Agricultural Fields Research Articles

A soil moisture experiment for validating high-resolution satellite products and monitoring irrigation at agricultural field scale

Validating the satellite soil moisture products is always an active research topic for the application of the products and improvement of the retrieval algorithms, attracting extensive attention. Nevertheless, seldom existing validation activities focus on the validation of high-resolution soil moisture products at the fine scale. To this end, an experiment was conducted in the middle stream of the Heihe River Basin in northwestern China in August to October of 2021, aiming to validate high-resolution satellite remote sensing products of soil moisture. The paper introduces the design, composite, and preliminary results of the experiment. A soil moisture observation network was established with two kinds of sensors (CS616 and Stevens Hydra Probe) validated against soil core measurements. Several synchronized campaigns were performed, and data were collected to validate the SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 and 1 km EASE-Grid Soil Moisture (SPL2SMAP_S) products. Besides, an optical trapezoid model (OPTRAM) and collected Sentinel-2 data were applied to estimate soil moisture and to map irrigated area. Preliminary analyses show that: 1) Steven probes perform best, with an RMSE = 0.040 m3m−3 and ubRMSE=0.034 m3m−3; 2) Both the SPL2SMAP_S products at 3 km and 1 km show large RMSE (0.128 m3m−3 for 3 km and 0.158 m3m−3 for 1 km) and ubRMSE (0.115 m3m−3 for 3 km and 0.158 m3m−3 for 1 km); 3) The OPTRAM retrievals over bare surface present relatively smaller RMSE (0.06 m3m−3) and ubRMSE (0.057 m3m−3), while retrievals over vegetated croplands present a relatively large RMSE/ubRMSE (0.083/0.083 m3m−3), and the retrievals can identify the irrigated area at field scale. Overall, the experiment provides fruitful methodologies and datasets for the validation of high-resolution remote sensing products, benefiting the development and improvement of soil moisture retrieval algorithms and products to support irrigation scheduling and management at a precision agricultural scale in the future.

Semantic Segmentation and Classification of Active and Abandoned Agricultural Fields through Deep Learning in the Southern Peruvian Andes

The monumental scale agricultural infrastructure systems built by Andean peoples during pre-Hispanic times have enabled intensive agriculture in the high-relief, arid/semi-arid landscape of the Southern Peruvian Andes. Large tracts of these labor-intensive systems have been abandoned, however, owing in large measure to a range of demographic, economic, and political crises precipitated by the Spanish invasion of the 16th century CE. This research seeks to better understand the dynamics of agricultural intensification and deintensification in the Andes by inventorying through the semantic segmentation of active and abandoned agricultural fields in satellite imagery across approximately 77,000 km2 of the Southern Peruvian Highlands. While manual digitization of agricultural fields in satellite imagery is time-consuming and labor-intensive, deep learning-based semantic segmentation makes it possible to map and classify en masse Andean agricultural infrastructure. Using high resolution satellite imagery, training and validation data were manually produced in distributed sample areas and were used to transfer-train a convolutional neural network for semantic segmentation. The resulting dataset was compared to manual surveys of the region and results suggest that deep learning can generate larger and more accurate datasets than those generated by hand.

From needs to policy action: Italian agri-food districts as a case of territorial cooperation

In the context of ecological and social challenges in global food systems, this study investigates the potential of agri-food districts to foster balanced territorial development. A multi-step approach to developing sustainable agri-food districts is outlined. How these districts, as integrated systems and meso-level organizational forms, can enhance sustainability through governance is then assessed. This research uses a context-driven analysis pathway involving stakeholder participation and needs identification. The theoretical background, the Italian regulatory framework, and a case study from Lombardy are presented. Needs are identified through participatory approaches and actions are prioritized using desk research and a narrative SWOT analysis combined with key stakeholder discussions (focus group). A total of eighteen needs are identified and categorized into 3 dimensions of sustainability: economic, environmental, and socio-institutional. Findings indicate that agri-food district organization has great potential to help achieve local and regional policy goals in line with the shift to sustainable approaches in the Common Agricultural Policy (CAP). The paper proposes actions to strengthen district capacity-building, focusing on internal governance and sustainable production chains. Additionally, initiatives to attract young people to rural areas and agreements for ecosystem services in agri-food districts are suggested. These actions aim to promote sustainability and competitiveness while addressing challenges related to governance, innovation, branding, demographics, and environment. In conclusion, the study prompts critical inquiry into governance models and system dynamics. The innovative aspects of this study lie in its methodological approach, integration of theory and practice, holistic perspective, policy relevance, and critical inquiry, which collectively contribute to advancing knowledge and understanding in the field of sustainable agriculture and territorial development.

Accumulation of Cd and Pb in soils irrigated with sewage and non-sewage water in Prayagraj, India

Unmanaged agricultural fields and the extended usage of sewage irrigation water can raise the concentration of heavy metals (Cd and Pb) in the soil, turning it into barren soil. The study was conducted during 2022-23 at four sewage and non-sewage irrigated sites in Prayagraj namely Naini, Baxibandh, Rasulabad and Draupadi Ghat. The representative soil samples were taken at depths of 0-15, 15-30, 30-45 and 45-60 cm. Di-acid mixture method was used for the estimation of heavy metals (Cd and Pb) in the soil sample by AAS (Atomic Absorption Spectrophotometer) (AAnalyst600, Perkin Elmer Inc., MA, USA). Contamination factor (CF) and enrichment factor % (EF) of different sites showed various results which fulfill the basic criteria of present and future prospective of research. The highest concentration of Cd and Pb was found in the surface layer (0-15 cm) of sewage and non-sewage irrigated sites at Naini i.e., Cd (4.46±0.42 and 1.05±0.08 mg/kg) and Pb (6.53±0.62 and 1.18±0.12 mg/kg) respectively. The lowest concentration of Cd and Pb was found in the sub-surface layer (45-60 cm) of sewage and non-sewage irrigated sites at Draupadi Ghat i.e., Cd (1.02±0.09 and 0.16±0.01 mg/kg) and Pb (1.68±0.12 and 0.18±0.01 mg/kg) respectively. The concentration of heavy metals cadmium (Cd) and lead (Pb) declined with soil profile depth in both sewage and non-sewage irrigated soils. The long-term persistence of heavy metal contamination in soil can lead to severe issues; therefore, regular monitoring of sewage-irrigated soils is necessary to minimize heavy metal pollution.

Farm Monitoring System with Drones and Optical Camera Communication.

Drones have been attracting significant attention in the field of agriculture. They can be used for various tasks such as spraying pesticides, monitoring pests, and assessing crop growth. Sensors are also widely used in agriculture to monitor environmental parameters such as soil moisture and temperature. Due to the high cost of communication infrastructure and radio-wave modules, the adoption of high-density sensing systems in agriculture is limited. To address this issue, we propose an agricultural sensor network system using drones and Optical Camera Communication (OCC). The idea is to transmit sensor data from LED panels mounted on sensor nodes and receive the data using a drone-mounted camera. This enables high-density sensing at low cost and can be deployed in areas with underdeveloped infrastructure and radio silence. We propose a trajectory control algorithm for the receiving drone to efficiently collect the sensor data. From computer simulations, we confirmed that the proposed algorithm reduces total flight time by 30% compared to a shortest-path algorithm. We also conducted a preliminary experiment at a leaf mustard farm in Kamitonda-cho, Wakayama, Japan, to demonstrate the effectiveness of the proposed system. We collected 5178 images of LED panels with a drone-mounted camera to train YOLOv5 for object detection. With simple On-Off Keying (OOK) modulation, we achieved sufficiently low bit error rates (BERs) under 10-3 in the real-world environment. The experimental results show that the proposed system is applicable for drone-based sensor data collection in agriculture.

Smart Farming System Using Sensors for Agricultural Task Automation

The Automation the agricultural system is very useful for old people and normal persons who live far away from the agricultural field. If installed and programmed properly, automatic agricultural systems can even save us money and help in water conservation. In this project focused on atomizing the irrigation system for social welfare of Indian agricultural system and also to provide perfect irrigation in particular area. Soil moisture sensor sense the condition of the soil whether it is dry or wet and sends the information to microcontroller. Water level sensor senses the water level in the water source and sends the information to the microcontroller. Microcontroller monitoring the information to the relay then on/off of the motor is done. Key Word: GSM, Arduino, AT Mega, PH Sensor, Soil Sensor, Temperature Sensor.

Effect of Pruning on the Growth and Yield of Indeterminate and Determinate Tomato Variety in the Northern Guinea Savanna of Nigeria

Pruning is a management practice that helps to increase fruit yield and quality of fruit, better aeration, better exposure of foliage to sunlight and photosynthetic. Pruning is not a common practice among tomato growers in Northern Guinea Savanna of Nigeria and most of the farmers have no idea about it. Field trials were conducted concurrently during the dry season of 2020 on the Research Farms of the Institute for Agricultural Research, Samaru (11o 11ꞌN, 07o 38ꞌE 686 m above sea level) and Farmers field in Kujama, (9o 34ꞌN, 8 o 18ꞌE 740m above sea level) in the Northern Guinea Savanna Ecological Zones of Nigeria; to determine the effect of pruning on the growth and yield of indeterminate and determinate hybrid tomato varieties. The experiment consisted of 12 treatments comprising of three pruning techniques (no-pruning, pruning on one stem and two stems) and four varieties of tomato (1 indeterminate hybrid tomato; Larisa F1, 2 determinate hybrid Delta F1, and Platinum F1), and one determinate open pollinated variety (UC82B). The four varieties of tomato with three pruning techniques were factorially combined and laid out in a randomized complete block design with three replicates. The result at both locations showed varietal significant differences on growth parameters such as higher plant height, shoot dry weight, leaf area index, crop growth rate, relative crop growth rate, and yield attribute such as fruit diameter, fruits weight per plant, where Larisa F1 performed better than other varieties. Pruning tomato plant to two- stem and one-stem significantly increased growth parameters where two-stem pruned plants performed best. Variety × pruning interaction on total fresh fruit yield indicated that the combination of Larisa F1 with two-stem was found suitable for maximum fruit yield though at par with Larisa F1 one-stem and unpruned.

Field efficacy of urease inhibitors for mitigation of ammonia emissions in agricultural field settings: a systematic review

Globally, ammonia (NH3) is one of the key air pollutants and reducing NH3 emissions and the associated indirect emission of the greenhouse gas nitrous oxide remains challenging for the agricultural sector. During the past three decades, a number of urease inhibitors have been placed on the market with the goal of reducing NH3 loss from urea containing fertilisers. N–(n-butyl) thiophosphoric triamide (NBPT), N–(2-nitrophenyl) phosphoric triamide (2-NPT), a 3:1 ratio of NBPT + N-(n-propyl) thiophosphoric triamide (NPPT) and the maleic and itaconic acid co-polymer (MIP) are registered urease inhibitors under the European Commission Fertilising Products Regulation (FPR). However, the availability of several inhibitor options has raised questions from farmers, policymakers and emissions inventory compiling authorities regarding the field efficacy of the different options available for reducing NH3 loss. Despite many disparate NH3 field studies existing for NBPT, 2-NPT, NBPT + NPPT and MIP there is presently no review that brings these results together, a significant and important knowledge gap. This review addresses the gap by summarising the published field trial literature on NH3 volatilisation mitigation offered by NBPT, 2-NPT, NBPT + NPPT and MIP. Our review identified 48 peer reviewed studies where NH3 loss mitigation was measured in a field setting, giving 256 replicated comparisons. The synthesised literature results revealed that NBPT + NPPT reduced NH3 loss by 75% (95% CI = 58–82% n = 32), 2-NPT reduced NH3 loss by 70% (95% CI = 63–76% n = 19) and NBPT reduced NH3 loss by 61% (95% CI = 57–64% n = 165), giving on average a 69% reduction by these three urease inhibitors. In contrast, MIP increased NH3 loss by 0.3% on average (95% CI = −8–9% n = 40). The results presented in this review broaden the understanding of urease inhibitor efficacy in field conditions and demonstrate that not all products behave the same in terms of field NH3 reduction efficacy. This review is important for farmers, policymakers, emission inventory compilers and other stakeholders.

Effects of mulch films with different thicknesses on the microbial community of tobacco rhizosphere soil in Yunnan laterite.

The mulch film (MF) management model of the agricultural field affects the physical and chemical properties of soil (PCPS) and the structure of the microorganism community; however, studies on the relationship between the rhizosphere microorganism community structure and the thickness of MF are still limited. To understand the interactions among the MF thickness, PCPS, and rhizosphere microorganism, a study was conducted by using an integrated metagenomic strategy, where tobacco rhizosphere soil was treated with four commonly representative and used thicknesses of MFs (0.004, 0.006, 0.008, and 0.010 mm) in Yunnan laterite. The results showed that agronomic traits such as the tobacco plant height (TPH), leaf number (LN), fresh leaf weight (FLW), and dry leaf weight (DLW) were significantly (p < 0.01) improved in the field mulched with the thickest film (0.010 mm) compared with the exposed field (CK), and there was a 6.81 and 5.54% increase in the FLW and TPH, separately. The correlation analyses revealed a significant positive correlation of the MF thickness with the soil water content (SWC), soil organic matter (SOM), total nitrogen (TN), available nitrogen (AN), total phosphorus (TP), and available phosphorus (AP; all p < 0.01), while the MF thickness was negatively correlated with the soil temperature (ST; p < 0.01). In addition, the community structure of the rhizosphere soil bacteria was significantly changed overall by the MF thickness, which also interfered with the function of the rhizosphere soil bacteria. The correlation analyses also showed that the abundance of Bradyrhizobium and Nitrospira was positively correlated with the MF thickness, while the abundance of Sphinsinomonas and Massilia was negatively correlated with it. This indicated that with the increase of the MF thickness, the ability of the rhizosphere soil to utilize N and remove harmful molecules was strengthened, while the capacity of the rhizosphere soil to degrade pollutants was greatly reduced. These findings provide additional insights into the potential risks of the application of different thicknesses of MFs, particularly concerning the PCPS and soil microbial communities.

Local and landscape predictors of herbivory on subsistence croplands in Brazilian dry forests

ABSTRACT Understanding the impact of herbivores on croplands is critical to developing sustainable agricultural landscapes. Here, we test the hypothesis that field size and landscape structure modulate the levels of herbivory in cowpea and maize subsistence croplands in Brazilian drylands. We conducted the study in nine small-scale farms with varying levels of landscape structure and field sizes (0.10–1.67 ha). In each farm, we haphazardly sampled 20 plants of maize and cowpea. The frequency of foliar herbivory was high for both crops (0.45–0.72), but the average magnitude of damage was low (<3% of leaf surface in both crops), indicating that herbivory is not an ecosystem disservice. Nonetheless, the field size and level of human impact on the landscape matrix were positively related to the frequency and magnitude of damage on maize foliage and cowpea foliage and pods. Landscape composition (proportion of woodland/forest surrounding the field) and configuration (number of surrounding woodland/forest patches) had weaker effects on herbivory levels, mixing positive and negative effects on both crops. Our findings indicate that herbivory attack is not concerning in the agricultural fields studied, but suggest that maize and cowpea should be preferably grown in small fields surrounded by an environmental-friendly matrix and patches of conserved ecosystems.

Analysis of plant physiological responses based on leaf color changes through the development and application of a wireless plant sensor

Optical sensing has been used to monitor the physiological responses of plants noninvasively and in real-time. In this study, we developed a low-cost plant sensor that performed a spectroscopic measurement at eight wavelengths in the visible region. The sensor head of the system was attached directly to the underside of the leaf, not blocking the light, and eliminating correction work because of the constant distance between the sensor head and the sample. The collected data was shared in the cloud via a network, thereby enabling remote monitoring. The characteristics of the plant sensor as a spectral photometer were validated, with major wavelengths also showing good correlations with those of a conventional spectrometer. The reflectance of 620 nm in this sensor detected plant aging indicator chlorophyll, and 550 nm detected stress indicator xanthophyll. In the field test, these plant physiological responses, seasonal leaf color changes and environmental stresses, were observed remotely. The results indicate that the novel spectroscopic measurement from the underside of the leaf is effective to realize accurate and stable measurement of the plant leaf. The plant sensor can be a powerful tool in the field of agriculture and ecological study by realizing simultaneous, multi-point and remote monitoring at a low cost.

Soil moisture content estimation of drip-irrigated citrus orchard based on UAV images and machine learning algorithm in Southwest China

Soil moisture content (SMC), as a pivotal component in the energy and matter exchange processes within the soil-plant-atmosphere continuum, plays a crucial role in surface water dynamics, energy fluxes, and carbon cycling within ecosystems. The development of remote sensing technology has offered new perspectives for monitoring soil moisture at regional scales. Unmanned aerial vehicles (UAV) equip with multispectral have distinct advantages for vegetation monitoring, including rapidity and cost-effectiveness, which has superior applicability and practicality. Therefore, in a 5a "Daya" late-maturing citrus orchard, the vegetation index (VI) and texture feature (TF) information of citrus canopy based on UAV multi-spectral images were extracted, and soil and plant analyzer development (SPAD) of citrus was collected. These different data sources were integrated into the framework of the random forest algorithm (RF) and genetic algorithm-optimized random forest (GA-RF) to evaluate the accuracy of surface SMC (SSMC) estimation in citrus orchard. The Biswas model was utilized to simulate the root zone SMC (RSMC). The spatiotemporal variations of SMC in citrus orchard were analyzed, and the potential of low-cost sensor-equipped drones in rapidly acquiring spatial and temporal distribution information of SMC at a large regional scale was explored. The results indicated that the GA-RF models outperformed the RF models in estimating citrus orchard SMC (with R2 ranging from 0.502 to 0.949 and RMSE ranging from 0.552 % to 3.166 % for GA-RF, compared to R2 ranging from 0.430 to 0.936 and RMSE ranging from 0.587 % to 3.449 % for the RF). The GA-RF models using VI+SPAD as inputs exhibited the best performance for SMC at depths of 5 cm, 10 cm, 20 cm and 40 cm (SMC5, SMC10, SMC20 and SMC40) across citrus growth stages (R2 ranging from 0.793 to 0.949 at 5 cm, R2 ranging from 0.702 to 0.938 at 10 cm, R2 ranging from 0.714 to 0.927 at 20 cm). In bud bust to flowering, young fruit and fruit maturation stages (stage Ⅰ, ⅠⅠ and ⅠⅤ), all models demonstrated good accuracy in estimating SMC at depth of 10 cm (R2 ranging from 0.567 to 0.908 in stage Ⅰ, with R2 ranging from 0.681 to 0.916 in stage ⅠⅠ and R2 ranging from 0.579 to 0.938 in stage ⅠⅤ). In fruit expansion stage (stage III), the models performed best in predicting SMC5 (R2 ranging from 0.698 to 0.861). The Biswas model was constructed to simulate SMC40 by utilizing the inverted SMC10 and SMC20, thereby generating spatiotemporal distribution maps of SMC at different depths in citrus orchard. The SSMC was susceptible to environmental factors, exhibiting significant spatiotemporal heterogeneity. In summary, this study illustrated that the integration of multiple data sources into GA-RF enhanced the estimation performance of SMC at different growth stages of late-maturing citrus orchard in the Southwest China. Additionally, it enabled the rapid and efficient monitoring of spatiotemporal variations in SMC, providing an effective method and practical foundation for precision irrigation and improved water use efficiency.

Estimation of top soil properties by Sentinel-2 imaging

ABSTRACT This study evaluated the feasibility of using free multispectral remote sensing data from Sentinel-2A satellites to predict soil properties in Northern Karnataka, India. Sentinel-2A images were downloaded for selected sites, covering Vertisol, Ultisol, and Alfisol soils. Multiple linear regression (MLR) models incorporated four Sentinel-2 bands and six spectral indices (NDVI, GNDVI, SAVI, TVI, EVI, and BI) as independent variables, with soil properties as dependent variables. Surface samples (0–15 cm depth) were collected from March to May 2022. The analysis showed significant correlations between individual bands and soil properties, with variations in Organic Carbon (OC) compared to sand, silt, clay, and pH. Sand positively correlated with all spectral indices, while silt, clay, and pH were negatively correlated. The red and Near-Infrared (NIR) bands showed a non-significant relationship with OC. No significant correlation was found between EVI and the soil properties. Strong regression coefficients were observed between Sentinel-2 predictions and laboratory measurements: sand (r² = 0.63), silt (r² = 0.73), clay (r² = 0.59), and pH (r² = 0.59). These results demonstrate the potential of Sentinel-2 data for predicting soil properties, offering a valuable tool for managing unsampled agricultural fields.

Study of the Effects of Bioactive Compounds of Cyanobacterium Desmonostoc alborizicum on Pathogenic Fungi of Wheat

Wheat, as one of the most economically important crops, constitutes a major part of the human diet. One of the major challenges in wheat preservation is combating various pests, including fungi, through different pesticides. Due to the toxicity caused in agricultural fields by chemical pesticides, there is a growing inclination towards using biopesticides instead. These biopesticides not only possess antimicrobial properties but also aid in the growth and development of crops. In this context, the bioactive compounds present in cyanobacteria are considered as potential biopesticide candidates. Therefore, the aim of this study is to observe the antifungal effect of bioactive compounds from the cyanobacterium Desmonostoc alborizicum on pathogenic fungi affecting wheat. For this purpose, after culturing the cyanobacterial strain Desmonostoc alborizicum for 14 days, the cyanobacterial extract was applied to wheat plants infected with the fungi Alternaria alternata, Fusarium oxysporum, Aspergillus terreus, and Phytophthora nicotiana var. The activity of antioxidant enzymes and MTT assay on 4T1 cells were then evaluated. The results showed that Aspergillus terreus exhibited the highest resistance, while Fusarium oxysporum showed the highest sensitivity to the cyanobacterial extract of Desmonostoc alborizicum. The activity of the enzymes guaiacol peroxidase, superoxide dismutase, catalase, and glutathione peroxidase in infected plants treated with the cyanobacterial extract significantly decreased (p&lt;0.05), indicating effective stress control and enhanced immune response in these treatments. Therefore, the results of this study suggest that the use of Desmonostoc alborizicum extract can be effective as an antifungal agent in protecting wheat and other agricultural crops.

Ecology of arbuscular mycorrhizal association in coconut (Cocos nucifera L.) palms: Analysis of factors influencing AMF in fields

This study is the first thorough ecological analysis of arbuscular mycorrhizal fungal (AMF) diversity in randomly selected traditional coconut fields across Kerala, South India. We conducted a critical analysis of AMF diversity, percentage root length colonization (PRLC), and mean spore density (MSD) across 248 sites, taking into account variations in plant, environmental, and soil factors like coconut varieties, palm health conditions, agroclimatic zones, soil types, and seasons in the region. A total of 23 AMF species from seven genera (Acaulospora, Archaeospora, Funneliformis, Glomus, Sclerocystis, Septoglomus, and Scutellospora) were identified, with Acaulospora scrobiculata being the dominant species in all studied fields. A critical analysis of diversity indices, including the Shannon-Weiner Index, Simpson's diversity index, and Gini-Simpson index, concerning variables indicated that soil series influences AMF diversity in specific fields. Correlational and principal component analyses highlighted the interrelationships between specific soil types and quality parameters affecting AMF characteristics, underscoring their crucial role in coconut palm growth. The study also revealed the ecological amplitudes of indigenous AMF species related to specific soil fertility parameters. Overall, this research serves as a model for identifying root- and soil-specific AMF in agricultural fields and provides valuable ecological insights for utilizing indigenous AMF species as ecotechnological tools for sustainable coconut cultivation.

Emerging patterns in rodent-borne zoonotic diseases

Rodents are ubiquitous and typically unwelcome dwellers in human habitats worldwide, infesting homes, farm fields, and agricultural stores and potentially shedding disease-causing microbes into the most human-occupied of spaces. Of the vertebrate animal taxa that share pathogens with us, rodents are the most abundant and diverse, with hundreds of species of confirmed zoonotic hosts, some of which have nearly global distributions. However, only 12% of rodent species are known to be sources of pathogens that also infect people, and those rodents that do are now recognized as tending to share a suite of predictable traits. Here, we characterize those traits and explore them in the context of three emerging or reemerging rodent-borne zoonotic diseases of people: Lassa fever, Lyme disease, and plague.

Assessing the Heavy Metal Contamination Prevailing in Groundwater at Rishipur Village, West Bengal, India

The recent surge in interest surrounding water contamination has prompted a study to evaluate the water quality of groundwater in Rishipur village, Malda District, West Bengal, India. The area's groundwater is crucial for drinking, residential, and irrigation purposes due to its proximity to agricultural fields, where most local residents live. The intended purpose of the study is to comprehensively appraise the groundwater quality of a dug well, specifically analysing the intensities of different heavy-metals existing in the water, resulting in valuable insights into its quality. The native residents rely on the usage of this groundwater for drinking and domestic purposes without any purification. However, potential contamination may be present due to nearby agricultural activities. Thus, the study will enable us to make recommendations for mitigating any identified contamination. Exemplary samples of water from a well were consistently collected near the agricultural region over the course of a year, from April 2021 to March 2022. The groundwater samples were analysed for the presence of heavy elements: iron; zinc; copper; manganese; nickel; chromium; cobalt and lead to evaluate the water excellence. To ascertain the overall water eminence status, the Heavy-Metal-Pollution-Index; (HPI), was calculated, which is considered an efficient and reliable technique for water eminence assessment. In this study, the value of HPI; 101.66 in January, indicates high heavy-metal pollution, although it remained within the desired range for the rest of the months. In the analysis of the descriptive statistical data, both substantial positive and negative relations were observed, which were illustrated in a correlation coefficient matrix. Furthermore, the interaction of heavy-metal variables was also explored by utilising the R-Square values derived from the multiple linear regression analysis, which allowed to gain insights into how these variables interplayed with one another. The investigation conclusively established that the eminence of water is deemed appropriate for consumption alongside other domiciliary purposes, with the exception of January.

Perception of Farmers towards M-Kisan Initiative

India is an agrarian country and risk and uncertainties are omnipresent in the field of agriculture. The farmer’s exposure to risk and uncertainty is often provoked by lack of information about required inputs and crop management practices etc. This information gap has adverse impact on production as well as the income of farmers. As use of ICT has strongly influenced every socio-economic aspect to become the most significant motivation of the socio-economic development and poverty reduction, Government of India launched m-Kisan portal to deliver updated information to the farmers through SMS. It is an initiative by the Indian government to provide agricultural services and information to farmers through mobile phones. The mKisan platform allows farmers to receive updates on agricultural practices, weather forecasts, market prices, pest control, and other farming-related advice via SMS, voice messages, and mobile apps. The present study tries to identify the utility perception of the m-kisan users. Data were collected from 120 respondents, from four villages of three different talukas of Bhavnagar district through personal interview schedule during 2022 and then after data were compiled, tabulated and analysed to get proper answer for the specific objectives of the study. Slightly more than half (51.67%) of the respondents had medium utility perception.

Identifying sources and distribution of organic pollutants in a Moroccan river: Characterization of dissolved organic matter by absorption, excitation–emission fluorescence and chemometric analyses

This study investigates surface water contamination of Ben-Kazza River in Morocco, fed by effluents from an adjacent lagoon-based wastewater treatment plant (WWTP) and seasonally by industrial effluents, and which occasionally serves to irrigate agricultural fields. This study has two purpose: i) to track the main sources of contamination through the evolution of dissolved organic matter (DOM) characteristics along the watercourse, and ii) to characterize the WWTP influents and effluents with a focus on the efficiency of the lagoon treatment. We characterized a total of 495 water samples across the watercourse and from the inlet and outlet of the WWTP, using UV–visible absorption and excitation–emission fluorescence coupled with chemometric analyses. Absorption indicators and fluorescence indices were calculated and compared across sampling points. Results highlight spatial shifts together with temporal changes in DOM. PARAFAC identified components that varied between protein-like, humic-like and anthropogenic-like fluorophores along the river, permitted to trace the anthropogenic components and their sources. The lagoon treatment appeared to better remove fresh organic material than humic material: fluorescence intensity decreased by 68 % for peak T1 and by 22 % for peak C. Maximum fluorescence intensities (Fmax) decreased across all PARAFAC components, leading to more than 55 % reduction of ΣFmax.

Challenging the Atacama desert: Agronomic and water conditions for pre-Hispanic maize agriculture in hyper arid environments inferred by δ18O isotopes

During the Formative Period (ca.2400–950 years BP), pre-Hispanic farmers in Pampa del Tamarugal developed a complex agricultural system in the hyper-arid Atacama Desert in Tarapacá, northern Chile. This system involved numerous agricultural fields congregated near the Tarapacá Valley’s perennial stream and the Guatacondo ravine’s ephemeral stream. Well-established villages such as Caserones, Pircas, Ramaditas, and Guatacondo accompanied these developments. However, the importance of understanding the water sources has been neglected despite the efforts to understand the relationships between pre-Hispanic agriculture and their living environment. Thus, we presumed the use of local water sources without a clear understanding of their exploitation methods or the associated technological implications. Furthermore, there is limited research on groundwater use in Tarapacá. This research aims to understand water sources used in the cultivation of maize (Zea mays) using δ18O isotope values obtained from pre-Hispanic maize kernels in Tarapacá. We compared these values with published δ18O values of water sources and applied a generalized linear model (GLM) with a Gaussian distribution, performing a Tukey’s post hoc test for multiple comparisons of means with heteroscedasticity-consistent covariance estimation. The best-fit model was identified using a stepwise model selection procedure based on the Bayesian information criterion (BIC). Our results indicate that mean δ18O values of organic matter from maize kernels range from 24.73 ‰ to 31.65 ‰. The best performing model on δ18O only included Group (BIC=298.2) as the explanatory variable as Period, Site, and Weight had no effects. These findings point towards a significant statistical relationship between the δ18O values of organic matter derived from maize kernels and the specific geographic regions they originate from. These values also show an enrichment of δ18O isotope in Tarapacá samples, except for Pica 8. These results indicate diverse agricultural strategies that utilized different water sources including perennial flow in the Tarapacá River, ephemeral runoff at Guatacondo, and groundwater in the Pampa del Tamarugal. Also, due to the enrichment of δ18O, we suggest that the circular structures found among the fields were used as water reservoirs leading to the observed enrichment of δ18O isotope values. We discuss the role of ancient agriculture technology in water management, the role of maize, cultural strategies, and the relationships with their environment. In conclusion, pre-Hispanic farmers managed limited water sources successfully despite intermittent drought for hundreds of years until the 1970 s, when agriculture was abandoned due to multiple factors.