Agricultural waste-based modified biochars differentially affected the soil properties, growth, and nutrient accumulation by maize (Zea mays L.) plants.

Biochar (BC) is an organic compound formed by the pyrolysis of organic wastes. Application of BCs as soil amendments has many benefits including carbon sequestration, enhanced soil fertility and sustainable agriculture production. In the present study, we acidified the different BCs prepared from rice straw, rice husk, wheat straw, cotton stalk, poultry manure, sugarcane press mud and vegetable waste; following which, we applied them in a series of pot experiments. Comparisons were made between acidified and non- acidified BCs for their effects on seed germination, soil properties (EC, pH) nutrient contents (P, K, Na) and organic matter. The treatments comprised of a control, and all above-described BCs (acidified as well as non-acidified) applied to soil at the rate of 1% (w/w). The maize crop was selected as a test crop. The results showed that acidified poultry manure BC significantly improved germination percentage, shoot length, and biomass of maize seedlings as compared to other BCs and their respective control plants. However, acidified BCs caused a significant decrease in nutrient contents (P, K, Na) of soil,maize seedlings, and the soil organic matter contents as compared to non- acidified BCs. But when compared with control treatments, all BCs treatments (acidified and non-acidified) delivered higher levels of nutrients and organic matter contents. It was concluded that none of the BCs (acidified and non-acidified) had caused negative effect on soil conditions and growth of maize. In addition, the acidification of BC prior to its application to alkaline soils might had altered soil chemistry and delivered better maize growth. Moving forward, more research is needed to understand the long-term effects of modified BCs on nutrient dynamics in different soils. In addition, the possible effects of BC application timings, application rates, particle size, and crop species have to be evaluated systemtically.

Estimation of rice yield using multivariate analysis techniques based on meteorological parameters.

This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980-81 to 2020-21 have been taken from various issues of Statistical Abstracts of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices.

Farmland pest recognition based on Cascade RCNN Combined with Swin-Transformer.

Agricultural pests and diseases pose major losses to agricultural productivity, leading to significant economic losses and food safety risks. However, accurately identifying and controlling these pests is still very challenging due to the scarcity of labeling data for agricultural pests and the wide variety of pest species with different morphologies. To this end, we propose a two-stage target detection method that combines Cascade RCNN and Swin Transformer models. To address the scarcity of labeled data, we employ random cut-and-paste and traditional online enhancement techniques to expand the pest dataset and use Swin Transformer for basic feature extraction. Subsequently, we designed the SCF-FPN module to enhance the basic features to extract richer pest features. Specifically, the SCF component provides a self-attentive mechanism with a flexible sliding window to enable adaptive feature extraction based on different pest features. Meanwhile, the feature pyramid network (FPN) enriches multiple levels of features and enhances the discriminative ability of the whole network. Finally, to further improve our detection results, we incorporated non-maximum suppression (Soft NMS) and Cascade R-CNN's cascade structure into the optimization process to ensure more accurate and reliable prediction results. In a detection task involving 28 pest species, our algorithm achieves 92.5%, 91.8%, and 93.7% precision in terms of accuracy, recall, and mean average precision (mAP), respectively, which is an improvement of 12.1%, 5.4%, and 7.6% compared to the original baseline model. The results demonstrate that our method can accurately identify and localize farmland pests, which can help improve farmland's ecological environment.

Unveiling the nexus between rural population aging, technical efficiency, and carbon emissions in Chinese agriculture.

INTRODUCTION: According to the seventh national population census in China, the proportion of people aged 65 and above in the population reached 13.5%. The aging trend is more pronounced in rural areas, indicating that China has entered an aging society. This article focuses on agricultural carbon emissions in the context of aging, studying the impact of rural population aging on agricultural carbon emissions. STUDY OBJECTIVES: Under the background of deepening population aging, let us discuss how to maintain the green and sustainable development of agriculture in China. METHODOLOGY: Fixed effects and mediating effects models are used. Technical efficiency is used as a mediating variable to discuss the relationship between rural population ageing, technical efficiency and agricultural carbon emissions. RESULTS: This paper adopts the classical carbon emission calculation theory of IPCC to measure agricultural carbon emissions from 2010 to 2019, and China's plantation carbon emissions show an "inverted U-shaped" trend, reaching a high level in 2015 and then starting to decline. In addition, the fixed-effects benchmark regression found that the aging of the rural population promotes agricultural carbon emissions, and the technical efficiency of agriculture suppresses agricultural carbon emissions. Finally, the mediating effect model is applied to explore the relationship between the three. Using technical efficiency as the mediating variable, it is found that under the masking effect, rural population aging will weaken agricultural carbon emissions through technical efficiency, thus achieving the suppression of agricultural carbon emissions. POLICY RECOMMENDATION: The formulation and modification of agricultural carbon-reducing policy row policies should take full account of the broader context of rural population ageing; increase the interconnectedness and interaction between rural population ageing and agricultural production technology, and actively play a positive role in promoting the efficiency of agricultural technology as a result of rural population ageing; and, in accordance with the actual situation of agricultural research, appropriately increase the strength of financial support for agriculture to improve agricultural technology and promote low-carbon development in agriculture.

Public healthcare disparities in Africa: the food production systems and its dichotomy in a South African context.

One of the major concerns of development in Africa is the issue of public health. In Africa, public healthcare has been and still is a problem most African countries are faced with. The problem of public healthcare seems to be unabated even though there are measures that are put in place for its effectiveness. There is hunger, malnutrition, high mortality rate, illnesses and deterioration of life expectancy in most developing countries of Africa. The dramatic unprecedented public health disparity has become a scourge in developing countries where it has purportedly impaired the developmental efforts, economic growth and prosperity. As a result, there is a need to scrutinize possible causes that exacerbates public health issues in developing countries. The paper argues that the current food production system (conventional) contributes to current status of public health as compared to the previous food production system (organic). The purpose of this paper is to conceptualize public healthcare disparities, juxtaposing organic and conventional food production that result as human food consumption. The paper employs literature-based analysis as a methodology to assemble data in respect of public healthcare disparities and food production systems.

Impact of biological manure substitution on grain yield, nitrogen recovery efficiency, and soil biochemical properties.

Fertilization plays a crucial role in ensuring global food security and ecological balance. This study investigated the impact of substituting innovative biological manure for chemical fertilization on rice (Oryza sativa L) productivity and soil biochemical properties based on a three-year experiment. Our results suggested rice yield and straw weight were increased under manure addition treatment. Specifically, 70% of total nitrogen (N) fertilizer substituted by biological manure derived from straw, animal waste and microbiome, led to a substantial 13.6% increase in rice yield and a remarkable 34.2% boost in straw weight. In comparison to the conventional local farmer practice of applying 165 kg N ha-1, adopting 70% of total N plus biological manure demonstrated superior outcomes, particularly in enhancing yield components and spike morphology. Fertilization treatments led to elevated levels of soil microbial biomass carbon and N. However, a nuanced comparison with local practices indicated that applying biological manure alongside urea resulted in a slight reduction in N content in vegetative and economic organs, along with decreases of 10.4%, 11.2%, and 6.1% in N recovery efficiency (NRE), respectively. Prudent N management through the judicious application of partial biological manure fertilizer in rice systems could be imperative for sustaining productivity and soil fertility in southern China.

Production and optimization of surfactin produced from locally isolated Bacillus halotolerans grown on agro-industrial wastes and its antimicrobial efficiency.

INTRODUCTION: Optimal exploitation of the huge amounts of agro-industrial residuals that are produced annually, which endangers the ecosystem and ultimately contributes to climate change, is one of the solutions available to produce value-added compounds. AIM AND OBJECTIVES: This study aimed at the economic production and optimization of surfactin. Therefore, the production was carried out by the microbial conversion of Potato Peel Waste (PPW) and Frying Oil Waste (FOW) utilizing locally isolated Bacillus halotolerans. Also, investigating its potential application as an antimicrobial agent towards some pathogenic strains. RESULTS: Screening the bacterial isolates for surfactin production revealed that the strain with the highest yield (49 g/100 g substrate) and efficient oil displacement activity was genetically identified as B. halotolerans. The production process was then optimized utilizing Central Composite Design (CCD) resulting in the amelioration of yield by 11.4% (from 49 to 55.3 g/100 g substrate) and surface tension (ST) by 8.3% (from 36 to 33 mN/m) with a constant level of the critical micelle concentration (CMC) at 125 mg/L. Moreover, the physiochemical characterization studies of the produced surfactin by FTIR, 1H NMR, and LC-MS/MS proved the existence of a cyclic lipopeptide (surfactin). The investigations further showed a strong emulsification affinity for soybean and motor oil (E24 = 50%), as well as the ability to maintain the emulsion stable over a wide pH (4-10) and temperature (10-100 °C) range. Interestingly, surfactin had a broad-spectrum range of inhibition activity against Bacillus subtilis, Staphylococcus aureus, Pseudomonas aeruginosa, klebsiella pneumonia, and Candida albicans. CONCLUSION: Subsequently, the screening of the isolates and the utilized food-processing wastes along with the extraction technique resulted in a high yield of surfactin characterized by acceptable ST and CMC levels. However, optimization of the cultural conditions to improve the activity and productivity was achieved using Factor-At-A-Time (OFAT) and Central Composite Design (CCD). In contrast, surface activity recorded a maximum level of (33 mN/n) and productivity of 55.3 g/100 g substrate. The optimized surfactin had also the ability to maintain the stability of emulsions over a wide range of pH and temperature. Otherwise, the obtained results proved the promising efficiency of the surfactin against bacterial and fungal pathogens.

Leopard density and determinants of space use in a farming landscape in South Africa.

Protected areas are traditionally the foundation of conservation strategy, but land not formally protected is of particular importance for the conservation of large carnivores because of their typically wide-ranging nature. In South Africa, leopard (Panthera pardus) population decreases are thought to be occurring in areas of human development and intense negative interactions, but research is biased towards protected areas, with quantitative information on population sizes and trends in non-protected areas severely lacking. Using Spatially Explicit Capture-Recapture and occupancy techniques including 10 environmental and anthropogenic covariates, we analysed camera trap data from commercial farmland in South Africa where negative human-wildlife interactions are reported to be high. Our findings demonstrate that leopards persist at a moderate density (2.21 /100 km2) and exhibit signs of avoidance from areas where lethal control measures are implemented. This suggests leopards have the potential to navigate mixed mosaic landscapes effectively, enhancing their chances of long-term survival and coexistence with humans. Mixed mosaics of agriculture that include crops, game and livestock farming should be encouraged and, providing lethal control is not ubiquitous in the landscape, chains of safer spaces should permit vital landscape connectivity. However, continuing to promote non-lethal mitigation techniques remains vital.

A study on nickel application methods for optimizing soybean growth.

Fertilization with nickel (Ni) can positively affect plant development due to the role of this micronutrient in nitrogen (N) metabolism, namely, through urease and NiFe-hydrogenase. Although the application of Ni is an emerging practice in modern agriculture, its effectiveness strongly depends on the chosen application method, making further research in this area essential. The individual and combined effects of different Ni application methods-seed treatment, leaf spraying and/or soil fertilization-were investigated in soybean plants under different edaphoclimatic conditions (field and greenhouse). Beneficial effects of the Soil, Soil + Leaf and Seed + Leaf treatments were observed, with gains of 7 to 20% in biological nitrogen fixation, 1.5-fold in ureides, 14% in shoot dry weight and yield increases of up to 1161 kg ha-1. All the Ni application methods resulted in a 1.1-fold increase in the SPAD index, a 1.2-fold increase in photosynthesis, a 1.4-fold increase in nitrogenase, and a 3.9-fold increase in urease activity. Edaphoclimatic conditions exerted a significant influence on the treatments. The integrated approaches, namely, leaf application in conjunction with soil or seed fertilization, were more effective for enhancing yield in soybean cultivation systems. The determination of the ideal method is crucial for ensuring optimal absorption and utilization of this micronutrient and thus a feasible and sustainable management technology. Further research is warranted to establish official guidelines for the application of Ni in agricultural practices.

Assessment of land use and land cover change detection and prediction using deep learning techniques for the southwestern coastal region, Goa, India.

Understanding the connections between human activities and the natural environment depends heavily on information about land use and land cover (LULC) in the form of accurate LULC maps. Environmental monitoring using deep learning (DL) is rapidly growing to preserve a sustainable environment in the long term. For establishing effective policies, regulations, and implementation, DL can be a valuable tool for assessing environmental conditions and natural resources that will positively impact the ecosystem. This paper presents the assessment of land use and land cover change detection (LULCCD) and prediction using DL techniques for the southwestern coastal region, Goa, also known as the tourist destination of India. It consists of three components: (i) change detection (CD), (ii) quantification of LULC changes, and (iii) prediction. A new CD assessment framework, Spatio-Temporal Encoder-Decoder Self Attention Network (STEDSAN), is proposed for the LULCCD process. A dual branch encoder-decoder network is constructed using strided convolution with downsampling for the encoder and transpose convolution with upsampling for the decoder to assess the bitemporal images spatially. The self-attention (SA) mechanism captures the complex global spatial-temporal (ST) interactions between individual pixels over space-time to produce more distinct features. Each branch accepts the LULC map of 2 years as one of its inputs to determine binary and multiclass changes among the bitemporal images. The STEDSAN model determines the patterns, trends, and conversion from one LULC type to another for the assessment period from 2005 to 2018. The binary change maps were also compared with the existing state of the art (SOTA) CD methods, with STEDSAN having an overall accuracy of 94.93%. The prediction was made using an recurrent neural network (RNN) known as long short term memory network (LSTM) for the year 2025. Experiments were conducted to determine area-wise changes in several LULC classes, such as built-up (BU), crops (kharif crop (KC), rabi crop (RC), zaid crop (ZC), double/triple (D/T C)), current fallow (CF), plantation (PL), forests (evergreen forest (EF), deciduous forest (DF), degraded/scurb forest (D/SF) ), littoral swamp (LS), grassland (GL), wasteland (WL), waterbodies max (Wmx), and waterbodies min (Wmn). As per the analysis, over the period of 13 years, there has been a net increase in the amount of BU (1.25%), RC (1.17%), and D/TC( 2.42%) and a net decrease in DF (3.29%) and WL(1.44%) being the most dominant classes being changed. These findings will offer a thorough description of identifying trends in coastal areas that may incorporate methodological hints for future studies. This study will also promote handling the spatial and temporal complexity of remotely sensed data employed in categorizing the coastal LULC of a heterogeneous landscape.

Intercropping with maize and sorghum-induced saikosaponin accumulation in Bupleurum chinense DC. by liquid chromatography-mass spectrometry-based metabolomics.

Bupleuri Radix is an important medicinal plant, which has been used in China and other Asian countries for thousands of years. Cultivated Bupleurum chinense DC. (B. chinense) is the main commodity of Bupleuri Radix. The benefits of intercropping with various crops for B. chinense have been recognized; however, the influence of intercropping on the chemical composition of B. chinense is still unclear yet. In this study, intercropping with sorghum and maize exhibited little effect on the root length, root diameter, and single root mass of B. chinense. Only the intercropping with sorghum increased the root length of B. chinense slightly compared to the monocropping. In addition, 200 compounds were identified by UHPLC-Q-TOF-MS, and metabolomic combined with the Venn diagram and heatmap analysis showed apparent separation between the intercropped and monocropped B. chinense samples. Intercropping with sorghum and maize could both increase the saikosaponins, fatty acyls, and organic acids in B. chinense while decreasing the phospholipids. The influence of intercropping on the saikosaponin biosynthesis was probably related with the light intensity and hormone levels in B. chinense. Moreover, we found intercropping increased the anti-inflammatory activity of B. chinense. This study provides a scientific reference for the beneficial effect of intercropping mode of B. chinense.

Effects of management practices on the ecosystem-service multifunctionality of temperate grasslands.

Human wellbeing depends on ecosystem services, highlighting the need for improving the ecosystem-service multifunctionality of food and feed production systems. We study Swiss agricultural grasslands to assess how employing and combining three widespread aspects of grassland management and their interactions can enhance 22 plot-level ecosystem service indicators, as well as ecosystem-service multifunctionality. The three management aspects we assess are i) organic production system, ii) an eco-scheme prescribing extensive management (without fertilization), and iii) harvest type (pasture vs. meadow). While organic production system and interactions between the three management aspects play a minor role, the main effects of eco-scheme and harvest type considerably shape single services. Moreover, the eco-scheme 'extensive management' and the harvest type 'pasture' enhance plot-scale ecosystem-service multifunctionality, mostly through facilitating cultural services at the expense of provisioning services. These changes in ecosystem-service supply occur mainly via changes in land-use intensity, i.e., reduced fertilizer input and harvest frequency. In conclusion, diversifying grassland management where this is currently homogeneous across farms and landscapes depicts an important first step to improve landscape-scale multifunctionality for sustainable grassland systems. To meet societal ecosystem services demand, the three studied management aspects can be systematically combined to increase ecosystem services that are in short supply.

Cover crops support the climate change mitigation potential of agroecosystems.

Cover crops have the potential to mitigate climate change by reducing negative impacts of agriculture on ecosystems. This study is first to quantify the net climate change mitigation impact of cover crops including land-use effects. A systematic literature and data review was conducted to identify major drivers for climate benefits and costs of cover crops in maize (Zea maize L.) production systems. The results indicate that cover crops lead to a net climate change mitigation impact (NCCMI) of 3.30 Mg CO2e ha-1 a-1. We created four scenarios with different impact weights of the drivers and all of them showing a positive NCCMI. Carbon land benefit, the carbon opportunity costs based on maize yield gains following cover crops, is the major contributor to the NCCMI (34.5% of all benefits). Carbon sequestration is the second largest contributor (33.8%). The climate costs of cover crops are mainly dominated by emissions from their seed production and foregone benefits due to land use for cover crops seeds. However, these two costs account for only 15.8% of the benefits. Extrapolating these results, planting cover crops before all maize acreage in the EU results in a climate change mitigation of 49.80 million Mg CO2e a-1, which is equivalent to 13.0% of the EU's agricultural emissions. This study highlights the importance of incorporating cover crops into sustainable cropping systems to minimize the agricultural impact to climate change.

Global synthesis on the response of soil microbial necromass carbon to climate-smart agriculture.

Climate-smart agriculture (CSA) supports the sustainability of crop production and food security, and benefiting soil carbon storage. Despite the critical importance of microorganisms in the carbon cycle, systematic investigations on the influence of CSA on soil microbial necromass carbon and its driving factors are still limited. We evaluated 472 observations from 73 peer-reviewed articles to show that, compared to conventional practice, CSA generally increased soil microbial necromass carbon concentrations by 18.24%. These benefits to soil microbial necromass carbon, as assessed by amino sugar biomarkers, are complex and influenced by a variety of soil, climatic, spatial, and biological factors. Changes in living microbial biomass are the most significant predictor of total, fungal, and bacterial necromass carbon affected by CSA; in 61.9%-67.3% of paired observations, the CSA measures simultaneously increased living microbial biomass and microbial necromass carbon. Land restoration and nutrient management therein largely promoted microbial necromass carbon storage, while cover crop has a minor effect. Additionally, the effects were directly influenced by elevation and mean annual temperature, and indirectly by soil texture and initial organic carbon content. In the optimal scenario, the potential global carbon accrual rate of CSA through microbial necromass is approximately 980 Mt C year-1, assuming organic amendment is included following conservation tillage and appropriate land restoration. In conclusion, our study suggests that increasing soil microbial necromass carbon through CSA provides a vital way of mitigating carbon loss. This emphasizes the invisible yet significant influence of soil microbial anabolic activity on global carbon dynamics.

Assessment of soil fertility and nutrient management strategies in calcareous soils of Khuzestan province: a case study using the Nutrient Index Value method.

Soil fertility (SF) is a crucial factor that directly impacts the performance and quality of crop production. To investigate the SF status in agricultural lands of winter wheat in Khuzestan province, 811 samples were collected from the soil surface (0-25 cm). Eleven soil properties, i.e., electrical conductivity (EC), soil organic carbon (SOC), total nitrogen (TN), calcium carbonate equivalent (CCE), available phosphorus (Pav), exchangeable potassium (Kex), iron (Fe), copper (Cu), zinc (Zn), manganese (Mn), and soil pH, were measured in the samples. The Nutrient Index Value (NIV) was calculated based on wheat nutritional requirements. The results indicated that 100%, 93%, and 74% of the study areas for CCE, pH, and EC fell into the low, moderate, and moderate to high NIV classes, respectively. Also, 25% of the area is classified as low fertility (NIV < 1.67), 75% falls under medium fertility (1.67 < NIV value < 2.33), and none in high fertility (NIV value > 2.33). Assessment of the mean wheat yield (AWY) and its comparison with NIV showed that the highest yield was in the Ramhormoz region (5200 kg.ha-1), while the lowest yield was in the Hendijan region (3000 kg.ha-1) with the lowest EC rate in the study area. Elevated levels of salinity and CCE in soils had the most negative impact on irrigated WY, while Pav, TN, and Mn availability showed significant effects on crop production. Therefore, implementing SF management practices is essential for both quantitative and qualitative improvement in irrigated wheat production in Khuzestan province.

Spatial and temporal distribution of optimal maize sowing dates in Nigeria.

Climate change and inter-annual variability cause variation in rainfall commencement and cessation which has consequences for the maize growing season length and thus impact yields. This study therefore sought to determine the spatially explicit optimum maize sowing dates to enable site specific recommendations in Nigeria. Gridded weather and soil data, crop management and cultivar were used to simulate maize yield from 1981-2019 at a scale of 0.5°. A total of 37 potential sowing dates between 1 March and 7 November at an interval of 7 days for each year were evaluated. The optimum sowing date was the date which maximizes yield at harvest, keeping all other management factors constant. The results show that optimum sowing dates significantly vary across the country with northern Nigeria having notably delayed sowing dates compared to southern Nigeria which has earlier planting dates. The long-term optimal sowing dates significantly (p<0.05), shifted between the 1980s (1981-1990), and current (2011-2019), for most of the country. The most optimum planting dates of southern Nigeria shifted to later sowing dates while most optimum sowing dates of central and northern Nigeria shifted to earlier sowing dates. There was more variation in optimum sowing dates in the wetter than the drier agro-ecologies. Changes in climate explain changes in sowing dates in wetter agro-ecologies compared to drier agro-ecologies. The study concludes that the optimum sowing dates derived from this study and the corresponding methodology used to generate them can be used to improve cropping calendars in maize farming in Nigeria.

Variation and interrelationships in the growth, yield, and lodging of oat under different planting densities.

Background: Oat is a dual-purpose cereal used for grain and forage. The demand of oat has been increasing as the understanding of the nutritional, ecological, and economic values of oat increased. However, the frequent lodging during the growing period severely affect the high yielding potential and the quality of the grain and forage of oat. Methods: Therefore, we used the lodging-resistant variety LENA and the lodging-sensitive variety QY2 as materials, implementing four different planting densities: 2.25×106 plants/ha (D1), 4.5×106 plants/ha (D2), 6.75×106 plants/ha (D3), and 9×106 plants/ha (D4). At the appropriate growth and development stages, we assessed agronomic traits, mechanical characteristics, biochemical compositions, yield and its components. The study investigated the impact of planting density on the growth, lodging, and yield of oat, as well as their interrelationships. Additionally, we identified the optimal planting density to establish a robust crop structure. The research aims to contribute to the high-yield and high-quality cultivation of oat. Results: We observed that with increasing planting density, plant height, grass and grain yields of both varieties first increased and then decreased; root fresh weight, stem diameter, stem wall thickness, stem puncture strength, breaking strength, compressive strength, lignin and crude fiber contents, and yield components decreased; whereas the lodging rate and lodging coefficient increased. Planting density affects lodging by regulating plant height, height of center of gravity, stem wall thickness, internode length, and root fresh weight of oat. Additionally, it can impact stem mechanical strength by modulating the synthesis of lignin and crude fiber, which in turn affecting lodging resistance. Plant height, height of center of gravity, stem wall thickness, internode length, root fresh weight, breaking strength, compressive strength, lignin and crude fiber content, single-plant weight, grain yield and 1,000-grain weight can serve as important indicators for evaluating oat stem lodging resistance. We also noted that planting density affected grain yield both directly and indirectly (by affecting lodging); high density increased lodging rate and decreased grain yield, mainly by reducing 1,000-grain weight. Nonetheless, there was no significant relationship between lodging and grass yield. As appropriate planting density can increase the yield while maintaining good lodging resistance, in this study, 4.5×106 plants/ha (D2) was found to be the best planting density for oat in terms of lodging resistance and grass and grain yield. These findings can be used as a reference for oat planting.

A novel remote sensing-based approach to determine loss of agricultural soils due to soil sealing - a case study in Germany.

Soils provide habitat, regulation and utilization functions. Therefore, Germany aims to reduce soil sealing to 30 ha day - 1 by 2030 and to eliminate it by 2050. About 55 ha day - 1 of soil are damaged (average 2018-2021), but detailed information on its soil quality is lacking. This study proposes a new approach using geo-information and remote sensing data to assess agricultural soil loss in Lower Saxony and Brandenburg. Soil quality is assessed based on erosion resistance, runoff regulation, filter functions, yield potential and the Müncheberg Soil Quality Rating from 2006 to 2015. Data from the German Soil Map at a scale of 1:200,000 (BÜK 200), climate, topography, CORINE Land Cover (CLC) and Imperviousness Layer (IMCC), both provided by the Copernicus Land Monitoring Service (CLMS), are used to generate information on soil functions, potentials and agricultural soil loss due to sealing. For the first time, soil losses under arable land are assessed spatially, quantitatively and qualitatively. An estimate of the qualitative loss of agricultural soil in Germany between 2006 and 2015 is obtained by intersecting the soil evaluation results with the quantitative soil loss according to IMCC. Between 2006 and 2015, about 73,300 ha of land were sealed in Germany, affecting about 37,000 ha of agricultural soils. This corresponds to a sealing rate of 11 ha per day for Germany. In Lower Saxony and Brandenburg, agricultural soils were sealed at a rate of 1.9 ha day - 1 and 0.8 ha day - 1 respectively, removing these soils from primary land use. In Lower Saxony, 75% of soils with moderate or better biotic yield potential have been removed from primary land use, while in Brandenburg this figure is as high as 88%. Implementing this approach can help decision-makers reassess sealed land and support Germany's sustainable development strategy.

Deer activity levels and patterns vary along gradients of food availability and anthropogenic development.

Animal activity reflects behavioral decisions that depend upon environmental context. Prior studies typically estimated activity distributions within few areas, which has limited quantitative assessment of activity changes across environmental gradients. We examined relationships between two response variables, activity level (fraction of each day spent active) and pattern (distribution of activity across a diel cycle) of white-tailed deer (Odocoileus virginianus), with four predictors-deer density, anthropogenic development, and food availability from woody twigs and agriculture. We estimated activity levels and patterns with cameras in 48 different 10.36-km2 landscapes across three larger regions. Activity levels increased with greater building density, likely due to heightened anthropogenic disturbance, but did not vary with food availability. In contrast, activity patterns responded to an interaction between twigs and agriculture, consistent with a functional response in habitat use. When agricultural land was limited, greater woody twig density was associated with reduced activity during night and evening. When agricultural land was plentiful, greater woody twig density was associated with more pronounced activity during night and evening. The region with the highest activity level also experienced the most deer-vehicle collisions. We highlight how studies of spatial variation in activity expand ecological insights on context-dependent constraints that affect wildlife behavior.