Agrosystems, Geosciences & Environment最新文献

This study, conducted at Nkolbisson in the Centre region of Cameroon, aimed to identify soybean [Glycine max (L.) Merr.] varieties released by the Pan-African Soybean Variety Trials (PAT) in 2016 that are adapted to the acidic soil conditions (pH 4.10). A completely randomized block design with three replications was used for experimentation. Fertilizer was not applied to allow each variety to develop its potential under acidic conditions. Quantitative parameters, including plant height, crown diameter, fresh and dry weights of aboveground parts and roots, and total plant dry matter, were measured. The total nitrogen content of the plant during nodulation and after pod formation, as well as the production yields, was also assessed. Results showed that six varieties had plant heights greater than 67 cm (Pan 237, TGX 2010 3F, Pan 3, Maksoy 2N, Songda, and TGX 2001 12F), with the highest height recorded with TGX 2001 12F (85.66 ± 5.68 cm). The highest fresh weights of the aerial parts, ranging from 13.36 ± 3.97 to 44.26 ± 13.95 g, were observed in 19 soybean varieties. Fifteen soybean varieties showed the highest dry matter (95.04%–95.60%). The soybean varieties with the highest total nitrogen content at nodulation and after pod formation were Sentinel (6.00%) and TGX 2011-3F (4.88%), respectively. Nine varieties achieved yields above 2 t/ha, with TGX 2010 3F scoring the highest yield (2.76 t/ha). This study demonstrated the potential of some varieties of soybeans to thrive in acidic soils, offering a viable alternative for cultivation in areas with edaphic constraints. For that, further studies should be conducted on both the nutritional performance and the symbiotic interactions of these soybean varieties under acidic soil conditions.

A challenge for precisin agriculture is developing automated computer methods to accurately estimate fruit and seed yield in the standing crop. Soybean (Glycine max (L.) Merr.) pods are hard to distinguish from stems, which causes inaccurate predictions of yield from images of mature shoots. We developed image analysis tools to estimate morphological traits in the vertical canopy profile that are associated with high seed yield in soybeans. Using common image processing methods involving thresholding and particle analysis, higher circularity of the shoot convex hull vertical profile was found to correlate with high seed yield (number and grams per plant) in both an indeterminate cultivar (P49T80R) and in a determinate cultivar (Glenn). These soybean cultivars achieved high yields using different growth and production strategies. Glenn had a smaller shoot but exhibited a high pod density phenotype throughout its canopy (PT1, where PT stands for phenotype), while P49T80R achieved high yield through a combination of increased height and greater branching width, which compensated for lower pod density in its branches (PT2). We trained a deep machine learning model to automate shoot phenotyping using nearly 400 images of soybean shoots. The resulting model distinguished between PT1 and PT2 shoot images with 80% overall accuracy. The highest prediction accuracy in the model, 95%, was attained for shoots exhibiting the PT2 phenotype. Our work illustrates real-world application of image analysis technologies to identify high-yield trait analysis in field-grown soybeans and emphasizes the importance of including pod density positioning in machine learning training models.

Analyzing genotype-by-environment interaction (GEI) is crucial in multi-environment trials before introducing new barley varieties for cultivation under diverse regional conditions. This study evaluated novel barley genotypes across five Iranian locations during the 2022–2024 cropping seasons, assessing traits such as days to heading, maturity, grain-filling period, plant height, 1000-kernel weight, and grain yield. Combined analysis of variance revealed significant effects of genotype (G), environment (E), and GEI. Substantial phenotypic variation was observed across genotypes. The additive main effects and multiplicative interaction (AMMI) model partitioned GEI into six interaction principal component axes (IPCA). Based on IPCA1 scores and mean yield, genotypes G1, G2, G3, and G5 were identified as both high-yielding and stable. The AMMI-based stability metrics and best linear unbiased prediction (BLUP) identified genotypes G14 and G16 as the most stable, with broad adaptability across environments. These findings were reinforced by complementary metrics integrating AMMI and BLUP: weighted average of absolute scores and yield balance, and weighted average of absolute scores and yield scenarios. The genotype plus genotype-by-environment biplot analysis defined three mega-environments in Iran's barley-growing regions—Gonbad (north), Ahvaz, and Darab (south)—highlighting key targets for breeding efforts. Genotype G3 showed strong performance in the northern environment, while G4 was better adapted to southern conditions. Genotypes G14 and G16, due to their consistent performance across sites, are recommended for cultivation under variable or harsh climatic conditions. These insights support targeted selection and breeding of barley varieties adapted to Iran's diverse agroecological zones.

Despite increased funding for conversion from furrow to sprinkler irrigation to conserve water in semiarid agricultural watersheds, little is known about the effects of this conversion on soil health. To address this gap, soil health changes were monitored under two fields that underwent a furrow-to-sprinkler transition: one field at a university research station and the other a producer-managed field. Soil samples were collected at the top and bottom of each field in the first year and 1–4 years after the conversion. Soil health was assessed using the Soil Management Assessment Framework, a scoring tool for 10 soil health characteristics that indicate physical, biological, chemical, and nutrient soil health. Results showed that conversion to sprinkler irrigation marginally improved soil health, though salinity concerns emerged at the research field (an increase from ∼0.48 ds/m to ∼1.7 ds/m over 4 years). There was some limited evidence of homogenization of soil health during the transition to sprinkler irrigation. At the research field, soil organic carbon began as highly uneven from the top to the bottom of the field (1.54% and 1.08%, respectively), but became more evenly distributed (1.39% and 1.68%, respectively) after 5 years of sprinkler irrigation. Spatial homogenization should be viewed as a soil health improvement as it simplifies decisions relating to nutrient and irrigation management and helps farmers to predict yields across the field. Consequently, converting from furrow to sprinkler irrigation may help producers more easily manage homogenized fields due to on-site soil health improvements.

Sugar beet (Beta vulgaris) production in Japan faces major challenges from virus yellows (VY), caused by beet leaf yellowing virus (BLYV) and transmitted by aphids. Outbreaks have reduced sugar yields, and breeding for tolerant varieties has not been conducted in Japan. This study marks the first step toward developing VY-tolerant varieties by testing three hypotheses: (1) leaf yellowing can be induced by artificial inoculation, (2) tolerance varies among breeding materials, and (3) differences in tolerance to yellowing disease can be evaluated. From 2017 to 2020, four sugar beet materials were grown in inoculated and uninoculated plots and assessed using two methods: the yellowing index (YI), scored as 0–3, and the yellowing area (YA), calculated from digital images. Triple antibody sandwich–enzyme-linked immunosorbent assay confirmed that all BLYV-inoculated plants were infected and exhibited marked yellowing compared with uninoculated plants. Analysis of variance (ANOVA) applied to YI and YA data revealed that inoculation significantly influenced yellowing, symptoms progressed over time, and yellowing progression varied by material. Additionally, YI and YA were significantly correlated, with a Spearman correlation coefficient (rs) of 0.718. The significant correlations between YI or YA values and sugar yield loss (rs = 0.86–0.87) and root weight loss (rs = 0.80–0.83), but no significant correlation with Brix loss (rs = 0.32–0.46). These results validate the tested methods for evaluating BLYV tolerance and highlight the potential for breeding sugar beet varieties with enhanced tolerance. Moreover, the findings offer valuable insights for future VY tolerance breeding programs.

Shrink-swell soils swell when wetting and shrink when drying. This shrinkage creates cracks that may measure >10 cm in width and >1 m in depth when the soil is dry. Current numerical models are not able to accurately represent these dynamic pore characteristics and often soil shrink-swell processes are not taken into consideration at all. In order to incorporate these dynamic characteristics into numerical models, it is necessary to first quantify changes in pore characteristics—pore number, connectivity, size distribution, and tortuosity—that accompany changes in soil water content. X-ray computed tomography (CT) is a technology used to visualize the internal structure of an object and can be used to observe and quantify pore spaces in a soil sample. The goal of this project was to improve our understanding of dynamic porosity in shrink-swell soil by using X-ray CT scanning to quantify pore space characteristics in shrink-swell soils at two soil water contents: after wetting and oven-dried. Three intact soil cores were wetted, scanned using X-ray CT, then dried and scanned again. ImageJ and MATLAB software were used for image processing and analysis of structural changes within the cores. Our results show a statistically significant difference in pore network characteristics between wet and dried cores, with higher porosity, smaller pores, lower connectivity, and higher tortuosity values for the wet cores. These results have important implications for numerical simulations of soil water flow, which often disregard porosity dynamics due to shrinkage.

A recent study demonstrated that a blend of amendments improved both the physical and hydraulic properties of reclaimed mine soils more effectively than standard mine treatments, suggesting further research on its impact on plant growth. Additionally, there is currently no published research that has examined the potential of the random forest (RF) algorithm for predicting the aboveground yield of Chloris gayana (Rhodes grass) and Digitaria eriantha (Smutsfinger grass) grown in reclaimed mine soils. To address this, a field trial of 36 bins consisting of nine treatments and four replications each was conducted in a randomized block design at the experimental farm of the University of Pretoria. The results showed that the dry matter yield, leaf area index, and leaf water potential were all significantly (p < 0.05) affected by the treatment. The blend of amendments increased aboveground dry matter yield by 70%–150% and leaf area index by 60%–95%. These improvements significantly enhanced productivity and, consequently, the carrying capacity of the rehabilitated land compared to the standard mine treatment of liming and fertilization. The most important wavelengths for predicting aboveground yield were located in the visible (400–700 nm) region of the electromagnetic spectrum, yielding an r² of 0.90, mean absolute error of 0.183 t ha⁻¹ and root mean square error of 0.255 t ha⁻¹. These findings demonstrate that a blend of amendments can enhance the production potential of these grasses by improving soil nutrient availability. However, the longevity of these effects needs to be verified through long-term studies. The results also indicate that RF algorithm can accurately predict aboveground yield of C. gayana and D. eriantha accurately based on changes in the plant canopy spectral signature.

High-resolution geospatial soil moisture measurements are needed to inform hydrological modeling and to guide water management in agriculture, especially in highly heterogeneous systems such as micro-irrigated orchards. In this research, we used a Portable L-band Radiometer (PoLRa) to map very high-resolution (<2 m) soil surface moisture in micro-irrigated orchards in Southern California. Almond (Prunus dulcis Mill.), olive (Olea europaea L.), and orange (Citrus × sinensis Osbeck) orchards grown on Monserate sandy-loam soil were surveyed from the Summer through the Fall of 2022. The sensor was mounted on an all-terrain vehicle and paired with a centimeter-level positioning system. PoLRa measurements were compared with ground-truth volumetric water content determined from soil cores collected at the study sites. The sensor data were calibrated to estimate surface soil moisture with an analysis of covariance linear regression approach. The lowest estimation errors were observed in the almond orchard, which had flat soil and no canopy interference. There, the root mean square error of the tested linear models ranged between 3.9% and 4.1%. Over the entire dataset, the root mean square error was 5.9%. This new sensor technology may be a means for improving understanding of water dynamics in complex and heterogeneous agricultural systems. Nevertheless, further research is needed to refine calibration models and address environmental variability and its effects on the sensor's measurements.

Drought is an abiotic stress that significantly threatens global food security by reducing crop yields. This study aimed to evaluate the drought tolerance of barley (Hordeum vulgare L.) using polyethylene glycol 6000 (PEG-6000). A hydroponic experiment was conducted to assess 24 barley genotypes with potential drought resilience during the seedling stage. These genotypes were subjected to four levels of drought stress, applied using PEG-6000 at concentrations of 0%, 5%, 10%, and 20%. The experiment followed a randomized factorial design with two replications. Two-way analysis of variance revealed significant effects of genotype (p < 0.001) and PEG-induced drought stress levels (p < 0.001) on most measured traits, except root number, shoot dry weight, and root dry weight. The interaction between genotype and stress level was also significant (p < 0.001), except for shoot length, root number, chlorophyll content readings, shoot dry weight, and shoot water content. Four barley genotypes—G16, G24, G13, and G17—exhibited the highest drought tolerance. Overall, as the PEG concentrations increased, there was a decline in germination percentage, vigor index, root and shoot length, and both new and dry weight. The identified drought-tolerant genotypes show promise for cultivation in water-limited environments, as they can maintain better growth performance under drought stress. In the future, efforts should focus on field validation, genetic and molecular research, breeding programs, and collaborative initiatives to enhance drought resilience strategies under real-world conditions.

Screening different germplasm and landrace collections of high-yielding forage crops improves feed availability and quality, addressing deficits in livestock production. The study was conducted to evaluate and identify high-yielding and stable Guinea grass (Panicum maximum Jacq.) genotypes for herbage yield, nutritional quality, and agronomic traits. Ten Guinea grass genotypes and one standard check variety (Degun guziya) were tested in randomized complete block design with three replications, using 5.4 m² plot area and 0.3 m row spacing. Seeds were sown at 10 kg/ha, with fertilizer application rates of 100 kg/ha NPS and 50 kg/ha urea. Agronomic traits, yields, and stability were measured and analyzed. Analysis of variance showed significant (p < 0.01) variations among genotypes, environments, and years for the number of leaves per plant (NLPP), herbage dry matter yield (HDMY), and seed yield. Genotype by environment (G × E) interactions significantly influenced NTPP and seed yield. Additionally, NLPP, leaf to steam ratio, HDMY, and seed yield were affected by genotype × environment × year interactions. Additive main effect and multiplicative interaction analysis indicated significant (p < 0.001) effects of genotype, environment, and G × E interaction, with genotype contributing 42.63% of the total variation, followed by environment (33.84%) and G × E interaction (23.53%). The maximum mean HDMY was recorded for genotype NG-0105 (15.01 t/ha), followed by NG-0104 (13.97 t/ha), across all environments. Stability analysis confirmed that NG-0105 and NG-0104 were the most stable genotypes, exhibiting yield advantages of 40.67 and 30.93%, respectively, over the standard check. Therefore, these genotypes are recommended for cultivation and release as new varieties in the tested environments.