Soybean Cropland Research Articles

Mapping annual 10-m soybean cropland with spatiotemporal sample migration

China, as the world’s biggest soybean importer and fourth-largest producer, needs accurate mapping of its planting areas for global food supply stability. The challenge lies in gathering and collating ground survey data for different crops. We proposed a spatiotemporal migration method leveraging vegetation indices’ temporal characteristics. This method uses a feature space of six integrals from the crops’ phenological curves and a concavity-convexity index to distinguish soybean and non-soybean samples in cropland. Using a limited number of actual samples and our method, we extracted features from optical time-series images throughout the soybean growing season. The cloud and rain-affected data were supplemented with SAR data. We then used the random forest algorithm for classification. Consequently, we developed the 10-meter resolution ChinaSoybean10 maps for the ten primary soybean-producing provinces from 2019 to 2022. The map showed an overall accuracy of about 93%, aligning significantly with the statistical yearbook data, confirming its reliability. This research aids soybean growth monitoring, yield estimation, strategy development, resource management, and food scarcity mitigation, and promotes sustainable agriculture.

Mapping the Potential Development of Soybenas Crops Based on Growing Degree Days

The main factors affecting plant growth and yield are soil, climate, and plants. The influence of clime is more than soil on plant growth under certain conditions. The evidence is that some cover vegetation growth conditions are much different even though they grow on the same land. In each distinct place, the harvest time of one type of plant is not the same. One crucial factor that affects this is the daily growth rate or the degree of Growing Degree Days (GDD). Know parameters such as soil type, slope, land use, rainfall, and GDD temperature by using geographic information systems in determining the suitability of agricultural commodity development areas. The study aims to determine the suitability of soybean cropland in West Sumatra based on GDD value. Conducting land suitability analysis using the GDD method through several stages, namely, the classification of soybean land suitability parameters, making a map of soybean plant land suitability in physical characteristics, classifying the GDD value of soybean plants for the West Sumatra region, and making a soybean cropland suitability map based on GDD value. The potential land for soybean cultivation in West Sumatra based on land suitability class, physical characteristics, and GDD value is 555,791.66 hectares in class S1 (very suitable) and an area of 2,313,796.44 hectares in class S2 (appropriate enough). The most optimal planting time for soybean cultivation is in August. Lima Puluh Kota Regency, Pasaman Regency, Sijunjung Regency, and Solok Regency have land suitable for soybean cultivation with more than 200 thousand hectares of areas and are in areas with growing degree days suitable for soybean growth.

The responses of CO2 emission to nitrogen application and earthworm addition in the soybean cropland.

The effects of nitrogen application or earthworms on soil respiration in the Huang-Huai-Hai Plain of China have received increasing attention. However, the response of soil carbon dioxide (CO2) emission to nitrogen application and earthworm addition is still unclear. A field experiment with nitrogen application frequency and earthworm addition was conducted in the Huang-Huai-Hai Plain. Results showed nitrogen application frequency had a significant effect on soil respiration, but neither earthworms nor their interaction with nitrogen application frequency were significant. Low-frequency nitrogen application (NL) significantly increased soil respiration by 25%, while high-frequency nitrogen application (NH), earthworm addition (E), earthworm and high-frequency nitrogen application (E*NH), and earthworm and low-frequency nitrogen application (E*NL) also increased soil respiration by 21%, 21%, 12%, and 11%, respectively. The main reason for the rise in soil respiration was alterations in the bacterial richness and keystone taxa (Myxococcales). The NH resulted in higher soil nitrogen levels compared to NL, but NL had the highest bacterial richness. The abundance of Corynebacteriales and Gammaproteobacteria were positively connected with the CO2 emissions, while Myxococcales, Thermoleophilia, and Verrucomicrobia were negatively correlated. Our findings indicate the ecological importance of bacterial communities in regulating the carbon cycle in the Huang-Huai-Hai Plain.

Soybean (Glycine max) Cropland Suitability Analysis in Subtropical Desert Climate through GIS-Based Multicriteria Analysis and Sentinel-2 Multispectral Imaging

Soybean (Glycine max) is a protein-rich oilseed crop that is extensively used for cooking oil and poultry feed and faces significant challenges due to adverse global climatic conditions aggravated by the ongoing climate crisis. In response to this critical issue, this study was initiated to assess suitable zones for soybean cultivation, aiming to facilitate informed land use decisions within the semi-arid terrestrial ecosystem. Through the utilization of geostatistical interpolation, data layers encompassing soil, irrigation water, land use and land cover, topographic features, and climate information were generated and overlaid based on criterion weightage derived from the Analytic Hierarchy Process. The accuracy of land use and land cover was rigorously evaluated, yielding a 70% overall accuracy and a Kappa (K) value of 0.61, signifying an acceptable level of precision. Validation through the Receiver Operating Characteristic curve for soybean crop suitability demonstrated a highly satisfactory area under the curve of 0.738. The study estimates that out of 172,618.66 hectares, approximately 47.46% of the land is highly suitable (S1) for soybean production, followed by 21.36% moderately suitable (S2), 11.91% marginally suitable (S3), 7.00% currently not suitable (N1), and 12.28% permanently not suitable (N2). Conclusively, the findings suggest that the study area exhibits conducive climatic conditions, optimal soil health, and access to quality irrigation water, all of which have the potential to support soybean crops with improved agronomic practices. This investigation offers valuable insights to both farmers and policymakers concerning irrigation water quality, agricultural productivity, and soil degradation.

Severe simplification of the structure of dung beetle assemblages in neotropical soybean croplands regardless of the native vegetation domain

The conversion of native vegetation into agricultural areas is arguably the key driver of biodiversity declines globally. We assessed the responses of dung beetle assemblages (Coleoptera: Scarabaeidae: Scarabaeinae) to the effects of native vegetation conversion into soybean monoculture in different vegetation domains (Amazonian forest, scrubland savannahs and transitional vegetation) in the world’s largest tropical soybean (Glycine max L.) agricultural frontier. We also assessed the effects of landscape variables (amount of native habitat and amount of pasture) and land use history (time of land use change including soybean cultivation and cattle pastures). We sampled dung beetles at 100 study landscapes throughout the state of Mato Grosso, Brazil, within a region encompassing Amazonian forests, the drier Cerrado savannahs, and transitional vegetation domain between these biomes. In total, we recorded 71,043 individuals of 165 species, with 39,090 individuals (155 species) sampled in native vegetation and 31,953 individuals (75 species) in soybean monoculture areas, 30 species of which could persist in croplands. We found differences in species composition, and lower species richness, overall abundance and biomass in monoculture areas, compared to adjacent native habitat. Time since land-use change into pasture was the main determinant of species richness and species compositional similarity in monocultures, while none of the landscape and land use history variables were informative in explaining overall abundance. We provide clear evidence that the conversion of native vegetation into soybean monoculture negatively affects dung beetle assemblages. Furthermore, croplands harboured their own unique dung beetle assemblages containing species typical of open-habitats and pasturelands, which are likely tracking the historical advance of the agricultural frontier. However, species typical of forest environments were most detrimentally affected and were largely missing in cropland areas.

The Relationship of Environmental Factors and the Cropland Suitability Levels for Soybean Cultivation Determined by Machine Learning

The relationship between cropland suitability and the surrounding environmental factors has an important role in understanding and adjusting agricultural land management systems to natural cropland suitability. In this study, the relationship between soybean cropland suitability, determined by a novel machine learningbased approach, and three major environmental factors in continental Croatia was evaluated. These constituted of two major land cover classes (forests and urban areas), utilized soybean growth seasons per agricultural parcels during a 2017–2020 study period and soil types. The sensitivity analysis in geographic information system (GIS) using a raster overlay method, along with auxiliary spatial processing, was performed. The proximity of soybean agricultural parcels to forests showed a high correlation with suitability values, indicating a potential benefit of implementing agroforestry in land management plans. A notable amount of suitable agricultural parcels for soybean cultivation, which were previously not utilized for soybean cultivation was observed. A disregard of crop rotations was also noted, with the same soybean parcels within the study period in three and four years. This analysis showed considerable potential in understanding the effects of environmental factors on cropland suitability values, leading to more efficient land management policies and future suitability studies.

Higher than expected N2O emissions from soybean crops in the Pampas Region of Argentina: Estimates from DayCent simulations and field measurements

In developing countries, agriculture generally represents a large fraction of GHG emissions reported in National Inventories, and emissions are typically estimated using Tier 1 IPCC guidelines. However, field data and locally adapted simulation models can improve the accuracy of IPCC estimations. In this study we aimed to quantify anthropogenic N2O emissions from croplands of Argentina through field measurements, model simulations and IPCC guidelines. We measured N2O emissions and their controlling factors in 62 plots of the Pampas Region with corn, soybean and wheat/soybean crops and in unmanaged grasslands. We accounted for gross emissions from crops and background emissions from unmanaged grasslands to calculate net anthropogenic emissions from crops as the difference between them. We calibrated and evaluated the DayCent model and then simulated different weather and management scenarios. Finally, we applied IPCC guidelines to estimate anthropogenic N2O emissions at the same plots. The DayCent model accurately simulated annual N2O emission for all crops as compared to measured data (RMSE = 1.4 g N ha−1 day−1). Measured and simulated emissions in soybean crops were higher than in corn and wheat/soybean crops. Gross N2O emissions ranged from 1.4 to 5.1 kg N ha−1 yr−1 for current environmental (soil and weather) and management (crops and fertilizer doses) conditions. Background emissions ranged between 1.1 and 1.3 kg N ha−1 yr−1, and therefore net anthropogenic emissions ranged from 0.3 to 4.0 kg N ha−1 yr−1. IPCC Tier 1 emission factors underestimated N2O releases from soybean, that were on average 4.87 times greater when estimated with DayCent and observations (0.53 vs 2.47 and 2.69 kg N ha−1 yr−1, respectively). On the contrary, IPCC estimates for corn and wheat/soybean crops were similar to modeled and measured values. Our results suggest that N2O emissions from the vast 15 million ha of soybean croplands in the Pampas Region may be substantially underestimated.

Soil organic carbon cycling in response to simulated soil moisture variation under field conditions

The combination of extended dry periods and high intensity rainfall, common in the southeastern US, leads to greater variability in soil moisture and consequently increases uncertainty to microbial processes pertinent to soil carbon (C) mineralization. However, field-based findings on soil moisture sensitivity to soil C cycling are very limited. Therefore, a field experiment was conducted in 2018 and 2019 on a soybean (Glycine max L.) cropland in the southeastern US with three soil moisture treatments: drought (simulated using rainout-shelter from June to October in each year), rainfed (natural precipitation), and irrigated (irrigation and precipitation). Soil respiration was measured weekly from May to November in both years. Soil samples were collected multiple times each year from 0–5, 5–15, and 15–30 cm depths to determine microbial biomass C (MBC), extractable organic C (EOC), hydrolytic enzyme activities, and fungal abundance. The cumulative respiration under drought compared to other treatments was lower by 32% to 33% in 2018 and 38% to 45% in 2019. Increased MBC, EOC, and fungal abundance were observed under drought than other treatments. Specific enzyme activity indicated fewer metabolically active microbes under drought treatment compared to rainfed and irrigated treatments. Also, maintenance of enzyme pool was observed under drought condition. These results provide critical insights on microbial metabolism in response to soil moisture variation and how that influences different pools of soil C under field conditions.

Simulated acid rain offset a warming-induced increase in soil respiration but did not impact the temperature sensitivity of soil respiration in a cropland

Investigating the effects of warming and simulated acid rain on soil respiration and its temperature sensitivity is crucial in predicting the potential responses of soil C cycles to future climate warming. A three-year field experiment with six treatments (CK, WA, CK-4.0, WA-4.0, CK-2.0 and WA-2.0) of warming (coded as WA) and three simulated acid rain levels (coded as CK, pH 4.0 and pH 2.0) was performed in an annual winter wheat–soybean cropland to study the comprehensive effects of warming and simulated acid rain on soil respiration and its temperature sensitivity. The results indicated that warming significantly (P < 0.001) increased soil respiration under no (CK) or low acid rain application (pH 4.0), while the warming effects on soil respiration were not significant under intensive acid rain conditions (pH 2.0). The annual mean soil respiration was 2.79 ± 0.02, 3.02 ± 0.01, 2.58 ± 0.07, 2.79 ± 0.03, 2.54 ± 0.04 and 2.50 ± 0.03 μmol m−2 s−1 in the CK, WA, CK-4.0, WA-4.0, CK-2.0 and WA-2.0 plots, respectively. Warming induced an 8.0% increase in soil respiration compared with the CK. Intensive simulated acid rain (pH 2.0) offset the warming effects on soil respiration. A total of 36.9–55.5% of the seasonal variation in soil respiration was explained by soil temperature, while the moisture effects on soil respiration were not significant (P > 0.05). Warming induced a significant (P < 0.001) decrease in the temperature sensitivity (Q10) of soil respiration. Simulated acid rain did not significantly (P > 0.05) impact the Q10 of soil respiration.

A talajlégzés térbeli heterogenitásának vizsgálata segédváltozók segítségével

Soil respiration is a significant contributor to the global emissions of CO2 and is governed by many soil factors. Reliable estimates of CO2 emission on different scales (e.g., field, regional level) are hard to obtain due to the expressed spatial and temporal variability of the CO2 flux. This study aims to investigate the spatial variability of CO2 flux and soil properties in soybean cropland on Fluvisols (Croatia). The field measurements and soil samples were taken in a regular sampling grid (2 × 2 m) with 44 points in total and the spatial variability was assessed using the kriging and cokriging techniques. The soil CO2 flux showed relatively high spatial heterogeneity, ranging from 0.03 mg/m2s to 0.40 mg/m2s. The soil organic matter content (SOM), soil water content (SWC), and soil temperature (ST) had the lower variability ranging from 2.09% to 2.52%, from 27.7% to 46.8%, and from 13.7 °C to 18.2 °C, respectively. The spatial dependence was high for CO2 flux and ST, moderate for SOM, and low for SWC. The incorporation of the auxiliary variables increased the precision of the estimations for CO2 flux, SOM, and SWC. Kriging was the most accurate method for the spatial prediction of ST. The SWC was associated as the most important factor of the CO2 fluxes, indicated by their significant negative correlation, and the highest increase of the prediction precision during spatial modeling. However, more robust co-variates should be incorporated in future models to further increase the precision.

Modeling Gross Primary Production of Midwestern US Maize and Soybean Croplands with Satellite and Gridded Weather Data

Gross primary production (GPP) is a useful metric for determining trends in the terrestrial carbon cycle. To estimate daily GPP, the cloud-adjusted light use efficiency model (LUEc) was developed by adapting a light use efficiency (LUE, ε) model to include in situ meteorological data and biophysical parameters. The LUEc uses four scalars to quantify the impacts of temperature, water stress, and phenology on ε. This study continues the original investigation in using the LUEc, originally limited to three AmeriFlux sites (US-Ne1, US-Ne2, and US-Ne3) by applying gridded meteorological data sets and remotely sensed green leaf area index (gLAI) to estimate daily GPP over a larger spatial extent. This was achieved by including data from four additional AmeriFlux locations in the U.S. Corn Belt for a total of seven locations. Results show an increase in error (RMSE = 3.5 g C m−2 d−1) over the original study in which in situ data were used (RMSE = 2.6 g C m−2 d−1). This is attributed to poor representation of gridded weather inputs (vapor pressure and incoming solar radiation) and application of gLAI algorithms to sites in Iowa, Minnesota, and Illinois, calibrated using data from Nebraska sites only, as well as uncertainty due to climatic variation. Despite these constraints, the study showed good correlation between measured and LUEc-modeled GPP (R2 = 0.80 and RMSE of 3.5 g C m−2 d−1). The decrease in model accuracy is somewhat offset by the ability to function with gridded weather datasets and remotely sensed biophysical data. The level of acceptable error is dependent upon the scope and objectives of the research at hand; nevertheless, the approach holds promise in developing regional daily estimates of GPP.

Responses of CO2 and N2O emissions from soil-plant systems to simulated warming and acid rain in cropland

Climate warming is anticipated to change the terrestrial carbon/nitrogen cycle through its impact on the fluxes of greenhouse gases such as CO2 and N2O. This study investigated the effect of diurnal warming and acid rain on CO2 and N2O emissions from soil-plant systems in a winter wheat–soybean rotation cropland. Field rotational experiments of winter wheat and soybean were conducted by simulating diurnal warming and acid rain. Manipulated experiments included the control (CK), diurnal warming (T, + 2 °C), acid rain (AR, pH = 2.5), and the combined treatment (TAR, + 2 °C and acid rain). CO2 and N2O fluxes from soil-plant systems were measured using a static chamber-gas technique. The results showed that in the winter wheat and soybean growing seasons, compared with CK treatment, T, AR, and TAR treatments did not change the accumulative amount of CO2 emission (AAC) across the full growth period (p ˃ 0.05), although T treatment significantly increased soil AAC (p < 0.05) at the grain filling-maturity stage. On the contrary, T and AR treatments significantly increased the accumulative amount of N2O emission (AAN) in winter wheat and soybean croplands (p < 0.05), which was attributed to leaf nitrate reductase activity, total biomass, and soil NO3−–N content. In addition, there was a significant interaction between warming and acid rain on N2O flux. The AAN from the winter wheat cropland under treatments was in the order: TAR ˃ AR ˃ T ˃ CK. Our findings suggest that diurnal warming and acid rain had no significant effect on CO2 emissions from soil-plant systems, but significantly increase N2O emissions in winter wheat and soybean growing seasons. Moreover, diurnal warming would strengthen the positive effect of acid rain on N2O emissions from soil-plant systems in winter wheat farmland.

Biomass Production with Conservation Practices for Two Iowa Watersheds

AbstractHydrologic modeling was used to estimate potential changes in nutrients, suspended sediment, and streamflow in various biomass production scenarios with conservation practices under different landscape designs. Two major corn and soybean croplands were selected for study: the South Fork of the Iowa River watershed and the headwater of the Raccoon River watershed. A physically based model, the Soil and Water Assessment Tool, was used to simulate hydrology and water quality under different scenarios with conservation practices and biomass production. Scenarios are based on conservation practices and biomass production; riparian buffer (RB), saturated buffer, and grassed waterways; various stover harvest rates of 30%, 45%, and 70% with and without winter cover crops; and conversion of marginal land to switchgrass. Conservation practices and landscape design with different biomass feedstocks were shown to significantly improve water quality while supporting sustainable biomass production. Model results for nitrogen, phosphorus, and suspended sediments were analyzed temporally at spatial scales that varied from hydrologic response units to the entire watershed. With conservation practices, water quality could potentially improve by reducing nitrogen loads by up to 20%–30% (stover harvest with cover crop), phosphorus loads by 20%–40% (RB), and sediment loads by 30%–70% (stover harvest with cover crop and RB).

The critical period of aluminum stress on soybean root growth

Aluminum is prevalent in soils of tidal swamps. Soybean is known to be very sensitive to aluminum stress and so when tidal swamps are converted to soybean cropland, considerable effort and expense are required to overcome Al toxicity in soybean roots. It is therefore necessary to determine at what time in early development soybeans can best endure aluminum stress and identify aluminum-tolerant cultivars. This study was conducted by testing the impact of aluminum exposure on three soybean cultivars (Tanggamus, Karasumame, and M652) (relative to no-exposure controls) at four time periods at 10, 20, and 30 days after planting. No significant effect of aluminum on root growth in the first five days after exposure was observed, but the toxic effects became evident after soybeans had been exposed to aluminum for 10 days. Soybean seedlings that experienced aluminum stress earliest (at 10 days after planting) were more negatively impacted by Al exposure than seedlings exposed later (e.g., 30 days after planting). Root growths of the three cultivars we tested in this study were all detrimentally impacted by aluminum exposure. However, the M652 cultivar was the most sensitive to aluminum exposure. We conclude that the critical threshold period for soybean root growth to succumb to aluminum stress is within the first 30 days after planting, whereas the tolerance to aluminum stress occurs only during the first 10 days of exposure.

Geographical distribution, diversity and pathogenicity of Colletotrichum associ-ated with soybean anthracnose in Brazil

Eighty-five Colletotrichum isolates were obtained from soybean croplands in 34 municipalities of Mato Grosso state. Our objectives were characterize the isolates through mycelial growth, cultural and morphological analyses, as well as assessing pathogenicity. To evaluate cultural characteristics, the mycelial growth was daily measured, and the color of colonies was characterized after ten days incubation. The morphology, length, and width of 50 conidia per isolate were assessed. The pathogenicity of twenty-one isolates was evaluated on seed germination rate, incidence, and detection of extracellular enzymes. The cultural and morphology characteristics of Colletotrichum isolates were greatly variable. All isolates studied were able to reduce seed germination, produce sign and proteinase activity. These results suggest there are more than one Colletotrichum species associated with soybean anthracnose in Mato Grosso State.

Spatiotemporal Derivation of Intermittent Ponding in a Maize–Soybean Landscape from Planet Labs CubeSat Images

Climate change is increasing the frequency and intensity of heavy precipitation in the US Midwest, overwhelming existing tile drainage, and resulting in temporary soil ponding across the landscape. However, lack of direct observations of the dynamics of temporal soil ponding limits our understanding of its impacts on crop growth and biogeochemical cycling. Satellite remote sensing offers a unique opportunity to observe and analyze this dynamic phenomenon at the landscape scale. Here we analyzed a series of red–green–blue (RGB) and near infrared (NIR) remote sensing images from the Planet Labs CubeSat constellation following a period of heavy precipitation in May 2017 to determine the spatiotemporal characteristics of ponding events in the maize–soybean cropland of Champaign County, Illinois USA. We trained Random Forest algorithms for near-daily images to create binary classifications of surface water versus none, which achieved kappa values around 0.9. We then analyzed the morphology of classification results for connected pixels across space and time and found that 2.5% (5180 ha) of this cropland was classified as water surface at some point during this period. The frequency distribution of areal ponding extent exhibited a log–log relationship; the mean and median areas of ponds were 1231 m2 and 126 m2, respectively, with 26.1% of identified ponds being at the minimum threshold area of 45 m2, and 2.5% of the ponds having an area greater than 104 m2 (1 ha). Ponds lasted for a mean duration of 2.4 ± 1.7 days, and 2.3% of ponds lasted for more than a week. Our results suggest that transient ponding may be significant at the landscape scale and ought to be considered in assessments of crop risk, soil and water conservation, biogeochemistry, and sustainability.

Hybridization and introgression between Helicoverpa armigera and H. zea: an adaptational bridge

BackgroundInvasion of organisms into new ecosystems is increasingly common, due to the global trade in commodities. One of the most complex post-invasion scenarios occurs when an invasive species is related to a native pest, and even more so when they can hybridize and produce fertile progeny. The global pest Helicoverpa armigera was first detected in Brazil in 2013 and generated a wave of speculations about the possibility of hybridization with the native sister taxon Helicoverpa zea. In the present study, we used genome-wide single nucleotide polymorphisms from field-collected individuals to estimate hybridization between H. armigera and H. zea in different Brazilian agricultural landscapes.ResultsThe frequency of hybridization varied from 15 to 30% depending on the statistical analyses. These methods showed more congruence in estimating that hybrids contained approximately 10% mixed ancestry (i.e. introgression) from either species. Hybridization also varied considerably depending on the geographic locations where the sample was collected, forming a ‘mosaic’ hybrid zone where introgression may be facilitated by environmental and landscape variables. Both landscape composition and bioclimatic variables indicated that maize and soybean cropland are the main factors responsible for high levels of introgression in agricultural landscapes. The impact of multiple H. armigera incursions is reflected in the structured and inbred pattern of genetic diversity.ConclusionsOur data showed that the landscape composition and bioclimatic variables influence the introgression rate between H. armigera and H. zea in agricultural areas. Continuous monitoring of the hybridization process in the field is necessary, since agricultural expansion, climatic fluctuations, changing composition of crop species and varieties, and dynamic planting seasons are some factors in South America that could cause a sudden alteration in the introgression rate between Helicoverpa species. Introgression between invasive and native pests can dramatically impact the evolution of host ranges and resistance management.

STUDY ON ARSENIC MITIGATION BY LIMING METHOD COMBINING ORGANIC MATERIAL ON SOYBEAN AND CORN IN AN GIANG PROVINCE

The study on arsenic mitigation by liming method, combining organic material on soybean and corn crops on the dyke in An Giang, was conducted to determine the effect of lime combination rice husk ash (03 tons/ha) on the uptake of arsenic in soybean, and the effect of lime combination sawdust (02 tons/ha) on the uptake of arsenic in corn crops. The experiment was arranged in a completely randomized block field format, where the experiments were arranged with 2 treatments with 4 replicates: Treatment 1 (NT1): control (No liming combined with organic materials); Treatment 2 (NT2): liming combined with organic materials in a ratioof 1: 1 (Dosage: 03 tons / ha of lime mixture with rice husk biochar for soybean crop land, 02 tons / ha of mixed lime combined with sawdust for corn crop land). The results revealed that liming treatments combined withorganic materials increased pH H2O and arsenic in soil - the lime combination rice husk ash arsenic content in roots (0.836 mg/kg), leaf stems (0.83 mg/kg) and seeds (0.06 mg/kg) had results lower than the control treatments, 33.1%; 32.5% and 45.5% respectively. Applying and combining lime with sawdust resulted in lower arsenic content in leaf stems (95.3 mg/kg) and seeds (6.33 mg/kg) compared to the control treatmentsby 31.9% and 49.4%. Therefore, added lime combined with organic matter can decrease arsenic content in plants, and it is recommended that growers apply this technique to reduce the absorption of arsenic into crops.

Soybean expansion and the challenge of the coexistence of agribusiness with local production and conservation initiatives: pesticides in a Ramsar site in Uruguay

SummarySoybean has undergone the greatest expansion of any global crop, fuelled by the emergence of herbicide-resistant crops. In Uruguay, soybean croplands have increased from virtually zero to more than 1 million ha in 20 years. Uruguay is also implementing its system of protected areas. Here, we assess the presence of pesticides within a Ramsar site and protected area, in a basin dominated by croplands. We consider pesticides as surrogates of the subtle impacts of agribusiness on conservation initiatives and other productions. Pesticides were found in soils, fishes and beehives, both within and around the protected area. Endosulfan was found in all matrices analysed (23 of 80 samples), while glyphosate (0–2.31 mg/kg) and aminomethylphosphonic acid (AMPA; 0–0.61 mg/kg) were found in all soil classes. The study also allowed for a retrospective evaluation of a recent policy banning endosulfan in Uruguay, suggesting that while the protected area has not been immune to the impacts of agribusiness on human health or biodiversity, limiting the use of pesticides reduces or avoids some of them. This has implications for the design of multifunctional landscapes and for the debate on land sharing versus land sparing.

Effects of warming and elevated O3 concentrations on N2O emission and soil nitrification and denitrification rates in a wheat-soybean rotation cropland

The effects of warming and elevated ozone (O3) concentrations on nitrous oxide (N2O) emission from cropland has received increasing attention; however, the small number of studies on this topic impedes understanding. A field experiment was performed to explore the role of warming and elevated O3 concentrations on N2O emission from wheat-soybean rotation cropland from 2012 to 2013 using open-top chambers (OTCs). Experimental treatments included ambient temperature (control), elevated temperature (+2 °C), elevated O3 (100 ppb), and combined elevated temperature (+2 °C) and O3 (100 ppb). Results demonstrate that warming significantly increased the accumulative amount of N2O (AAN) emitted from the soil-winter wheat system due to enhanced nitrification rates in the wheat farmland and nitrate reductase activity in wheat leaves. However, elevated O3 concentrations significantly decreased AAN emission from the soil-soybean system owing to reduced nitrification rates in the soybean farmland. The combined treatment of warming and elevated O3 inhibited the emission of N2O from the soybean farmland. Additionally, both the warming and combined treatments significantly increased soil nitrification rates in winter wheat and soybean croplands and decreased denitrification rates in the winter wheat cropping system. Our results suggest that global warming and elevated O3 concentrations will strongly affect N2O emission from wheat-soybean rotation croplands.