Subplot Level Research Articles

Evidence that spatial scale and environment factors explain grassland community assembly in woodland-grassland ecotones.

How communities of living organisms assemble has long been a central question in ecology. The impact of habitat filtering and limiting similarity on plant community structures is well known, as both processes are influenced by individual responses to environmental fluctuations. Yet, the precise identifications and quantifications of the potential abiotic and biotic factors that shape community structures at a fine scale remains a challenge. Here, we applied null model approaches to assess the importance of habitat filtering and limiting similarity at two spatial scales. We used 63 natural vegetation plots, each measuring 5 × 5 m, with three nested subplots measuring 1 × 1 m, from the 2021 field survey, to examine the alpha diversity as well as beta diversity of plots and subplots. Linear mixed-effects models were employed to determine the impact of environmental variables on assembly rules. Our results demonstrate that habitat filtering is the dominant assembly rules at both the plot and subplot levels, although limiting similarity assumes stronger at the subplot level. Plot-level limiting similarity exhibited a positive association with fine-scale partitioning, suggesting that trait divergence originated from a combination of limiting similarity and spatial partitioning. Our findings also reveal that the community assembly varies more strongly with the mean annual temperature gradient than the mean annual precipitation. This investigation provides a pertinent illustration of non-random assembly rules from spatial scale and environmental factors in plant communities in the loess hilly region. It underscores the critical influence of spatial and environmental constraints in understanding the assembly of plant communities.

Role of integrated farming systems in land degradation control and plant diversity enhancement: A case of northeast Thailand

While agriculture provides livelihood to rural poor, it has also been the reason of land degradation and biodiversity loss over time. Although the Integrated Farming Systems (IFS) are promoted to address these issues, their adoption among local farmers has been slow. The adoption is contextual affected by local conditions and factors. To understand the role of IFS in land degradation control and plant diversity enhancement, the objective of this study was to examine the determinants of farmers’ adoption of IFS practices and investigate the role of IFS in land degradation control and plant diversity enhancement on farmlands. Combining socioeconomic survey, econometrics, and ecological assessment techniques, the data representing different types of land degradation and sub-plot level plant diversity were collected using both farmer interviews and field measurements for two groups farmers, those who have adopted some sort of IFS, and other who have not. When compared with non-IFS as base category, the results indicated that education and income positively influenced IFS adoption with a marginal effect of 0.035 and 0.001, respectively, contrasting with the negative impact of landholding size with a marginal effect of -0.081. IFS adoption led to reduced land degradation of various forms as perceived by the farmers, particularly reduced soil acidification with marginal effect of 2.52, soil erosion (2.071), and enhanced plant diversity as shown by the indices, Shannon-Weiner – 1.94, Inverse Simpson – 6.14, species richness – 10.65 for IFS plots compared to 1.03, 2.28, 5.06, respectively for the non-IFS plots. The findings offer valuable insights for agricultural extension authorities and local farmers, emphasizing the benefits of IFS practices. The study also underscores not only the environment gains of IFS practices but also holds the potential for long-term economic and ecological benefits.

Quantitative analysis of soil degradation in response to land use change in the Guinea savanna zone of Ghana

Soil degradation (SD) poses a significant challenge to tropical agricultural production systems due to its role in depleting soil nutrients. Recently, SD has threatened population growth by causing hunger and poverty among smallholder farmers in the Guinea savanna agro-ecological zone of Ghana. However, SD cannot be directly measured. Instead, assessment and modelling efforts rely on biological and physico-chemical indicators. This study sought to: (i) select a minimum dataset (MDS) using a multivariate statistical technique; (ii) identify SD processes, and evaluate the extent of degradation; and (iii) suggest strategies to monitor SD in the study area. Soil samples were collected using the Land Degradation Surveillance Framework (LDSF), specifically designed for sub-plot level (1000 m2) sampling. This scheme aligns well with the typical farm structure of smallholder farmers (<10 acres). Soil samples, taken from a depth of 0–20 cm, were analyzed to assess their physico-chemical properties. Principal component analysis (PCA) identified seven key SD processes: soil texture, pH, organic carbon (OC), total nitrogen (TN), available phosphorus (Av. P), base saturation (BS) and Effective Cation Exchange Capacity (ECEC). These identified processes lead to the loss of OC, deterioration of soil texture, reduction in soil nutrients, and increased soil acidity. There was a strong positive correlation (r = 0.70, p<0.05) between soil physical and chemical properties. PC1 explained 26.86% of the total variance, followed by PC2 at 15.31%, PC3 at 12.46%, and PC4 at 9.98%. Also, PC5, PC6, and PC7 accounted for 8.49%, 7.25% and 1.42% of the variance, respectively. The land-use systems we identified significantly affected the concentration of soil nutrients (p<0.001). In our computed Composite Soil Degradation Index (CSDI), CEC emerged as the most significant SD indicator with a weight of 0.328, followed by Si:CL ratio at 0.187. The results indicate that soil management activities should focus on increasing organic matter. Soil nutrient depletion primarily occurs through several processes. Land clearing and burning of organic materials during land preparation are primary causes. Furthermore, a total crop removal during harvest, inadequate manure application, and subsequent leaching and erosion aggravates SD, leading to nutrient-depleted soils. We conclude that the loss of nutrients, specifically CEC, OC, Ex. K and Av. P is the primary contributor to chemical SD. Therefore, implementing integrated soil fertility management (ISFM) strategies, such as applying organic (e.g., animal-based manures, green manure, crop residues, compost) and inorganic fertilizers (e.g., NPK + micronutrients) is crucial for increasing soil nutrient availability, enhancing SOM content, and promoting carbon sequestration in these farming systems. This study establishes a baseline for comparing and evaluating the effectiveness of land management interventions at the landscape level in northern Ghana. Consequently, soil nutrient management should be tailored to site-specific conditions, focusing on increasing the efficiency of N, P, K and micronutrient uptake by plants. Adopting such site-specific management strategies could significantly contribute to the sustainable intensification of agricultural production systems in the Guinea Savanna agro-ecological zone of Ghana.

Yield Test of Shade-Tolerant Maize Using the Intercropping Method of Maize-Soybean Under Coconut Trees

Maize is a light-dependent crop, but new varieties have been developed that can tolerate shade. This allows farmers to grow maize as an intercrop between coconut trees, making more efficient use of their land. Farmers can also intercrop with legume plants, which provide additional nitrogen through Rhizobium bacteria, leading to increased maize productivity. The aim of this study was to obtain the best intercropping pattern for maize and soybeans under coconut trees that results high yields in maize. The study was conducted in Padang Pariaman Regency of West Sumatra, at an altitude of approximately 150 meters, from February to July 2022. The experimental design was a Split Plot Design with Randomized Block Design, consisting of three main plot levels (planting time) and four subplot levels (planting model). The results of the study indicate that intercropping maize and soybeans under coconut trees has no effect on the weight of maize cobs, but there is a positive correlation between ear weight, cob length, cob diameter, number of rows per cob, and the weight of 1000 maize seeds.

Effect of Different Nitrogen and Potassium Levels on Yield and Nutrient Uptake of RNR 15048 during Summer Season in Southern Odisha

Background: The low glycemic rice variety RNR 15048 was introduced in Southern Odisha recently but the nitrogen and potassium requirement was not worked out for rabi / summer season. Methods: In this study, RNR 15048, a low glycemic index rice variety, was conducted in a split plot design with four main plots at 0, 50, 100 and 150 kg of nitrogen per hectare and three subplot levels at 0, 30 and 60 kg of potassium per hectare, each of which was reproduced three times. Result: A significant increase in yield, yield components and NPK uptake was noted with increase in nitrogen levels from 0 to 50, 100 and 150 kg/ha and potassium levels from 0 to 30 and 60 kg/ha.

Field path optimization to reduce headland and turning maneuvers at regional scales: automated detection of cultivation direction in the state of Brandenburg, Germany

Path planning for optimized field-work pattern is an important task within precision farming. The decision on a particular direction and path to cultivate and manage the field is complex and can significantly affect working time, energy consumption, soil compaction and yield. This study proposed a new method for automated detection of the current cultivation direction of several thousands of agricultural fields and compared the current cultivation direction with an optimized cultivation direction generated from a path planning algorithm. Airborne imagery from 2019 was analyzed using a modified Gabor filter. The identification takes place on a sub-plot level and can therefore detect small-scale differences in cultivation direction within fields. The method for identification of current cultivation direction had a high success rate of 87.5%. Fields with a high potential to save turning maneuvers and to reduce the area of headland were identified. From 3410 fields, a total of 58162 turning maneuvers and 507 ha headland were saved. This corresponds to 14.1% of all turning maneuvers and 7.6% of the total headland area for all analyzed fields in Brandenburg. A high optimization potential was demonstrated for field paths when efficient processing directions are taken into account. The method can be extended to the analysis of satellite imagery and thus offers the possibility of identifying current cultivation directions with a high spatial and temporal resolution. In future, this knowledge can be embedded within decision support systems for real-time optimization of field machinery path planning to support sustainable cropping practices.

Local‐ and landscape‐scale drivers of terrestrial herbaceous plant diversity along a tropical rainfall gradient in Western Ghats, India

Abstract In tropical forests, understory herbaceous angiosperms (herbs), which can comprise up to 40% of plant species richness, have received relatively little attention compared with trees, and their diversity patterns and drivers remain poorly understood. While tropical tree diversity has been shown to be driven primarily by water availability, we hypothesized that herb diversity may be equally or more limited by light availability. To test the importance of water and light in shaping herb diversity, we surveyed herb communities in 13 one‐ha plots along a rainfall gradient in a seasonally dry tropical forest landscape in India. In each plot, herbs were censused thrice during the year in 47–50 one‐m2 subplots. We examined drivers of herb diversity and ground cover at the landscape scale (across plots) and local scale (among subplots) using simple linear regression and linear mixed models, respectively. At the landscape scale, herb diversity and ground cover were negatively related to rainfall and soil moisture, in contrast with previous studies of tropical forest trees. This indicates that water was not limiting for herbs. Instead, light availability, which was negatively correlated with rainfall, appears to drive patterns of herb diversity and ground cover across the gradient. Herb diversity and ground cover were uniformly low across the gradient during the dry season, indicating that water limitation does have a seasonal impact. When water was abundant (rainy season), herbs took advantage of higher light availability at more open (lower‐rainfall) plots. Consistent with landscape‐scale patterns, herb richness at the subplot level was positively related to light availability, with its effect stronger at higher‐rainfall (more closed‐canopy) sites. Herb species richness declined with increasing subplot soil moisture in all sites and seasons, suggesting negative impacts of water logging and high moisture. Synthesis. Diversity of understory herbs is limited by light availability at both local and landscape scales in this region, with the impact of water limitation restricted to the dry season. Our study shows that tree diversity patterns cannot be assumed to hold for other plant lifeforms, and conservation and restoration efforts must consider unique strategies for different lifeforms.

Using Sentinel-1 and Sentinel-2 imagery for estimating cotton crop coefficient, height, and Leaf Area Index

In cotton, an optimal balance between vegetative and reproductive growth will lead to high yields and water-use efficiency. Remote sensing estimations of vegetation variables such as crop coefficient (Kc), Leaf Area Index (LAI), and crop height during plant development can improve irrigation management. Optical and Synthetic Aperture Radar (SAR) satellite imagery can be a useful data source since they provide synoptic cover at fixed time intervals. Furthermore, they can better capture the spatial variability in the field compared to point measurements. Since clouds limit optical observations at times, the combination with SAR can provide information during cloudy periods. This study utilized optical imagery acquired by Sentinel-2 and SAR imagery acquired by Sentinel-1 over cotton fields in Israel. The Sentinel-2-based vegetation indices that are best suited for cotton monitoring were identified, and the most robust Sentinel-2 models for Kc, LAI, and height estimation achieved R2 = 0.879, RMSE= 0.0645 (MERIS Terrestrial Chlorophyll Index, MTCI); R2 = 0.9535, RMSE= 0.8 (MTCI); and R2 = 0.8883, RMSE= 10 cm (Enhanced Vegetation Index, EVI), respectively. Additionally, a model based on the output of the SNAP Biophysical Processor LAI estimation algorithm was superior to the empirical LAI models of the best-performing vegetation indices (R2 =0.9717, RMSE=0.6). The most robust Sentinel-1 models were obtained by applying an innovative local incidence angle normalization method with R2 = 0.7913, RMSE= 0.0925; R2 = 0.6699, RMSE= 2.3; R2 = 0.6586, RMSE= 18 cm for the Kc, LAI, and height estimation, respectively. This work paves the way for future studies to design decision support systems for better irrigation management in cotton, even at the sub-plot level, by monitoring the heterogeneous development of the crop from space and adapting the irrigation accordingly to reach the target development at different growth stages during the season.

The Impact of Sowing Date on Soil Mineral Nitrogen Uptake Efficiency and Fertilizer N Uptake Efficiency for Winter Oilseed Rape (Brassica napus L.) in Ireland

Nitrogen (N) application on winter oilseed rape (Brassica napus L.; WOSR) in the mild Atlantic climate of Ireland is based on a soil N Index system, which does not take into account any variations in crop N demand prior to the main spring dressing of N fertilizer. This study tests whether UK- canopy management (CM) principles for oilseed rape N fertilization are applicable for oilseed rape grown in Ireland. The tested principles included (i) final N uptake of unfertilized crops will be equivalent to the soil mineral N (SMN) and N in the crop measured at the end of winter, i.e., soil N uptake efficiency (SNUpE) = 1; and (ii) the (apparent) N fertilizer N uptake efficiency, (FNUpE) is 0.6 (60%). Three years of field trials were carried out from 2017/18 to 2019/20 on different sites based on a split-plot design. Three sowing dates (SD): mid-August (SD1), End-August (SD2) and mid-September (SD3) were set as main plots and five N application strategies namely, CM standard, CM higher yield, CM low N rate, Fix225 and zero-N were set at subplot level. Results from unfertilized plots demonstrated that the ratio of final crop N uptake at harvest to the combined post-winter SMN + crop N was 1.13 and 1.14 on the two early sowing dates but 1.68 for the latest sowing. Additionally, SMN was not systematically impacted by SD, except in site-year-2. Instead, crop N content in spring had the biggest influence when calculating soil N supply for the season. More mineralization occurred through the growing season resulting in SNUpE of above 100% on all SDs. This additional available N (AAN) was quantified as 11 kgN·ha−1 on average for SD1 and SD2 and 38.6 kgN·ha−1 for SD3. FNUpE values were calculated closest to the economical optimum N rate (Nopt) and were 0.534, 0.574 and 0.486, respectively for SD1, SD2 and SD3. The Nopts at each SD were 157 kgN·ha−1 (SD1), 148 kgN·ha−1 (SD2) and 175 kgN·ha−1 (SD3) and the respective yields at each Nopt were 4.93 t ha−1, 4.90 t·ha−1 and 4.34 t·ha−1. This study shows the UK–CM principles were applicable in the mild Irish climate; however, values of SNUpE and FNUpE differ from one site-year-SD to another. Mid-August to early September sowing dates produced higher post-winter crop N content due to SMN uptake, and this results in a lower Nopt than the later SD. Sowing later led to a higher Nopt due to a combination of lower post-winter N uptake and lower FNUpE, although the effect of these factors was partially offset by a greater AAN. Measurements of SNS post-winter, an estimate of N mineralization during the growing season, together with a consideration of the sowing date helped determine the scope for reduction in fertilizer while achieving high yield and high FNUpE. Among different N rate strategies, CM standard and CM high yield were closest to the Nopt for having lower N rates at the maximum economical yield point.

Spatial scaling of gregarious host populations and nonlinearities in infectious disease transmission.

Research Highlight: Lunn, T. J., Peel, A. J., Eby, P., Brooks, R., Plowright, R. K., Kessler, M. K., & McCallum, H. (2021). Counterintuitive scaling between population abundance and local density: Implications for modelling transmission of infectious diseases in bat populations. Journal of Animal Ecology, https://doi.org/10.1111/1365-2656.13634. Quantifying the transmission of an infectious disease is often difficult and for natural animal systems it can be a major challenge. Animals move over time and space changing their degree of aggregation and rate of contact, which, in turn, affects the risk of infection and the onward spread of the pathogen. Capturing the fundamentals of these processes requires the identification of both the correct spatial scale at which the processes take place and what constitutes a meaningful host population unit. Lunn et al. collected data on the gregarious Pteropus (flying foxes) bats from roost sites in Australia and investigated whether total bat abundance at the roost level, the spatial scale commonly used to model pathogen spread in bat populations, was representative of bat measurements at the tree level, the scale at which pathogen transmission between bats most likely occurs. Their findings showed that bat population measurements at the sub-plot level were strong predictors for potential transmission at the tree scale, while roost-level measurements were less robust. This study suggests that bat abundance at roost is inadequate to capture the gregarious structure of bat populations and the fundamental processes of transmission at lower scale.

Reconciling design-based and model-based causal inferences for split-plot experiments

The split-plot design arose from agricultural science with experimental units, also known as the subplots, nested within groups known as the whole plots. It assigns different interventions at the whole-plot and subplot levels, respectively, providing a convenient way to accommodate hard-to-change factors. By design, subplots within the same whole plot receive the same level of the whole-plot intervention, and thereby induce a group structure on the final treatment assignments. A common strategy is to run an ordinary least squares (ols) regression of the outcome on the treatment indicators coupled with the robust standard errors clustered at the whole-plot level. It does not give consistent estimators for the treatment effects of interest when the whole-plot sizes vary. Another common strategy is to fit a linear mixed-effects model of the outcome with normal random effects and errors. It is a purely model-based approach and can be sensitive to violations of the parametric assumptions. In contrast, design-based inference assumes no outcome models and relies solely on the controllable randomization mechanism determined by the physical experiment. We first extend the existing design-based inference based on the Horvitz–Thompson estimator to the Hajek estimator, and establish the finite-population central limit theorem for both under split-plot randomization. We then reconcile the results with those under the model-based approach, and propose two regression strategies, namely (i) the weighted least squares (wls) fit of the unit-level data based on the inverse probability weighting and (ii) the ols fit of the aggregate data based on whole-plot total outcomes, to reproduce the Hajek and Horvitz–Thompson estimators, respectively. This, together with the asymptotic conservativeness of the corresponding cluster-robust covariances for estimating the true design-based covariances as we establish in the process, justifies the validity of the regression estimators for design-based inference. In light of the flexibility of regression formulation for covariate adjustment, we further extend the theory to the case with covariates, and demonstrate the efficiency gain by regression-based covariate adjustment via both asymptotic theory and simulation. Importantly, all our theories are either numeric or design-based, and hold regardless of how well the regression equations represent the true data generating process.

Artemisia sieberi dominated landscapes of Northeastern Iran host great diversity in lichen and annual plant species

In this study, we aim to evaluate the diversity of vascular plants and lichens coexisting in Artemisia sieberi dominated landscapes of northern Iran. Specifically, we wanted to unveil to what extent Artemisia plant cover and soil features affected species diversity and composition of annuals and lichens growing beneath them, and if annual plants and lichen species were associated at fine spatial scales. The study area was located in Artemisia steppes in North-Eastern Iran. Therefore, four localities dominated by Artemisia sieberi in Golestan National Park and Alagol wetland were selected. We quantified plant and lichen species cover in 5 plots in each location, including 25 subplots and five soil samples to characterize edaphic heterogeneity. Data were analyzed using GLM, NMDS, PERMANOVA, and Mantel Tests. We recorded 19 lichen species, three moss species, and 21 annual plant species in the four localities. Alagol and Alikhani had the most similar lichen compositions and Almeh showed great differences with other sites. However, Bagh and Almeh had the most similar and the most different annual species composition comparing the rest locations, respectively. Location explained 50% and 30% of the variance in lichens and annuals species composition, respectively. Soil chemical properties had no significant influences on lichens and annuals parameters except for Mg content in lichen evenness and clay content on total plant cover. We found a highly significant relationship between the lichen composition and annual plant species composition on fine scale (Mantel r: 0.13; p = 0.0003). The cover of Artemisia explained significant but tiny fractions of lichen species composition (1%) and annual plant species composition (2%) at subplot level. Artemisia steppes of northeastern Iran are not homogeneous habitats and shelter high diversity in lichen and annual plant species. Site location conditions may explain the differences among lichens and annuals compositions.

A Semi-Automatic Workflow to Extract Irregularly Aligned Plots and Sub-Plots: A Case Study on Lentil Breeding Populations

Plant breeding experiments typically contain a large number of plots, and obtaining phenotypic data is an integral part of most studies. Image-based plot-level measurements may not always produce adequate precision and will require sub-plot measurements. To perform image analysis on individual sub-plots, they must be segmented from plots, other sub-plots, and surrounding soil or vegetation. This study aims to introduce a semi-automatic workflow to segment irregularly aligned plots and sub-plots in breeding populations. Imagery from a replicated lentil diversity panel phenotyping experiment with 324 populations was used for this study. Image-based techniques using a convolution filter on an excess green index (ExG) were used to enhance and highlight plot rows and, thus, locate the plot center. Multi-threshold and watershed segmentation were then combined to separate plants, ground, and sub-plot within plots. Algorithms of local maxima and pixel resizing with surface tension parameters were used to detect the centers of sub-plots. A total of 3489 reference data points was collected on 30 random plots for accuracy assessment. It was found that all plots and sub-plots were successfully extracted with an overall plot extraction accuracy of 92%. Our methodology addressed some common issues related to plot segmentation, such as plot alignment and overlapping canopies in the field experiments. The ability to segment and extract phenometric information at the sub-plot level provides opportunities to improve the precision of image-based phenotypic measurements at field-scale.

Counterintuitive scaling between population abundance and local density: Implications for modelling transmission of infectious diseases in bat populations.

Models of host-pathogen interactions help to explain infection dynamics in wildlife populations and to predict and mitigate the risk of zoonotic spillover. Insights from models inherently depend on the way contacts between hosts are modelled, and crucially, how transmission scales with animal density. Bats are important reservoirs of zoonotic disease and are among the most gregarious of all mammals. Their population structures can be highly heterogeneous, underpinned by ecological processes across different scales, complicating assumptions regarding the nature of contacts and transmission. Although models commonly parameterise transmission using metrics of total abundance, whether this is an ecologically representative approximation of host-pathogen interactions is not routinely evaluated. We collected a 13-month dataset of tree-roosting Pteropus spp. from 2,522 spatially referenced trees across eight roosts to empirically evaluate the relationship between total roost abundance and tree-level measures of abundance and density-the scale most likely to be relevant for virus transmission. We also evaluate whether roost features at different scales (roost level, subplot level, tree level) are predictive of these local density dynamics. Roost-level features were not representative of tree-level abundance (bats per tree) or tree-level density (bats per m2 or m3 ), with roost-level models explaining minimal variation in tree-level measures. Total roost abundance itself was either not a significant predictor (tree-level 3D density) or only weakly predictive (tree-level abundance). This indicates that basic measures, such as total abundance of bats in a roost, may not provide adequate approximations for population dynamics at scales relevant for transmission, and that alternative measures are needed to compare transmission potential between roosts. From the best candidate models, the strongest predictor of local population structure was tree density within roosts, where roosts with low tree density had a higher abundance but lower density of bats (more spacing between bats) per tree. Together, these data highlight unpredictable and counterintuitive relationships between total abundance and local density. More nuanced modelling of transmission, spread and spillover from bats likely requires alternative approaches to integrating contact structure in host-pathogen models, rather than simply modifying the transmission function.

Lurking in the depth: Pond depth predicts microhabitat selection by Rhinella icterica (Anura: Bufonidae) tadpoles at two different sampling scales

Habitat selection has long been a central theme in ecology and has historically considered both physiological responses and ecological factors affecting species establishment. Investigating habitat selection patterns at different scales can provide important information on the relative roles of the environmental factors influencing the organisms’ abilities to use their surrounding habitat. This work aimed at investigating which environmental factors determine habitat selection by Rhinella icterica tadpoles, and also took the opportunity to investigate how the scale in which tadpoles and environmental data are sampled might influence the habitat selection results. A total of 2.240 tadpoles were counted in the whole sampling area, and while substrate cover and depth were the variables that better explained the abundance of tadpoles at the larger scale (plot level), depth and water turbidity better explained tadpoles’ abundance at the smaller scale (subplot level). The results suggest that avoiding predation by matching the background color is a likely process explaining tadpoles’ occupancy at both scales. Depth is known to influence tadpole habitat use in the tropics, and although its combination with turbidity and substrate cover varied between scales, our study suggests that sampling at different scales might not affect the inferred ecological processes driving habitat selection. This information might also be useful to predict tadpoles’ responses to micro-environmental perturbations and help in guiding the choice of parameters that should be taken into account when analyzing the effects of habitat degradation in Atlantic Forest amphibian populations.

Prediction error aggregation behaviour for remote sensing augmented forest inventory approaches

Abstract In this study we investigated the behaviour of aggregate prediction errors in a forest inventory augmented with multispectral Airborne Laser Scanning and airborne imagery. We compared an Area-Based Approach (ABA), Edge-tree corrected ABA (EABA) and Individual Tree Detection (ITD). The study used 109 large 30 × 30 m sample plots, which were divided into four 15 × 15 m subplots. Four different levels of aggregation were examined: all four subplots (quartet), two diagonal subplots (diagonal), two edge-adjacent subplots (adjacent) and subplots without aggregation. We noted that the errors at aggregated levels depend on the selected predictor variables, and therefore, this effect was studied by repeating the variable selection 200 times. At the subplot level, EABA provided the lowest mean of root mean square error ($\overline{\mathrm{RMSE}}$) values of 200 repetitions for total stem volume (EABA 21.1 percent, ABA 23.5 percent, ITD 26.2 percent). EABA also fared the best for diagonal and adjacent aggregation ($\overline{\mathrm{RMSE}}$: 17.6 percent, 17.4 percent), followed by ABA ($\overline{\mathrm{RMSE}}$: 19.3 percent, 18.2 percent) and ITD ($\overline{\mathrm{RMSE}}$: 21.8, 21.9 percent). Adjacent subplot errors of ABA were less correlated than errors of diagonal subplots, which resulted also in clearly lower RMSEs for adjacent subplots. This appears to result from edge tree effects, where omission and commission errors cancel for trees leaning from one subplot into the other. The best aggregate performance was achieved at the quartet level, as expected from fundamental properties of variance. ABA and EABA had similar RMSEs at the quartet level ($\overline{\mathrm{RMSE}}$ 15.5 and 15.3 percent), with poorer ITD performance ($\overline{\mathrm{RMSE}}$ 19.4 percent).

Yield and quality of soybean seeds inoculated with Bacillus subtilis strains

ABSTRACT The use of growth-promoting bacteria can increase the yield and quality of seeds of several annual crops, such as soybean. Based on that, the present study aimed to evaluate the effects of the inoculation of Bacillus subtilis strains on seed treatment, evaluating the yield and quality of seeds of two soybean cultivars. The experiment was conducted in a conventional system, in an Oxisol of clayey texture, in a crop under sprinkler irrigation. The experimental design was randomized blocks, arranged in split plots with four repetitions. Treatments were composed, in the plots, of two soybean cultivars: M7110 IPRO and Brasmax Desafio RR. Within the levels of subplots, the treatments consisted of inoculation and doses of inoculant: Bacillus subtilis Pant001 strain at doses of 0, 2, 3, 4 and 5 mL kg-1, and QST713 strain at the recommended dose of 2 mL kg-1. Inoculation with the strains Pant001 and QST713 increased the yield of both soybean cultivars tested, besides improving seed quality due to the increase in the concentration of total storage proteins, seedling emergence percentage and seed vigor by the accelerated aging test.

The Effects of Micronutrient and Organic Fertilizers on Yield and Growth Characteristics of Sunflower (Helianthus annuus L.)

Abstract To evaluate the effect of different micronutrients (Mn, Fe, Zn) and farmyard manure (FYM) on the vegetative, phenological, and achene yield components of sunflower a field experiment was conducted in Maragheh, North West of Iran during the growing season 2018–2019. The treatments were arranged in a split-plot design- with three replicates. The main plots assigned to two levels of FYM (0 and 20 t ha−1) and sub plots assigned to the recommended dose of three micronutrients (Mn, Fe, Zn). The results revealed significant improvements in the growth and yield of sunflower due to the main and interaction effects of farmyard manure and inorganic fertilizer application. The highest number of leaves and the highest leaf area was observed in a plant grown by FYM + Zn. However, there was no significant difference between micronutrients under inorganic condition (without FYM application). The highest leaf relative water content (RWC), plant height, internode distance, and chlorophyll was recorded for plants grown by Fe or Zn under FYM applied condition. Application of FYM and micronutrients reduced the number of days to flowering. However, the application of FYM significantly delayed plant maturity. Achene number per head, 100-achene weight, achene length, achene width, achene oil percent, achene yield, and harvest index significantly increased with the application of FYM and the best performance was in a plant grown by FYM + Zn and FYM + Fe. The percentage of hollow and empty achene was significantly reduced by the application of FYM. Combined applications of FYM and micronutrients has become an increasingly common practice.

Decoupling of nutrient cycles in a Eucalyptus woodland under elevated CO2

Abstract Elevated atmospheric [CO2] (eCO2) is currently altering nutrient cycling and availability in ecosystems worldwide. If the availabilities and turnover rates of macro‐ and micronutrients are differentially affected, then nutrient cycles may become out of sync (i.e., decoupled). We evaluated the impacts of 3 years of eCO2 (550 µmol CO2/mol) on the availability, stability and coupling of eleven essential macro and micronutrients in a mature, P‐limited Eucalyptus woodland from Eastern Australia (EucFACE). Despite increases in N and P availability in the first 18 months of study, nutrient availabilities and their stoichiometric ratios were unaffected by eCO2 across the study period (26–57 months of experimental duration). In contrast, the stability of nutrient availability increased under eCO2, which was concomitant with a reduction in the degree of biogeochemical coupling. Synthesis. We demonstrate that macro‐ and micro‐nutrient cycles can quickly become out of sync (i.e., decoupled) under eCO2 in a low‐nutrient Australian eucalypt woodland, despite the lack of consistent effects on nutrient availability; such decoupling of nutrient cycles may have unpredicted consequences in terms of ecosystem functioning under the widely assumed positive relationship between biogeochemical coupling and ecosystem functioning.

Disentangling the processes driving plant assemblages in mountain grasslands across spatial scales and environmental gradients

Abstract Habitat filtering and limiting similarity are well‐documented ecological assembly processes that hierarchically filter species across spatial scales, from a regional pool to local assemblages. However, information on the effects of fine‐scale spatial partitioning of species, working as an additional mechanism of coexistence, on community patterns is much scarcer. In this study, we quantified the importance of fine‐scale spatial partitioning, relative to habitat filtering and limiting similarity in structuring grassland communities in the western Swiss Alps. To do so, 298 vegetation plots (2 m × 2 m) each with five nested subplots (20 cm × 20 cm) were used for trait‐based assembly tests (i.e., comparisons with several alternative null expectations), examining the observed plot and subplot level α‐diversity (indicating habitat filtering and limiting similarity) and the among‐subplot β‐diversity of traits (indicating fine‐scale spatial partitioning). We further assessed variations in the detected signatures of these assembly processes along a set of environmental gradients. We found habitat filtering was the dominating assembly process at the plot level with diminished effect at the subplot level, whereas limiting similarity prevailed at the subplot level with weaker average effect at the plot level. Plot‐level limiting similarity was positively correlated with fine‐scale partitioning, suggesting that the trait divergence resulted from a combination of competitive exclusion between functionally similar species and environmental micro‐heterogeneities. Overall, signatures of assembly processes only marginally changed along environmental gradients, but the observed trends were more prominent at the plot than at the subplot scale. Synthesis. Our study emphasises the importance of considering multiple assembly processes and traits simultaneously across spatial scales and environmental gradients to understand the complex drivers of plant community composition.