: an inverse U‐shape for forest generalists, negative for forest specialist species, and positive for open land specialists. When assessing the temporal stability of bird community composition, we found that such stability was significantly correlated with the proportion of grasslands, waterbodies, and urban landscapes. Specifically, avian communities surrounded by grasslands were characterized by higher species replacement over time. Additionally, very low or very high proportions of urban landscapes were associated with a relative instability of bird community composition. Our findings support the hypothesis that traditional farming systems represent valuable landscapes supporting avian diversity. However, the relative composition of land use types is crucial in shaping different taxonomic, functional, and phylogenetic diversity components in bird assemblages and their temporal stability.

Our previous study reported that three Paenibacillus strains promoted the bactericidal effect against Ralstonia pseudosolanacearum at low temperatures such as 25 °C for anaerobic soil disinfestation (ASD) (Pitti et al. 2024). The bactericidal effect against R. pseudosolanacearum was lower for ASD at 25 °C because of less production of Fe ²⁺ and Mn ²⁺ in the soil than ASD at 30 °C. However, the three Paenibacillus strains exhibited antagonistic activity against R. pseudosolanacearum at 25 °C, thereby suppressing the revived growth of the pathogen after ASD. Furthermore, this effect was possible because the three Paenibacillus strains were resistant to the bactericidal properties of Fe ²⁺ and Mn ²⁺ .

The storage of soil organic matter (SOM) is essential for maintaining and improving soil fertility. To obtain basic information about the status of SOM in paddy fields in Nepal under various ecological settings, we investigated the amount and turnover rate of stored carbon (C) in fractionated SOM in the surface layer. Soil samples from the top 15 cm plough layer were collected from 21 sites along an elevation gradient ranging from 78 to 2002 m a.s.l. in the central region of the country, and in eight sites in the lowland area in the eastern region to investigate regional differences in SOM status. SOM was fractionated into four components: (1) light fraction (LF, < 1.8 g cm⁻³), (2) heavy fraction (HF) consisting of physically stable aggregates, (3) oxidizable clay + silt fraction (OxF), and (4) nonoxidizable clay + silt fraction (NOxF) forming organo‐mineral complexes with fine‐textured minerals. The amounts of C in all fractions were determined, and the ∆¹⁴C values of selected samples were evaluated as indices of C turnover rate. The amount of stored C increased with elevation from 78 m (13.3 g kg⁻¹) to ca. 1700 m a.s.l. (28.0 g kg⁻¹). However, the total C content and C contents in LF, OxF, and NOxF exhibited decreasing trends from 1700 to ca. 2000 m a.s.l. (20.4 g kg⁻¹), probably because of decreased biomass production and decreased amorphous soil minerals at ca. 2000 m. The Δ¹⁴C values indicated that the C turnover rates in HF, OxF, and NOxF were faster at higher elevations (1221 m) than at lower elevations (78 m). These results suggest that mineralogy can have greater influence on C turnover than the climate difference in these mineral‐associated C fractions through SOM stabilisation. In lowland, the amounts and turnover rates of stored C in the soil fractions were larger and slower in the central region than in the eastern region, respectively, reflecting differences in soil texture and mineralogy. Multiple regression analysis showed that the amount of C was negatively influenced by the mean annual temperature in all fractions and positively influenced by amorphous Al minerals (Alo–Alp) in OxF and NOxF. The coefficients for temperature further suggest that the relative vulnerability of C to temperature increase is in the order of LF>HF>OxF>NOxF. These findings can serve as a basis for the maintenance and improvement of paddy soil fertility in Nepal for sustainable agricultural management.

The future increase of large-scale weather disasters resulting from the increased frequency of extreme weather events caused by climate change is a matter of concern. Predicting future flood damage through statistical analysis requires accurate modeling of the relationship between historical precipitation and flood damage. An analysis that considers precipitation as a time series may be appropriate for this purpose. Functional data analysis was applied to model the relationship between historical daily precipitation and daily flood damage for river basins in the Kanto and Koshin regions of Japan. Flood damage statistics from the national government and 1-km grid past precipitation data from the National Agriculture and Food Research Organization were used. The models obtained through the functional data analysis were more accurate than those derived from the simple linear regression without considering the time series of precipitation. The new models were also about four times more accurate in estimating the annual sum of flood damage, compared to the flood damage of each flood event. The accuracy of prediction was higher in recent years than in earlier years of the study period (1993–2020). The results showed that the influence of precipitation on flood damage was more apparent in recent years. This findings may imply that the progress of the river development project and the resulting improvement of the structures along the river have indirectly affected levels of flood damage associated with levels of precipitation.

Bacterial wilt disease caused by Trinickia (Burkholderia) caryophylli poses a significant threat to carnation cultivation in many regions around the world, often leading to severe damage once established. In this study, we developed a BIO-PCR method with high sensitivity and accuracy to detect and quantify T. caryophylli in soil, enabling precise evaluation of pathogen contamination levels. Single PCR (using a touchdown PCR program) was performed using the bacterial cells pre-incubated in a selective liquid medium as a template. The detection limit for this assay was 3 colony-forming-units (cfu) per g dry mass soil. By combining the most probable number (MPN) method and touchdown BIO-PCR, T. caryophylli can be quantified simultaneously. We validated this method in carnation cultivation fields and found a correlation between the degree of disease in each field and the measured density of the bacteria. This method will help develop and establish effective pest control techniques because it targets only live T. caryophylli in soil and can measure the density with high sensitivity and accuracy.

An important control on long-term soil organic carbon (SOC) storage is the adsorption of SOC by short-range-ordered (SRO) minerals. SRO are commonly quantified by measuring oxalate-extractable metals (Mox = Alox + ½ Feox), which many studies have shown to be positively correlated with SOC. It remains uncertain if this organo-mineral relationship is robust at the global scale, or if capturing regional differences is needed to maximize model accuracy. We used a global synthesis of Alox and Feox data to test their role in controlling SOC abundance across regions. We compiled 37,344 individual soil horizon measurements, with soil depth ranging between 0 and 200 cm, from 11,122 profiles. We used the Holdridge Life Zones, which are characterized by biotemperature, precipitation, and potential evapotranspiration, to group the soil profiles by their climatic conditions that also correlate with other important soil-forming factors. Based on linear mixed-effects models, we found a positive relationship between Mox and SOC across regions and depths, accounting for 49% of the SOC variation. This relationship is strongest in wetter regions and at depths between 20 and 100 cm. Across all environmental conditions, Alox is a stronger predictor of SOC than Feox. Our analysis suggests oxalate-extractable metals are good proxies for mineral-induced SOC protection at the global scale. However, our findings also indicate that the importance of organo-mineral interactions at the global scale varies with climatic conditions and depth. The underlying mechanisms need to be considered when incorporating these relationships as proxies for mineral sorption capacity into soil C models.

Metal toxicity depends on water chemistry characteristics such as hardness, pH, and dissolved organic matter. In this study, we investigated the effects of pH and hardness on the toxicity of Zn, Cu, Cd, and Ni to a freshwater diatom, Navicula pelliculosa, using a fluorescence microplate toxicity assay. The toxicity of Zn, Cu, and Cd to N. pelliculosa increased with increasing pH, but the relationship between pH and Ni toxicity was unclear. The toxicity of Zn, Cd, and Ni to N. pelliculosa decreased with increasing hardness, whereas Cu toxicity showed no clear relationship with hardness. These results were used to develop a bioavailability model to predict metal toxicities in natural water samples. We modelled the effect of pH by using a log-linear relationship between pH and toxicity with the biotic ligand model-type competition effects of Ca2+ and Mg2+. In addition, the metal bioavailability model was based on the free ion activity of metals considering the complexation of metals with dissolved organic matter. Model applicability was tested by comparing experimentally observed toxicities with predicted ones. The differences between predicted and observed EC50 and EC10 were within a factor of 2 for all metals in both synthetic and natural water samples except for Cu in natural water samples. Although the model was shown to be applicable for Zn, Cd, and Ni, it greatly underestimated the Cu toxicity in natural water samples, probably due to overestimation of the toxicity mitigation effect of dissolved organic matter.

Scientific literature documents and dictates the direction of knowledge and to recognize it we rely on techniques that compile information to point out the next paths in science. One of the promising techniques is bibliometric analysis, especially when applied to the recent and under developing approach, such as Life Cycle Assessment (LCA). Considering that the methodology still has limitations and can generate simplified results, we sought to map its characteristics in agricultural use, characterizing the evolution and trends of scientific research when using LCA in the agricultural sector over a period of twenty years, from 2002 to 2022. We used bibliometric analysis for a systematic review of the literature, using the Scopus repository as a database. To organize and process the data, we used the VOSviewer software. It was found 4943 articles, of which the publication peaks occurred in the last ten years, published predominantly by journals related to environmental sciences. This is due to the study characteristics being mainly focused on methodology improving. They have been produced mainly in the USA, Europe, and China, without emphasis on regions, such as Latin America, Africa, and Asia, although they largely concentrate global agricultural production and with relevant sustainability challenges. Through bibliometric analysis, three main clusters were formed: Impact Assessment (which deals with methodological development); Types of products (about raw materials for alternative energy production); and Substances and activities (particularly substances related to greenhouse gas emissions and activities related to animal and feed farming). We note that the methodology still neglects certain types of impacts, such as biodiversity loss, soil degradation, and partly the (eco) toxicity of pesticides. The methodology is still insufficient for decision-making, with a limited understanding of agricultural functions and difficulties in modeling certain persistent effects, although it continues to be strategically improved. Graphical abstract