Agricultural Crop Fields Research Articles

A biorefinery approach for the production of bioethanol from alkaline-pretreated, enzymatically hydrolyzed Nicotiana tabacum stalks as feedstock for the bio-based industry

In this study, an attempt was made to investigate bioethanol production using low-cost feedstock, namely, tobacco wastes obtained after the leaves harvesting. Tobacco stalks, an abundant biomass source of the leftover agricultural crop field, are a promising feedstock for bioethanol production. Traditional Thai tobacco (Nicotiana tabacum L.) is known as non-Virginia type tobacco stalks and was used as biomass feedstock for ethanol production by separate hydrolysis and fermentation (SHF) with a computerized fermenter. Tobacco stalks were efficiently hydrolyzed after a mild physical-chemical pretreatment. The economically cheapest alkaline chemical (2% CaO) was used for pretreatment. The robust yeast Saccharomyces cerevisiae was utilized, and it is suitable for industrial ethanol production. These data suggest that tobacco stalks are potential candidates for ethanol production from physical alkali-pretreated biomass with enzymatic hydrolysis on the SHF system.

Adsorption of Imazalil herbicide onto Moroccan agricultural soils: Kinetic and isotherm adsorption studies

Imazalil herbicide [1-[2-(2,4-dichlorophenyl)-2-prop-2-enoxyethyl] imidazole] belongs to the Enilconazole family of herbicides, this compound is largely utilized on agricultural field crops in Morocco. Soil properties greatly improve the adsorption of this chemical compound in soils, but there is a need to understand their effect on said soils. This current study evaluates the adsorption behavior of Imazalil using four agricultural soils from Morocco in batch systems and examines the impact of contact time and initial Imazalil concentration. The physico-chemical properties of soils were characterized by several analytical methods. The equilibrium isotherms of Imazalil removal by Moroccan soils were studied using batch tests. The obtained data revealed that the adsorbed amount of pollutant increases with the increasing the organic matter and organic carbon contents in soils. The adsorption kinetic process of different soils followed the pseudo second order kinetic model, and the adsorption mechanism of herbicide using different soils was controlled by intraparticle diffusion model. Isotherm adsorption experiments of Imazalil on four agricultural soils indicated that adsorption was best fitted by the Langmuir and Freundlich models. Our data suggest potential for reduced transfer of Imazalil from agricultural soils to groundwater in Morocco.

Comparative study on the performance of Typha latifolia and Cyperus Papyrus on the removal of heavy metals and enteric bacteria from wastewater by surface constructed wetlands

Semi-arid countries continue to face water scarcity, especially with the current global climatic changes. This scarcity has continuously increased over the last five decades in countries like Egypt, Syria, Libya and Jordan, where the agriculture sector consumes more than 85% of the country’s water resources. The problem of water scarcity in Egypt is further challenged by high levels of urbanization, increasing industrial uses, and the high cost of advanced treatment processes. These challenges lead to the utilization of untreated or poorly treated wastewater for irrigation of agricultural crop fields. Thus, the current study proposes the use of an eco-friendly technology consisting of a constructed wetland planted with Typha latifolia and Cyperus papyrus supported with zeolite substrate for water purification, to curb this challenge. The results showed that, the removal efficiency of COD, BOD, TSS, and ammonia were 68.5%, 71%, 70%, and 82.3%, respectively by Typha latifolia bed. On the other hand, the removal efficiency of COD, BOD, TSS and ammonia were 85.5%, 86.2%, 83.9% and 92.3% respectively by Cyperus papyrus bed. As a result, bacteriological parameters were reduced to 99.9%, and complete removal of Salmonella sp was achieved during three days by Cyperus papyrus. Box–Behnken design was utilized to optimize independent factors, including contact time (24–72h) and initial concentration of metals (15–45 mg L−1) and their responses. The removal efficiency of Cu and Zn were 72% and 84%, respectively of the optimum reaction time (72 h), with 16 plant stems and an initial metal concentration of 15 mg L−1.

Simulation of an Autonomous Mobile Robot for LiDAR-Based In-Field Phenotyping and Navigation

The agriculture industry is in need of substantially increasing crop yield to meet growing global demand. Selective breeding programs can accelerate crop improvement but collecting phenotyping data is time- and labor-intensive because of the size of the research fields and the frequency of the work required. Automation could be a promising tool to address this phenotyping bottleneck. This paper presents a Robotic Operating System (ROS)-based mobile field robot that simultaneously navigates through occluded crop rows and performs various phenotyping tasks, such as measuring plant volume and canopy height using a 2D LiDAR in a nodding configuration. The efficacy of the proposed 2D LiDAR configuration for phenotyping is assessed in a high-fidelity simulated agricultural environment in the Gazebo simulator with an ROS-based control framework and compared with standard LiDAR configurations used in agriculture. Using the proposed nodding LiDAR configuration, a strategy for navigation through occluded crop rows is presented. The proposed LiDAR configuration achieved an estimation error of 6.6% and 4% for plot volume and canopy height, respectively, which was comparable to the commonly used LiDAR configurations. The hybrid strategy with GPS waypoint following and LiDAR-based navigation was used to navigate the robot through an agricultural crop field successfully with an root mean squared error of 0.0778 m which was 0.2% of the total traveled distance. The presented robot simulation framework in ROS and optimized LiDAR configuration helped to expedite the development of the agricultural robots, which ultimately will aid in overcoming the phenotyping bottleneck.

The interaction of dissolved organic nitrogen removal and microbial abundance in iron-filings based green environmental media for stormwater treatment

Nonpoint sources pollution from agricultural crop fields and urbanized regions oftentimes have elevated concentrations of dissolved organic nitrogen (DON) in stormwater runoff, which are difficult for microbial communities to decompose. The impact of elevated DON can be circumvented through the use of green sorption media, such as Biosorption Activated Media (BAM) and Iron-Filing Green Environmental Media (IFGEM), which, as integral parts of microbial ecology, can contribute to the decomposition of DON. To compare the fate, transport, and transformation of DON in green sorption media relative to natural soil (control), a series of fixed-bed columns, which contain natural soil, BAM, and two types of IFGEM, respectively, were constructed to compare nutrient removal efficiency under three distinct stormwater influent conditions containing nitrogen and phosphorus. The interactions among six microbial species, including ammonia-oxidizing bacteria, nitrite-oxidizing bacteria, complete ammonia oxidation (comammox) bacteria, anaerobic ammonium oxidation (anammox) bacteria, dissimilatory nitrate reduction to ammonium bacteria, and iron-reducing bacteria, were further analyzed from microbial ecology perspectives to determine the DON impact on nutrient removal in BAM and IFGEM. Natural soil was only able to achieve adequate DON transformation at the influent condition of lower nutrient concentration. However, the two types of IFGEM showed satisfactory nutrient removals and achieved greater transformation of DON relative to BAM when treating stormwater in all three influent conditions.

Unsupervised Land Cover Classification of Hybrid and Dual-Polarized Images Using Deep Convolutional Neural Network

Enormous volumes of data made available by the high-resolution satellite imagery enable us to use a deep framework in the field of remote sensing for image classification. Recently, deep learning has been an area of interest for the researchers in the computer vision domain due to its high efficiency toward large-scale, high-dimensional data. In this letter, we propose an unsupervised learning algorithm to cluster hybrid polarimetric SAR images, and dual-polarized SAR images using the deep framework. We use feature extraction layers of the VGG16 model with batch normalization, which is trained with small patches derived from the hybrid polarimetric SAR images. It uses an entropy-based loss function and an adaptive learning rate optimization algorithm, Adam, for training. Broadly, the patches are segmented into three classes, namely, surface, volume, and double-bounce, which are defined with reference to the SAR scattering characteristics. Furthermore, we classify volume into dense forest region and agricultural crop fields. We also observe mixed classes between volume and double-bounce, mainly covering the settlements surrounded by areas covered by tall trees. Furthermore, we use transfer learning for generating the labels for dual-polarized images by using the learned weights of a hybrid polarized image model. Such a technique renders an average accuracy of 89.70% and 86.08% for hybrid polarized SAR images and dual-polarized SAR images, respectively. Hence, this method explores the spatial characteristics of remotely sensed images to distinguish urban settlements, water bodies, agricultural, and forest areas from the underlying scene in an unsupervised fashion.

A novel methodology for the development of an optimal agricultural crop field using Internet of Things

AbstractThe Internet of Things (IoT) plays a vital role in the entity sharing and minimizing the workload of the human beings in various aspects. Nowadays the term IoT is used in various fields such as health care, automobiles, industry, agriculture, and so on. Agriculture is the main source of food to whole world. There are various problems faced by the farmers in agriculture due to shortage and wastage of water and fertilizers. In this regard, an optimal IoT model has been developed and proposed to attain an effective crop field. The proposed IoT model will monitor, record temperature, and soil moisture values, which will be continuously analyzed to achieve optimal plant growth and yield. The motor will be connected to the IoT model which automatically switch on/off based on optimal threshold temperature and soil moisture content value. A novel irrigation algorithm named differential waterflow algorithm has been proposed and deployed in the proposed IoT model for the automatic usage of the motor in the field. The proposed IoT model provides a web interface to the user through the cloud storage, so that the farmer can control and monitor the system in remote. The proposed system will reduce the water consumption and will ensure the uniform water distribution to the crops through the Poisson distribution which results in increasing yield.

Drone Operated Crop Monitoring and Agrochemical Spraying in Agriculture

Agriculture plays a strategic role in a country's economic development process. In order to raise the farm productivity and to develop proficient administration, it is crucial to look into the growth of the crops, then the surrounding environment and farming operations. Earlier most of the human communities fully depend on agriculture for their survival. But now the scenario is totally opposite due to various reasons. It is our duty and also important to consider the proper and smart utilization of good resources. The main issue nowadays is using agro chemicals. To have a good resource it is mandatory to use agrochemicals in agriculture to maintain the quality of the plants. In agricultural crop fields, agrochemicals are generally used to increase productivity and improve quality. Through improved spraying effectiveness, the amount of pesticide used in agriculture can be minimized and the risk of environmental damage can be mitigated.. However direct contact with these agro chemicals can result in various diseases like cancers, birth defects, reproductive dysfunction, nausea, rashes etc. Also most of the agro chemicals are wasted during the time of spray. But if agrochemical is used, then it will improve agricultural quality production. By considering all these, a new system is proposed which consists of Drone, Tank for agrochemical, Camera and Controllers and other supporting mechanisms. The proposed system will be operated by a human over the farming filed and monitors all the area through the camera. The proposed system will give the details of affected area alone, so that only those areas can be sprayed with the help of micro nozzle. Due this human contact will be avoided, amount of agrochemicals used will be reduced, the quality and production of crop will be increased. Also it will reduce the cost of agrochemicals used.

UAV Based Agricultural Crop Canopy Mapping for Crop Field Monitoring

Abstract. Nowadays, mapping of agricultural crop canopy in different growing stages are vital data for crop field monitoring than field-based observations in large scale agricultural crop fields. By mapping agricultural crop canopy, it is very easy to analyse the status of an agricultural crop field by using different vegetation indices. Further, the data can be used to estimate the yield. These information are timely and reliable spatial information to the farmers and decision makers. Mapping of crop canopy in an agricultural crop field in different growing stages are very challenging using satellite imagery mainly due to the difficulty of recording with high cloud coverage. Also, the cost for satellite imagery are higher in proportion to the spatial resolution. It takes some time to order a satellite imagery and sometimes can’t cover some growing stages. This problem can be solved by using low cost RGB based UAV imageries which can be operated at low altitudes (below the clouds) which and when necessary. This study is therefore aimed at mapping of a maize crop canopy using RGB based UAV imageries. UAV flights at different growth stages were carried out with a high resolution RGB camera over a maize field in Ampara District, Sri Lanka. For accurate crop canopy mapping, very high-resolution multi-temporal ortho-mosaicked images were derived from UAV imageries using free and open source image processing platforms with spatial resolution in centimetre level. The resultant multi-temporal ortho-mosaicked images can be used to map and monitor the crop field’s precise and efficient manner. These information are very important for farmers and decision makers to properly manage the crop fields.

Exploring Ecological Alternatives for Crop Protection Using Coriandrum sativum Essential Oil.

Essential oils (EOs) are a natural source of active compounds with antifungal, antimycotoxigenic, and herbicidal potential, and have been successfully used in organic agriculture, instead of chemical compounds obtained by synthesis, due to their high bioactivity and the absence of toxicity. The aim of this study was to highlight the importance of Coriandrum sativum essential oil (CEO) as a potential source of bioactive constituents and its applications as an antifungal and bioherbicidal agent. The CEO was obtained by steam distillation of coriander seeds and GC-MS technique was used to determine the chemical composition. Furthermore, in vitro tests were used to determine the antifungal potential of CEO on Fusarium graminearum mycelia growth through poisoned food technique, resulting in the minimum fungistatic (MCFs) and fungicidal concentrations (MCFg). The antifungal and antimycotoxigenic effect of CEO was studied on artificially contaminated wheat seeds with F. graminearum spores. Additionally, the herbicidal potential of CEO was studied by fumigating monocotyledonous and dicotyledonous weed seeds, which are problematic in agricultural field crops in Romania. The in vitro studies showed the antifungal potential of CEO, with a minimum concentration for a fungistatic effect of 0.4% and the minimum fungicidal concentration of 0.6%, respectively. An increase in the antifungal effects was observed in the in vivo experiment with F. graminearum, where a mixture of CEO with Satureja hortensis essential oil (SEO) was used. This increase is attributed to the synergistic effect of both EOs. Moreover, the synthesis of deoxynivalenol (DON)-type mycotoxins was found to be less inhibited. Hence, CEO has shown an herbicidal potential on weed seeds by affecting inhibition of germination.

Food composition of Indian Eagle Owl Bubo bengalensis Franklin (Aves: Strigiformes: Strigidae) from Tiruchirappalli District, Tamil Nadu, India

The diet of the Indian Eagle Owl was studied from April to September 2017 in Tiruchirapalli District, Tamil Nadu, India. Analysis of 1082 regurgitated pellets yielded 2077 prey items; the mean prey items/ pellet was 1.91. The diet constituted 65.1% of rodent prey and the remaining 34.83% of other groups of both vertebrate and invertebrate animals. The mean percentage of prey composition was 31.15% Millardia meltada Soft-furred Field Rat, 12.95% Bandicota bengalensis Lesser Bandicoot Rat, 10.25% Mus booduga Indian Field Mouse, and 10.24% of other rodent species. Of the 34.83% of non-rodent prey, the owls ingested insects (Rhinoceros beetles, 9.58%), Arachnida (Solifugae or Sun spider, Galeodes sp., 9.58%), reptiles (Calotes sp., 3.7%), amphibians (3.56%), shrews (Suncus murinus, 2.84%), and others (5.57%). The Indian Eagle Owls consumed more than one prey per day and chiefly foraged in agricultural crop fields and consumed both small mammals and insects of agricultural importance under crop ecosystems.

Assessment of the energy potential of agricultural biomass residues in Turkey

Agricultural biomass residue based energy generation is one of the major focus areas of renewable energy in different countries around the world. A large quantity of biomass residue is generated from agricultural crop production. However, the biomass residues are the distributed resources with variation in spatio-temporal availability and its characteristics which their energy potential depends on. Biomass residues are emerged after harvesting of main crops as stalks, straw, stems and leaves, husks, cobs and etc. In this study, agricultural biomass residues energy potential was explored for Turkey using conducted studies of agricultural biomass residues characteristics in several countries located at different geographic regions. Data on characteristics of agricultural biomass residues were used from 16 European Union countries, as well as in India, Cameroon, China, Pakistan, Nigeria, Uganda and Turkey. Some residue characteristics such as residue product ratios, moisture levels, and lower heating calorific values of dry matter were taken into account. Furthermore, a mathematical model was defined for the calculation of the energy potential of Turkey's agricultural biomass residues. Theoretical biomass amount and theoretical energy potential, as well as available energy potential values of agricultural biomass residues were determined. The total amounts of biomass residues obtained from arable field crops and horticultural crops in Turkey were estimated as 59,432 kilotonne and 15,652 kilotonne respectively. The theoretical energy potential of these all agricultural biomass residues were calculated as 908,119 TJ and 90,354 TJ, respectively. The available energy potentials from agricultural biomass residues were calculated as 298,955 TJ for arable agricultural field crops and 65,491 TJ for horticultural crops.

Weed infestation structure of fodder beet fields under various tillage systems

Nowadays in domestic arable farming sown crop (weed) vegetation is among the leaders as to the harmfulness for agricultural crop yields. Weeds are an annually acting factor which reduces the yields of economically-valuable output in all the regions of Ukraine. It is to be mentioned that in arable farming of the country the yield losses of agricultural crops, caused by weeds, are growing constantly. A serious decrease of public target financing to protect cultivated crops from pests, diseases and weeds resulted in the violation of farm practices in agricultural crop cultivation all over the country. Namely, in all agro-climatic regions of Ukraine well-balanced scientifically-grounded crop rotations were reduced to 3–4 field rotations with 60–70 % share of grain crops. And, without a proper expertise of the farm machinery available at the farms, minimal or zero tillage is used. For example, reduced crop rotation with a dominating share of grain crops led to the increased load, caused by annul application of the same herbicides, and this, in turn, resulted in the appearance of resistant weed kinds in agro-phytocoenoses; the term violation of the weed control measures (first of all, chemical thinning) caused the decrease of their efficiency, particularly in controlling root-sprout weeds. Secondly, climate warming resulted in the increase of weed infestation of the agricultural crop fields due to the fact that most of the weeds survived during winter time and those typical for southern regions moved to the north (barnyard grass, amaranth, nightshade black, milkweed sharp, mallow runty and others). At the same the migration of northern kinds to the south was not recorded. One of the leading measures to regulate a weed component in agro-phytocoenoses is mechanical tillage. The updated tillage system has to be based on the principles of minimization which envisage the reduction of a mechanical effect on the soil aimed at the increasing of its erosion resistance and the optimization of soil fertility indicators. One of the ways to minimize mechanical tillage is to substitute moldboard tillage for mould boardless one, and also to decrease its depth and the number of cultivations. Purpose of the research is to estimate weed infestation of the fodder beet fields when various tillage systems are used. The experiments in five-field crop rotation were carried out in accordance with the theme of the research in a stationary field trial of SPC of Bila Tserkva NAU in 2009-2011. Four systems of tillage were studied. Three-fold replication and compact placing of replications are used; plots of the first order (tillage) are placed in one layer, gradually, systematically. Farm practices of fodder beet cultivation, used in the experiment, are typical to the ones applied in the research institutions and at the advanced farms of the zone. Machines, equipment and mechanisms, which are available at SPC BTsNAU and advanced farm enterprises are equipped with, are used when growing fodder beets. The methodology and organization of the technique of performing the trial facilitated this. Lowing at 30-32 cm depth was done with plow PLN –3–35, mouldboardless tillage – at 30–32 cm depth with subsurface cultivator KPG –250, shelling – at 10–12 cm depth with stubble plow PL – 5–25 and disc harrow BDV –3.0. The largest amount of weed raw mass was recorded under regular tillage with a subsurface cultivator. When differentiated and continuous shallow tillage was done, this indicator was the highest, as compared with the control, in the first year of the trial, and a reverse regularity was recorded in the last year of the trial. The raw mass of one sown crop was the highest under regular mouldboardless tillage, and under differentiated and continuous shallow tillage it was at the level of the control. Under continuous mouldboard, regular mouldboardless, differentiated and continuous shallow tillage this indicator was 3.67; 4.06; 3.71 and 3.73 g in 2009 and 3.37; 3.82; 3.34 and 3.34 g in 2011. A determinative factor, which weed infestation of agricultural crop fields depends on in the period of their vegetation, is light condition of the soil surface in the field. The latter is determined by the peculiarities of plant morphology, their development and sowing practice. In the structure of weed infestation the highest percentage – 20.7 % belongs to amaranth, 15.7 %– to Setaria pumila, 14.2 % – to barnyard grassand 11.1 %– to quinoa white. Under the effect of crop rotation weed grouping is changed (succession) in the period of germination of fodder beets which is connected with both the effect of a forecrop and a fertilization system of crop rotations and variants of tillage. Key words: fodder beet (mangold), tillage system, structure of weed infestation, raw mass of weeds, succession.

Moving window growth-A method to characterize the dynamic growth of crops in the context of bird abundance dynamics with the example of Skylark (Alauda arvensis).

Agricultural field crops differ in their vegetation height, coverage, and temporal development, affecting the abundances of bird species, which are often used as bioindicators. Although this relationship has been observed, no significant methodology exists to describe the dynamics of field crop growth on a landscape scale in connection with the abundance of indicator bird species that allows meaningful interpretation of bird abundance data with respect to crop vegetation parameters during the breeding season. In a field observation program, we monitored 2,900 ha of agricultural landscape to represent both the crop growth processes and the bird abundances. We measured these two parameters in the study area, dominated by winter wheat, winter rapeseed, maize, and fallow fields, and adapted the moving window approach to a new method of “moving window growth” to describe the dynamic development of height and coverage of the crops over time. Simultaneously, Skylarks (Alauda arvensis) territorial behavior was measured concurrently on the same fields and crops. Their dynamic abundance was documented over the breeding season. To test the relationship between crop growth and development and bird abundance, we applied a generalized linear model (GLM) in two ways: (a) without differentiation of crop species and (b) with differentiation of crop species. We found significant relationships between bird abundance and vegetation height and coverage with respect to both individual parameters and their interactions, even without differentiation of the agricultural crops. In general, increasing vegetation height and coverage, especially the interaction, led to decreasing bird abundance values. The model quality increased significantly by including differentiation of specific crops as an explanatory variable indicating a non‐homogenous situation between crops. Separate models for individual crop species revealed larger differences in model quality with best and least goodness of fit values for fallow fields and winter rapeseed, respectively. Because of the clear interactions between bird abundance, type of field crop, and vegetation height and coverage, it follows that both habitat suitability assessments of arable fields and the definition of favorable vegetation structures for farmland birds should be crop species‐specific.

Continuous estimation of canopy leaf area index (LAI) and clumping index over broadleaf crop fields: An investigation of the PASTIS-57 instrument and smartphone applications

Automatic leaf area index (LAI) measurements are important for obtaining sufficient amounts of field data over an extended period of time. A seasonal field campaign was carried out to obtain continuous LAI measurements over maize, soybean, and sorghum fields in northeast China in 2016. Field LAI measurements were acquired with the automatic PASTIS-57 (PAI Autonomous System from Transmittance Instantaneous Sensed from 57°) instrument and two smartphone applications, PocketLAI and LAISmart. These measurements were compared with data obtained using the LAI-2200 Plant Canopy Analyzer, digital hemispherical photography (DHP), and destructive sampling measurements.The effective plant area index (PAIeff) estimates from LAI-2200 and DHP are consistent over the season, with the overall relative errors (RE) of less than 5%. The PASTIS-57 data exhibit a small underestimation of the LAI-2200 and DHP values (RE < 20%). The relative errors for the LAISmart data are between −20% and −30%. PocketLAI significantly underestimates the LAI-2200 values (RE > 40%) and saturates at around PAIeff = 3.5. The canopy clumping index (CI) exhibits an S-shaped seasonal variation that decreases with the increase of PAIeff during the vegetative growth stage but increases after this stage. PASTIS-57 shows great potential for obtaining continuous LAI measurements in agricultural crop fields, but the smartphone applications should be further examined before they can be used for research purposes. The data collected in this study are valuable for the validation of remote sensing products.

Stable isotope and DNA analyses reveal the spatial distribution of crop‐foraging brown bears

We investigated maize (Zea mays) consumption by brown bears (Ursus arctos) in a subpopulation of eastern Hokkaido, Japan, using carbon stable isotope analysis of hair samples to better understand the pattern and scale of movement of the bears’ crop-foraging behavior. We identified a total of 30 individuals (19 males and 11 females) using DNA analysis of the hair samples. Stable isotope analysis suggested that female bears that were more likely to consume maize lived closer to agricultural crop fields. Our statistical model also estimated that the relative maize consumption of female bears decreased rapidly as the distance to crop fields increased to 3–4 km, similar to the radius of the annual home range of female bears. In contrast, male bears that were more likely to consume maize lived both in forested areas and near crop fields. Even if male bears live in a forested area except during the maize harvest period, they appeared to be at risk of being killed by lethal control because they can move from forested areas to crop fields during the maize harvest period. Collecting hair samples of many bears in the population over several years may allow the effect of lethal control on population viability to be estimated non-invasively.

Impact of conservation land management practices on soil microbial function in an agricultural watershed

The USDA Conservation Reserve Program (CRP) involves removing agricultural land from production and replanting with native vegetation for the purpose of reducing agriculture9s impact on the environment. In 2002, part of the Beasley Lake watershed in the Mississippi Delta was enrolled in CRP. In addition, areas between the lake and agricultural row crop (RC) fields were established as vegetative buffers (VB) to provide habitat for wildlife. Although the VB were established to improve wildlife habitat, an additional ecosystem service might include serving as an impediment to runoff from adjacent upland areas. The purpose of the current study was to assess the long-term impact of CRP, VB, and RC land management practices on the soil microbial community as an indicator of soil health. Soil samples were collected at two depths (0 to 5 and 5 to 15 cm [0 to 1.97 and 1.97 to 5.91 in]) from 12 sites within each land management (CRP, VB, and RC) area. Samples were assayed for soil enzyme activities (phosphatase, β-glucosidase, N-acetylglucosaminidase [NAGase], and fluorescein diacetate [FDA] hydrolysis) and microbial biomass. All enzyme activities were significantly higher in CRP and VB than in RC soils in the 0 to 5 cm depth. Microbial biomass in 0 to 5 cm soil was higher in CRP than in VB or RC areas. Significant correlations between microbial biomass carbon (C) and the activities of phosphatase (R2 = 0.514; p

Enhancing nitrogen fixation in soybean (Glycine max L.) by composting under saline stress condition

Soybean is susceptible to saline stress and reduced yield loss. Organic fertilization has several beneficial effects on agricultural crop fields. To minimize the deleteri­ous effects of the salinity organic fertilization is using which can provide economic yield under saline conditions. Therefore, a greenhouse research was conducted at the Department of Environmental Dynamics and Management, Hiroshima University, Japan to find out the role of compost in alleviating the adverse effects of salinity stress on soybean. The nitrogen-fixing activity was estimated by using gas chromatograph. The results revealed that salinity stress reduced the biological nitrogen fixation and specific nodule activity. Our results show that, there was a corresponding improve in nitrogen fixation with compost application.

Soil Moisture Retrieval in Agricultural Fields Using Adaptive Model-Based Polarimetric Decomposition of SAR Data

The aim of this paper was to estimate soil moisture in agricultural crop fields from fully polarimetric L-band synthetic aperture radar (SAR) data through the polarimetric decomposition of the SAR coherency matrix. A nonnegative-eigenvalue-decomposition scheme, together with an adaptive volume scattering model, is extended to an adaptive model-based decomposition (MBD) (Adaptive MBD) model for soil moisture retrieval. The Adaptive MBD can ensure nonnegative decomposed scattering components and allows two parameters (i.e., the mean orientation angle and a degree of randomness) to be determined to characterize the volume scattering. Its performance was tested using airborne SAR data and coincident ground measurements collected over agricultural fields in southeastern Australia and compared with previous MBD methods (i.e., the Freeman three-component decomposition using the extended Bragg model, the Yamaguchi three-component decomposition, and an iterative generalized hybrid decomposition). The results obtained with the newly proposed decomposition scheme agreed well with expectations based on observed plant structure and biomass levels. The new method was superior in tracking soil moisture dynamics with respect to previous decomposition methods in our study area, with root-mean-square error of soil moisture estimations being 0.10 and 0.14 m3/m3, respectively, for surface and double-bounce components. However, large variability in the achieved soil moisture accuracy was observed, depending on the presence of row structures in the underlying soil surface.

Potential of Biological Agents in Decontamination of Agricultural Soil.

Pesticides are widely used for the control of weeds, diseases, and pests of cultivated plants all over the world, mainly since the period after the Second World War. The use of pesticides is very extensive to control harm of pests all over the globe. Persistent nature of most of the synthetic pesticides causes serious environmental concerns. Decontamination of these hazardous chemicals is very essential. This review paper elaborates the potential of various biological agents in decontamination of agricultural soils. The agricultural crop fields are contaminated by the periodic applications of pesticides. Biodegradation is an ecofriendly, cost-effective, highly efficient approach compared to the physical and chemical methods which are expensive as well as unfriendly towards environment. Biodegradation is sensitive to the concentration levels of hydrogen peroxide and nitrogen along with microbial community, temperature, and pH changes. Experimental work for optimum conditions at lab scale can provide very fruitful results about specific bacterial, fungal strains. This study revealed an upper hand of bioremediation over physicochemical approaches. Further studies should be carried out to understand mechanisms of biotransformation.