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Efficient and reliable corn (Zea mays L.) yield prediction is important for varietal selection by plant breeders and management decision-making by growers. Unlike prior studies that focus mainly on county-level or controlled laboratory-scale areas, this study targets a production-scale area, better representing real-world agricultural conditions and offering more practical relevance for farmers. Therefore, the objective of our study was to determine the best combination of vegetation indices and abiotic factors for predicting corn yield in a rain-fed, production-scale area, identify the most suitable corn growth stage for yield estimation using machine learning, and identify the most effective machine learning model for corn yield estimation. Our study used high-resolution (6 cm) aerial multispectral imagery. Sixty-two different predictors, including soil properties (sand, silt, and clay percentages), slope, spectral bands (red, green, blue, red-edge, NIR), vegetation indices (GNDRE, NDRE, TGI), color-space indices, and wavelengths were derived from the multispectral data collected at the seven (V4, V5, V6, V7, V9, V12, and V14/VT) growth stages of corn. Four regression and machine learning algorithms were evaluated for yield prediction: linear regression, random forest, extreme gradient boosting, and gradient boosting regressor. A total of 6865 yield values were used for model training and 1716 for validation. Results show that, using random forest method, the V14/VT stage had the best yield predictions (RMSE of 0.52 Mg/ha for a mean yield of 10.19 Mg/ha), and yield estimation at V6 stage was still feasible. We concluded that integrating abiotic factors, such as slope and soil properties, significantly improved model accuracy. Among vegetation indices, TGI, HUE, and GNDRE performed better. Results from this study can help farmers or crop consultants plan ahead for future logistics through enhanced early-season yield predictions and support farm profitability and sustainability. [ABSTRACT FROM AUTHOR]

Due to the constant increase in the number of plant diseases and the lack of available treatments, there has been a growing interest in plant extracts over the past few decades. Numerous studies suggest that plant extract molecules possess valuable antimicrobial activities, particularly against fungi and bacteria. This suggests that these biomaterials could potentially serve as attractive therapeutic options for the treatment of phytopathogen infections. In the present study, we investigated and analyzed the methanolic extract of Eryngium campestre L. whole plant extract using HPLC. The analysis revealed the presence of several polyphenolic constituents, with benzoic acid, catechol, quercetin, vanillic acid, resveratrol, naringenin, and quinol being the most abundant. The amounts of these constituents were determined to be 2135.53, 626.728, 579.048, 356.489, 323.41, 153.038, and 128.77 mg/kg, respectively. Furthermore, we isolated and identified different plant fungal and bacterial isolates from symptomatic potato plants, which were accessioned as Rhizoctonia solani (OQ880458), Fusarium oxysporum (OQ820156) and Fusarium solani (OQ891085), Ralstonia solanacearum (OQ878653), Dickeya solani (OQ878655), and Pectobacterium carotovorum (OQ878656). The antifungal activity of the extract was assessed using fungal growth inhibitions (FGI) at concentrations of 100, 200, and 300 µg/mL. The results showed that at the lowest concentration tested (100 µg/mL), the extract exhibited the highest effectiveness against R. solani with an FGI of 78.52%, while it was least effective against F. solani with an FGI of 61.85%. At the highest concentration tested, the extract demonstrated the highest effectiveness against R. solani and F. oxysporum, with FGIs of 88.89% and 77.04%, respectively. Additionally, the extract displayed a concentration-dependent inhibitory effect on all three bacterial pathogens. At the highest concentration tested (3000 µg/mL), the extract was able to inhibit the growth of all three bacterial pathogens, although the inhibition zone diameter varied. Among the bacterial pathogens, D. solani exhibited the highest sensitivity to the extract, as it showed the largest inhibition zone diameter at most of the extract concentrations. These findings highlight the potential of the E. campestre extract as a source of natural antimicrobial agents for controlling various plant pathogens. Consequently, it offers a safer alternative to the currently employed protective methods for plant disease management. [ABSTRACT FROM AUTHOR]

Soil fertility (SF) assessment is an important strategy for identifying agriculturally productive lands, particularly in areas that are vulnerable to climate change. This research focuses on detecting SF zones in Firozabad district, Uttar Pradesh, India, for agricultural purposes, so that they can be prioritized for future management using the fuzzy technique in the Arc GIS model-builder. The model computing technique was also deployed to determine the different fertility zones, considering 17 soil parameters. The derived fuzzy technique outperformed the traditional method of dividing the sampling sites into clusters to correlate soil fertility classes with the studied soil samples. The prioritization of the soil factors and a spatial analysis of the fertility areas were carried out using the Analytic Hierarchy Process (AHP) and GIS tools, respectively. The AHP analysis outcome indicated that hydraulic properties had the highest weighted value, followed by physical and chemical properties, regarding their influence on SF. The spatial distribution map of physico-chemical properties also clearly depicts the standard classification. A fuzzy priority map was implemented based on all the classes parameters to identify the five fertility classes of the soil, namely very high (0.05%); high (16.59%); medium (60.94%); low (22.34%); and very low (0.07% of total area). This study will be of significant value to planners and policymakers in the future planning and development of activities and schemes that aim to solve similar problems across the country. [ABSTRACT FROM AUTHOR]

Land abandonment is among the most complex la nd use change processes driven by a multiplicity of anthropogenic and natural factors, such as agricultural over-exploitation, implementation of agricultural policies, socio-economic and climatic aspects. Therefore, it is necessary to deepen the effects of land abandonment based on methodologies that are as multidisciplinary as possible. Environmental and social problems related to abandonment include soil erosion and environmental degradation. Approaches combining GIS (Geographic Information System), remote sensing, and image analysis techniques allow for assessments and predictions based on integrating theoretical models with advanced geospatial and geostatistical models. One of the most widely used models for soil erosion estimation is the Revised Universal Soil Loss Equation (RUSLE). The present work developed a model using remote sensing and GIS tools to investigate some factors of the RUSLE equation to evaluate the adverse effects of soil erosion in areas covered by arable crops and subsequently abandoned. To identify potentially degraded areas, two factors of the RUSLE were related: the C Factor describing the vegetation cover of the soil and the A Factor representing the amount of potential soil erosion. Through statistical correlation analysis with the RUSLE factors, based on the deviations from the average erosion values and mapping of the areas of vegetation degradation relating to arable land, the areas identified and mapped are susceptible to soil degradation. [ABSTRACT FROM AUTHOR]

Understanding the spatial pattern of soil chemical properties along with the topologic indicators is essential for site-specific agriculture management. A study was conducted on a 50 ha field to investigate the effect of selected topographic indicators, including elevation (DEM), slope (SL), flow accumulation (FA) and Topographic Wetness Index (TWI) on forage crop production. The soil samples and yield data were obtained from the field inventory. Topographical parameters of elevation and slope were estimated with the use of a real-time kinematic global positioning system (RTK-GPS), and then the DEM was generated. The collected soil samples were analyzed for pH, EC, nitrogen and soil organic carbon. Sentinel-2 images were for the creation of yield maps of alfalfa and Rhodes grass. Subsequently, on the basis of DEM, the generated elevation, slope and FA model were then compared with the yield and soil chemical properties. Statistical analysis revealed that the SL, FA and TWI, which are associated with water distribution, were significantly related to crop yields. The FA showed a medium-to-non-significant correlation with the productivity of both alfalfa (R2 = 0.586; p = 0.015) and Rhodes grass (R2 = 0.578; p = 0.01). A significant inverse correlation was recorded between the SL and the yield of both crops (R2 = −0.591 to −0.617; p = 0.01). The yield map revealed that the majority of the area (37.56%) of the experimental field was occupied by the medium-yield class, followed by the high-yield class (33.03%). [ABSTRACT FROM AUTHOR]

Land suitability assessment for irrigation is critical to inform as well as manage current and future irrigated agriculture production systems. Land suitability analysis determines whether a given land area could potentially be used for specific crop production. The objective of this study was to identify the availability of suitable land for surface irrigation systems for the production of millet, sorghum, sugarcane, and wheat production in the Lower Omo Gibe plain, Southern Ethiopia. Land suitability analysis was performed by a parametric method using factors such as soil texture, effective soil depth, Calcium Carbonate (CaCO3), soil electrical conductivity (ECe), drainage class, and slope. Five land suitability classes were identified that include highly suitable (S1), moderately suitable (S2), marginally suitable (S3), currently not suitable (N1), and permanently not suitable (N2). Results showed that 6.6, 7.5, 6.6, and 6.6% of the study area mostly located in the western part of the basin, were highly suitable (S1) for irrigated millets, sorghum, sugarcane, and wheat crops production, respectively. However, the mountainous areas in the central part of the basin were classified as N2 due to the steep slope and shallow soil depth. Overall, the results of the study revealed that the use of various suitability analysis techniques could assist in identifying suitable land for irrigated agriculture. [ABSTRACT FROM AUTHOR]

The purpose of a comprehensive field experiment was to evaluate the agronomic efficiency of a precise organomineral fertilizer system based on a uniform and differentiated application of mineral and organic fertilizers. The methodological basis of the study was a two-factor landscape field experiment with grain-grass crop rotation, established within the sloping agricultural landscape of a gently undulating glaciolacustrine plain. It was determined, that soil and agrochemical conditions and a stable soil water regime were of decisive importance in the effectiveness of fertilizers within the agrolandscape. The level increase in yield from the differentiated application of peat-dung compost (once in a bare fallow) and mineral fertilizers relative to the uniform application was 7–12% for winter wheat, 5–11% for oats, 3–8% for perennial grasses, and in the entire crop rotation—5–8%. It regularly decreased during the mineralization of the applied organic fertilizers. Among the three variants of the precise fertilization system studied, the best result was achieved in the option, where organic and mineral fertilizers were applied differentially. In this case, the absolute increase in crop rotation productivity relative to the unfertilized variant reached 16.39 t ha−1 of cereal units or 116%, and relative to the uniform fertilizer system—2.27 t ha−1 of cereal units or 8%. [ABSTRACT FROM AUTHOR]

The limited amount of freshwater is the most important challenge facing Egypt due to increasing population and climate change. The objective of this study was to investigate how climatic change affects the winter potato water footprint at the Nile Delta covering 10 governorates from 1990 to 2016. Winter potato evapotranspiration (ETC) was calculated based on daily climate variables of minimum temperature, maximum temperature, wind speed and relative humidity during the growing season (October–February). The Mann–Kendall test was applied to determine the trend of climatic variables, crop evapotranspiration and water footprint. The results showed that the highest precipitation values were registered in the northwest governorates (Alexandria followed by Kafr El-Sheikh). The potato water footprint decreased from 170 m3 ton−1 in 1990 to 120 m3 ton−1 in 2016. The blue-water footprint contributed more than 75% of the total; the remainder came from the green-water footprint. The findings from this research can help government and policy makers better understand the impact of climate change on potato crop yield and to enhance sustainable water management in Egypt's major crop-producing regions to alleviate water scarcity. [ABSTRACT FROM AUTHOR]

The cultivation of bioenergy plants in fertile, arable lands increasingly results in new land use conflicts with food production and cannot be considered as sustainable. Marginal lands have been frequently considered as potential alternatives for producing bioenergy from biomass. However, clear definitions and assessment methods for selecting marginal lands and for calculating potentials are still widely missing. The project "SEEMLA" aims at triggering the exploitation of currently underused marginal lands for biomass production for energy purposes. Study sites have been selected in different European countries: Germany, Greece, and Ukraine. The selected sites represent a wide variety of different types of marginal lands. Based on a soil assessment set given by the Muencheberg Soil Quality Rating (SQR) system potentially "marginal" sites have been investigated. The SQR system allows for clearly distinguishing between soils of higher and lower quality. Soils with SQR scores below 40 are regarded as "marginal". They can be classified into different groups with regard to the importance of soil hazard indicators as evaluated by the SQR approach. The calculated SQR scores correlate significantly with biomass yields of bioenergy plants. Further, the SQR method was adapted for use in a GIS study on marginal-land potentials in Europe. Thus, 46 % of the investigated European area could be classified as "marginal" with SQR scores below 40. From that area 22.6 % can be considered as potentially suitable for producing renewable resources after eliminating protected sites or other places not suitable for any kind of land use. Taking the ecological demands of selected bioenergy plants into account it is possible to give first preliminary recommendations for regional crop cultivation. It can be concluded that Europe offers a large potential for renewable resources from marginal sites. However, the implementation into practice is often impeded by missing or varying policies and regulations. A proper implementation needs clear regulations and also incentives for farmers at the European level. [ABSTRACT FROM AUTHOR]

With the growth of the Internet, crowdsourcing has become a popular way to perform intelligence tasks that hitherto would be either performed internally within an organization or not undertaken due to prohibitive costs and the lack of an appropriate communications infrastructure. In crowdsourcing systems, whereby multiple agents are not under the direct control of a system designer, it cannot be assumed that agents will act in a manner that is consistent with the objectives of the system designer or principal agent. In situations whereby agents’ goals are to maximize their return in crowdsourcing systems that offer financial or other rewards, strategies will be adopted by agents to game the system if appropriate mitigating measures are not put in place. The motivational and incentivization research space is quite large; it incorporates diverse techniques from a variety of different disciplines including behavioural economics, incentive theory, and game theory. This paper specifically focusses on game theoretic approaches to the problem in the crowdsourcing domain and places it in the context of the wider research landscape. It provides a survey of incentive engineering techniques that enable the creation of apt incentive structures in a range of different scenarios. [ABSTRACT FROM AUTHOR]

Assessing soil quality is considered one the most important indicators to ensure planned and sustainable use of agricultural lands according to their potential. The current study was carried out to develop a spatial model for the assessment of soil quality, based on four main quality indices, Fertility Index (FI), Physical Index (PI), Chemical Index (CI), and Geomorphologic Index (GI), as well as the Geographic Information System (GIS) and remote sensing data (RS). In addition to the GI, the Normalized Difference Vegetation Index (NDVI) parameter were added to assess soil quality in the study area (western part of Matrouh Governorate, Egypt) as accurately as possible. The study area suffers from a lack of awareness of agriculture practices, and it depends on seasonal rain for cultivation. Thus, it is very important to assess soil quality to deliver valuable data to decision makers and regional governments to find the best ways to improve soil quality and overcome the food security problem. We integrated a Digital Elevation Model (DEM) with Sentinel-2 satellite images to extract landform units of the study area. Forty-eight soil profiles were created to represent identified geomorphic units of the investigated area. We used the model builder function and a geostatistical approach based on ordinary kriging interpolation to map the soil quality index of the study area and categorize it into different classes. The soil quality (SQ) of the study area, classified into four classes (i.e., high quality (SQ2), moderate quality (SQ3), low quality (SQ4), and very low quality (SQ5)), occupied 0.90%, 21.87%, 22.22%, and 49.23% of the total study area, respectively. In addition, 5.74% of the study area was classified as uncultivated area as a reference. The developed soil quality model (DSQM) shows substantial agreement (0.67) with the weighted additive model, according to kappa coefficient statics, and significantly correlated with land capability R2 (0.71). Hence, the model provides a full overview of SQ in the study area and can easily be implemented in similar environments to identify soil quality challenges and fight the negative factors that influence SQ, in addition to achieving environmental sustainability. [ABSTRACT FROM AUTHOR]

Mapping and quantifying land degradation status is important for identifying vulnerable areas and to design sustainable landscape management. This study maps and quantifies land degradation status in the north Gojjam sub-basin of the Upper Blue Nile River (Abbay) using GIS and remote sensing integrated with multicriteria analysis (MCA). This is accomplished using a combination of biological, physical, and chemical land degradation indicators to generate a comprehensive land degradation assessment. All indicators were standardized and weighted using analytical hierarchy and pairwise comparison techniques. About 45.3% of the sub-basin was found to experience high to very high soil loss risk, with an average soil loss of 46 t ha−1yr−1. More than half of the sub-basin was found to experience moderate to high level of biological degradation (low vegetation status and low soil organic matter level). In total, 80.2% of the area is characterized as having a moderate level of physical land degradation. Similarly, the status of chemical degradation for about 55.8% and 39% of the sub-basin was grouped as low and moderate, respectively. The combined spatial MCA of biological, chemical, and physical land degradation indicators showed that about 1.14%, 32%, 35.4%, and 30.5% of the sub-basin exhibited very low, low, moderate, and high degradation level, respectively. This study has concluded that soil erosion and high level of biological degradation are the most important indicators of land degradation in the north Gojjam sub-basin. Hence, the study suggests the need for integrated land management practices to reduce land degradation, enhance the soil organic matter content, and increase the vegetation cover in the sub-basin. [ABSTRACT FROM AUTHOR]