Agrosystems, Geosciences & Environment最新文献

Delineation of management zones (MZ) based on soil mineral nitrogen (SMN) dynamics can enhance site-specific management, reduce nitrate leaching, and improve nutrient efficiency. We tested proximal sensing as an alternative to standard laboratory methods to capture the spatial variability of SMN, nitrate (NO₃⁻), and soil moisture (SM) and combined these data with topographic and remote sensing inputs to delineate MZ using data fusion and k-means clustering. Two conventionally managed fields with winter oilseed rape (Brassica napus L.) and winter barley (Hordeum vulgare L.) were chosen for Field-A and Field-B. Fresh soil samples were analyzed in the laboratory using KCl extraction, while global positioning system-labeled data from a proximal soil sensor (FarmLab) were accessed via cloud storage. FarmLab estimated NO₃⁻ and SMN were higher than laboratory results (p < 0.05), whereas SM showed no significant difference between the two methods. Bland–Altman analysis, which assesses the limit of agreement between methods to ensure consistency, revealed significant discrepancies in NO₃⁻ estimated by both methods, particularly in Field-B, with limits of agreement ranging from −17.40 to 29.66 mg kg⁻¹. Results of k-means clustering, a method for grouping data into similar categories, were evaluated using 11 feature sets, which combine data from multiple sources (laboratory and FarmLab data, satellites, and topographic data) to create a comprehensive dataset for analysis at different time points in autumn and spring. The results showed that the optimal clustering result varied depending on the field and date. Feature sets with topographic variables performed well in Field-A, while feature sets with remote sensing, topography, and FarmLab data improved MZ in Field-B. This study demonstrates how the FarmLab device can capture within-field SMN variability and examines the similarities and differences between both methods (laboratory and FarmLab). Despite discrepancies between methods, FarmLab showed the potential of integrating in-season NO₃⁻ and SMN data with topographic and remote sensing data to delineate MZ. This approach can be scaled up to farm and landscape scale, allowing farmers to leverage proximal and remote sensing data for in-season SMN monitoring, which enables efficient nutrient management and promotes sustainable farming practices with economic and environmental benefits.

This study assesses the energy use, ecological footprint, and greenhouse gas emissions from rice (Oryza sativa) production in Iran's Golestan province. The energy indices, greenhouse gas emissions, and ecological footprint in the rice paddies were calculated and analyzed after identifying the key inputs and outputs of these cropping systems. The energy use efficiency, energy productivity, specific energy, and net energy were measured to be 3.17, 0.19 kg MJ⁻¹, 5.30 MJ kg⁻¹, and 77,685.42 MJ ha⁻¹, respectively. Additionally, the analysis revealed that the global warming potential, net carbon, and carbon efficiency ratio were 4565.35 kg carbon dioxide equivalent (CO₂-eq) ha⁻¹, 1804.86 kg C ha⁻¹, and 2.46, respectively. The ecological footprint was measured to be 2.68 global hectares, which was more than the carrying capacity of each hectare of land allocated for crop cultivation. Hence, the environmental sustainability of rice production in the Golestan province was low. Alternatives such as rapeseed (Brassica napus) could reduce the environmental impact of rice farming in the province. Sustainability could be improved by reducing the reliance on electricity and nitrogen fertilizer that are produced using fossil fuels.

Micronutrient accumulation caused by low pH can lead to toxicity and have detrimental impacts on plant growth. In substrates with elevated pH, micronutrients become less available. In the first experiment, industrial hemp (Cannabis sativa L.) growth was less at pH 3.0 and 4.0 than when pH was ≥5.0. Root growth was also observed to be inhibited at low pH levels. Leaf tissue micronutrient concentrations were higher at the lowest pH level, but no toxic accumulation occurred. In experiment 2, root growth had less mass at the lowest pH (3.1) and highest pH level (7.1). In experiment 3, substrates with three target pHs (3.8, 4.8, and 6.5) as well as three micronutrient concentrations (1X, 2X, and 4X) were examined to determine the impact of pH on micronutrient accumulation in two cultivars Cherry Wine (CW) and Sweetened (SW). Foliar micronutrient concentrations were the greatest in plants grown with pH 3.8, and the lowest concentrations occurred in plants grown at pH 6.5. Susceptibility to toxicity from micronutrient accumulation in plant tissue varied by cultivar. SW plants grown at pH 3.8 and 4X micronutrients resulted in lower leaf micronutrient toxicity symptoms, while CW plants grown under the same conditions did not. These studies suggest that C. sativa does not accumulate micronutrients to toxic levels at low pH when micros are applied within normal growing ranges, but that growth is inhibited at substrate pH < 5.0.

Soil microbial communities have numerous soil ecological and physiological functions. However, knowledge is lacking on the interaction effects of no-till and cover crops (CC) practices on these soil health indicators. This study evaluated the effects of CC and tillage on soil microbial communities in a corn (Zea mays L.) system. The study was conducted for 2 consecutive years on plots allotted to three practices: (1) no-till and cover crop (NC), (2) conventional till and no cover crop (CN), and (3) no-till no cover crop (NN). A grass strip (G) was used as a reference, assuming it was subjected to the least disturbance. Surface (0–5 cm and 5–10 cm) soils were sampled over 2 years in April and October. Soil microbial biomass was measured using phospholipid fatty acid (PLFA) analysis. Seasonal variations indicated greater microbial biomass in fall than in spring. The G and NC significantly increased soil microbial biomass at both depths compared to CN and NN during fall 2021 sampling and numerically in fall 2020, where greater changes were observed at 0- to 5-cm depth. In fall 2021 sampling, NC practices had 65%–75% more total microbial biomass than CN and NN at both depths (p < 0.001), with total bacterial biomass 70% greater (p < 0.002) and total fungal biomass 75%–85% greater (p < 0.007). NC also showed 85% more actinomycetes biomass than CN at 5- to 10-cm depth (p < 0.05). The study concluded that soil microbial communities significantly improved after two CC seasons, with higher microbial biomass in fall compared to spring.

Banana wilt, caused by Fusarium oxysporum f. sp. cubense (Foc), is one of the most important diseases of banana (Musa L.) worldwide including Ethiopia. Foc is responsible for sever yield losses of up to 90% in commercial and local banana cultivars planted in southern Ethiopia. However, there is limited research information about its epidemiology, distribution, and relative importance of the disease in the country particularly in Gamo zone, southern Ethiopia, where banana is dominantly grown. Hence, this study was conducted to investigate and analyze the distribution, importance, and intensity of the disease and its association with biophysical factors. Fields surveys were conducted in three major banana-growing districts of Gamo zone in Arba Minch Zuria, Mirab Abaya, and Daramalo during the 2019/2020 cropping season in which a total of 90 fields were surveyed. The association of the disease incidence and severity with independent variables was analyzed using a logistic regression model in SAS under the GENMOD procedures. The results of the survey study indicated that Fusarium wilt was widely distributed all the three districts, regardless of agro-ecological factors. The highest mean incidence (47.37%) and severity (46.31%) values were recorded at Daramalo, while the lowest incidence (20.75%) and severity (28.63%) values were recorded from Arba Minch Zuria district. Variables such as banana cultivars, age of plantations, weeding practices, and disease management methods were significantly associated with the incidence and severity of the disease. The use of improved cultivars reduced disease incidence by 43.60% and the severity by 30.10%, compared to that of local ones. Regression analysis of the biophysical factors with Fusarium wilt severity implied the necessity of effective and feasible integrated management options to be developed against the disease and as well as the importance of awareness raising with all stakeholders regarding its distribution, importance, and possible management options such as the usage of Foc-resistant varieties in the study area.

The increasing availability of modern digital soil databases provides an opportunity to integrate these data into land surface models (LSMs), such as Noah-MP, for a more realistic representation of soil in estimating mass and energy flux at the land-atmosphere boundary. Noah-MP uses a default soil parameter table and a texturally uniform vertical soil profile to a depth of 2 m. Previous research has revised this soil parameter table, and 95% of the values investigated were suggested to be replaced using updated pedotransfer functions and new datasets. In addition to updated parameters, most LSMs do not consider vertical heterogeneity in soil texture despite the widespread distribution of these soils globally. This research assessed both (1) revisions to the soil parameter table and (2) vertical soil heterogeneity, including the presence of bedrock, on simulated water and energy fluxes. At three locations across Texas, plant-available water (PAW) estimates from Noah-MP simulations were evaluated using in situ measurements. Due to the lack of water and energy flux data, soil water content values simulated by Noah-MP were compared with the output from another well-established model, Root Zone Water Quality Model 2 (RZWQM2). Results showed improving representation of soil improved Nash–Sutcliff efficiency coefficient, model bias, and root mean square difference of Noah-MP simulated PAW when compared with measured PAW and RZWQM2 simulated PAW. A maximum difference in annual evapotranspiration of 150 mm between simulations was observed. These results demonstrate the need for better accounting of soil knowledge in LSMs for modeling mass and energy exchange at the land-atmosphere boundaries.

Information on the water retention curve (WRC) is required for the numerical simulation of water flow; however, its acquisition is time- and cost-consuming. In this study, the applicability of two-dimensional (2D) image of soil taken by a digital microscope to WRC measurement was assessed. For this purpose, four undisturbed soil samples were collected at surface (10–15 cm) and undersurface (22.5–27.5 cm) depths to acquire 2D images and measure WRCs from the conventional method, pressure plate method. To derive the WRCs, soil pore-size distribution in soil images was manually extracted using the following three methods: In Methods 1 and 2, the fuzzy region and the dark region, respectively, in the image were assumed to be a soil pore, and in Method 3, the pore boundary, being assumed to be a bright part, connected the lines and divided the region into pores. Method 3 had much large-size pores (>75 µm in radius) at deeper depths than at surface depths. This trend was the same as the pressure plate method. The WRCs in three methods were well fitted to the lognormal model. In all methods, the values of parameter of θ_e were larger at surface layers than at deeper depths, of which similar result was also seen in the pressure plate method. We discussed applicability of the parameters. It is effective to determine θ_e from the pressure plate method and the other parameters (ψ_m and σ) from the image-based method. From those analyses, it could be said to prove to obtain WRC from an image-based method.

Various works have quantitatively characterized the effects of environmental and management factors on Miscanthus $�\times$ giganteus Greef et Deu (mxg) yield and, therefore, anticipated land requirement per unit production. However, little work has addressed the effects of cutting height, which may significantly contribute to the difference between the standing aboveground biomass at harvest (i.e., biological yield) and harvested yield. This study quantitatively characterized the effect of cutting height using a replicated nitrogen trial of a 5-year-old mxg stand in southeast Iowa and related this information to observations of cutting height in nearby commercial fields. Nitrogen fertilizer did not significantly change the relationship of the stem segment mass to length, and overall, a 1-cm stem segment contributes 0.5% of the total stem biomass within the bottom 44 cm of the stem. This results in an average harvest loss of 15% of the aboveground standing biomass when cutting at 30 cm, typically seen in commercial mxg fields in eastern Iowa. Cutting height should be considered when accurately predicting commercial mxg harvest yields and changes in soil organic carbon in a commercial mxg agroecosystem.

Timely crop monitoring and yield prediction are essential in guiding management decision making. The aim of the study was to estimate the agronomic traits of paddy rice (Oryza sativa L.) using unmanned aerial vehicle (UAV)-multispectral imaging. A randomized complete block design field experiment with a split–split plot arrangement was set up in the Ruzizi plain, Democratic Republic of Congo (DRC). Spectral imaging data were collected at rice tillering and panicle initiation stages. Predictive analysis of rice agronomic traits was performed using linear and decision tree-based machine learning techniques. Paddy rice trait predictions were critically sensitive to the timing of image acquisition but not largely affected by the model. The most accurate predictions were made at rice panicle initiation stage, with R² values of 0.62, 0.65, and 0.75 for yield, aboveground biomass, and plant nitrogen (N) uptake, respectively. The visible atmospherically resistant index (VARI), modified chlorophyll absorption in reflective index, and ratio vegetation index, along with near infrared and green bands, played a critical role in predicting paddy rice N uptake and yield. The same spectral features associated with crop height and canopy data were essential for predicting paddy rice aboveground biomass. UAV-multispectral data were able to assess agricultural intensification strategies at field/landscape scale irrespective of soil types, watering regimes, and cultivars. Special consideration should be attributed to VARI, as it enables economical prediction of paddy rice traits. The UAV technologies are therefore reliable tools for monitoring rice production and can be applied in agricultural extension in the DRC.

Different cover crop (CC) species may respond differently to the projected climate change scenarios. A study was carried out in a controlled environmental chamber to evaluate early season growth and development of five CC species: cereal rye (Secale cereale L.), triticale (x Triticosecale Wittmack), winter wheat (Triticum aestivum L.), crimson clover (Trifolium incarnatum L.), and mustard (Brassica juncea). Treatments consisted of two levels of carbon dioxide (CO₂) (420 and 720 ppm), ultraviolet-B (UV-B) radiation (0 and 10 kJ m⁻² day⁻¹), and temperatures (29/21°C and 19/11°C day/night), and their combinations. Root, shoot, and physiological parameters were recorded, and a combined stress response index (CSRI) was derived. Results indicated that higher CO₂ (+CO₂) had a net positive effect on all five CC species, with CSRI values ranging from 1.0 to 5.1. Conversely, higher UV-B radiation (+UV) had a net negative impact, with CSRI values ranging from −2.9 to −7.6. The most favorable environment for all CC species was the combination of increased fall temperature and elevated CO₂ (+T+ CO₂). The negative impact of +UV was mitigated in an elevated CO₂ and a high temperature environment, mimicking fall temperatures in the US Midsouth. Among the CC species, mustard was the most responsive, with a 151% increase in root and shoot combined dry weight under the +T+ CO₂ treatment and an 86% decrease under the +UV treatment. Rye and triticale were the least impacted by the imposed climatic stressors. These results are of particular interest to the agricultural and environmental science community as they offer insights into developing and selecting CC species with adaptable and desirable morphological characteristics in anticipation of a changing climate.