Yield Of Agricultural Crops Research Articles

An Ensemble Based Clustering and Classification Framework for Prediction of Agricultural Crop Yield

An Ensemble Based Clustering and Classification Framework for Prediction of Agricultural Crop Yield

Chili cultivars vulnerability: a multi-factorial examination of disease and pest-induced yield decline across different growing microclimates and watering regimens

BackgroundAs identified by the research, it is imperative to develop effective ways to address the pressing problem of disease and pest susceptibility in chili agriculture and secure sustainable crop yield. The research examines the impact of various growing microclimates, watering regimens, and chili cultivars on disease incidence, pest attacks, and yield loss.ResultsThe study, which took place over a season, used a randomized complete block design to evaluate how well Tanjung, Unpad, and Osaka cultivars performed in four different watering regimens (100, 75, 50, and 25% ETc) and different microclimates (greenhouse, rain shelter, screen house, and open field). The findings exhibited that watering regimens and microclimates greatly influenced disease and pest occurrence, but cultivars had a minimal effect on these variables. Disease and pest attack rates were highest in the open field and lowest in the screen house. A correlation was found between lower disease and pest incidence and optimal irrigation levels (75% and 100% ETc). At lower watering regimens of 25% ETc and in the open field, yield loss was the greatest.ConclusionThe results emphasize how crucial controlled environments and appropriate irrigation techniques are to reducing crop loss and increasing production. Enhancing watering regimens and implementing screen house cultivation are two strategies for improving the productivity and sustainability of chili output.

Protein Hydrolysates—Production, Effects on Plant Metabolism, and Use in Agriculture

Agriculture is facing challenges to produce more food in a climate scenario that works in the opposite direction. To amend this, agriculture has to invent new ways of making more with less. Interest in using by-products and finding new ways to utilize them has been increasing in recent years. The use of protein-rich sources for protein hydrolyzation and the use of these protein hydrolysates as biostimulants in plant production have been increasing. These mixtures are mainly produced by chemical and enzymatic hydrolysis from agro-industrial protein-rich by-products of animal, plant, and algal origins. The application of PHs has the potential to alleviate environmental stress; improve plant growth; and increase productivity, fruit yield, and abiotic-stress tolerance in agricultural crops. The use of these biostimulants offers a way to reduce the use of agrochemicals and agrees with the “do more with less” task in the future of agricultural production. This review gives an insight into the production of PHs, referring to sources of raw materials and methods of hydrolysis, the uptake and translocation of PHs, their effect on plant growth, the development and physiology, their role in alleviating stressful conditions, and their use in agriculture. The beneficial effects of PHs on different aspects of plant physiology, metabolism, and plant functioning under stressful conditions are evident. Inconveniently, crops, and sometimes even cultivars, are affected differently based on the way that PH is applied, the timing, and the concentration applied. Further research is needed to elucidate the mechanisms by which the components of PHs modify plant physiology and metabolism.

Analysis of the Level of Agricultural Development in Kazakhstan: Identifying Agro-Hubs

The study examines Kazakhstan's agricultural sector and its potential for developing resilient agro-hubs, focusing on identifying regions that can contribute to the country's long-term agricultural growth. The purpose of this study is to examine essential agricultural indicators, including gross agrarian output, gross crop production, gross livestock production, and agricultural services, in order to identify regions with the potential for agro-hub development. The methodology includes Pearson correlation analysis of data collected from national agricultural reports and regional statistical databases. Data for the study were gathered from national agricultural reports and regional statistical databases, which provide quantitative measures of agricultural output, crop yields, livestock production, and farming services. Results indicate the strongest correlations between Kostanay and North Kazakhstan (0.995 for Gross Agricultural Output and 0.996 for Crop Production, p < 0.001). Additionally, significant alignment in crop production was observed between Almaty and South Kazakhstan (0.969, p = 0.007), whereas weaknesses in agricultural services were noted, particularly in Kyzylorda and Zhambyl, with a negative correlation. The results highlight the northern and southern regions' potential for forming agro-hubs supported by solid production indicators. The study provides strategic recommendations for policymakers to foster regional collaboration, enhance productivity, and promote sustainable agricultural development across Kazakhstan. Future research will focus on improving infrastructure and developing collaborative agricultural initiatives within agro-hubs to strengthen Kazakhstan's agricultural sector resilience.

Influence of Colored Shade Nets and Salinity on the Development of Roselle Plants

Adequate fertilizer concentration and use of shade nets can favor the development and yield of agricultural crops. The objective of this investigation was to evaluate the growth of roselle plants with nutrient solutions of different electrical conductivities (ECns) (1.0, 2.0, 3.0, 4.0 and 5.0 dS m−1) and under different colored shade nets (red, blue, black) compared with full sun. The experiments were conducted in a controlled greenhouse environment and in full sun in the Plant Production Department of ESALQ-USP, Piracicaba, SP, Brazil. The experiments were organized using a 4 × 5 randomized block design. The results of analysis of variance and regression showed a significant impact of EC and colored shade nets on plant height, stem diameter, number of leaves, number of flowers, fresh and dry mass of shoots and fresh and dry calyxes. The data were subjected to analysis of variance and regression, which showed a quadratic effect for the variables studied, with increasing values up to 3.0 dS m−1; after this value, there was a decrease. Increasing ECns up to approximately 3.0 dS m−1 promoted increments of 2.34% in plant height, 7.21% in number of leaves, 19.76% in shoot fresh mass and 12.38% in shoot dry mass.

SCREENING OF PESTICIDAL PROPERTIES OF LANTANA CAMARA AND CALOTROPIS PROCERA EXTRACT AND ITS APPLICATION AGAINST SPODOPTERA LITURA(TOBACCO CUT-WORM)

In recent years botanical extracts that appears to have a vital role in the Development of low cost pesticides together with for Agricultural and forestry crop yield enhancement. Further more for the safety of Environment and public health. The Detrimental Environmental implications of the Synthetic bio-pesticides have forced to search for some substitute methods. This lead to enhanced development of compounds based on the types of naturally occurring toxins of biological origin having a lot of biological activities. This includes plant extracts, which are now well known because they are environmentally harmless and host specific. The different concentrations of plant extracts with different solvents (polar, semi-polar and non-polar) were tested for mortality properties against Spodoptera liturathird instar larvae. The prepared extract of aerial parts of plant Lantana camara and Calotropis procera were tested and the most effective concentrations have been worked out L. camara and C. procera weed plant having different medicinal properties. (MS excel & poloplus) software is applied. The LC50 and LC90 estimated using Hexane, Aqueous, Chloroform, Ethyl acetate, Ethanol extracts of C. proceraand L. camara were 0.24, 0.33, 0.20, 018, 0.15 and 1.97, 3.09, 0.90, 1.77, 0.88 respectively with fiducial limit of 0.16 to 2.44, 0.25 to 10.65, 0.17 to 2.67, 0.15 to 2.00, 0.10 to 5.97 and 0.16 to 2.44, 0.20 to 6.44, 0.17 to 1.61, 0.09 to 6.33, 0.06 to 11.12. The LC50 and LC90 limits estimated with Hexane, Aqueous, Chloroform, Ethyl acetate extract L. camara wire 0.19, 0.26, 0.16, 0.14, 0.13 and 1.35, 3.09, 0.99, 0.79, 0.59. Respectively with fiducial limits of 2.44 to 16.31, 6.44 to 4.98, 1.61 to 9.58, 6.33 to 4.56, 11.12 to 2.33 and 4.08 to 6.17, 10.65 to 5.49, 2.07 to 11.13, 2.00 to 18.96, 5.97 to 6.42 respectively.

Identifying the most effective climate parameters on crop yield in rain-fed agriculture and irrigated farming in Iran

Agriculture is an industry that has been traditionally used in Iran to meet people's daily nutritional needs. Since crops are affected by climate change, finding the best location to grow crops will improve crop yields. The Geographical Detector Model (GDM) was adopted in this study to measure the effects of four climate parameters, i.e., precipitation, temperature, humidity, and evaporation, in different seasons of the year on the yields of barley, beans, lentils, chickpeas, rapeseed, and wheat across Iran. According to the results showed, the highest crop yields across Iran come from wheat and barley. Furthermore, the yields of most crops are reduced in rain-fed agriculture, as the yield of wheat in rain-fed agriculture decreases by nearly 70% as opposed to irrigated farming. According to the findings, EA, ESU and EW with 24, 22 and 19 percent has more affected to barley. Rising temperatures to more than 26.1 °C can cause a sharp decrease in crop yields, with some crops yielding less than 0.5 tons/ha. The results also showed that wheat thrives more effectively in regions characterized by cold and extremely cold winters, where temperatures ranging from −3.5 to 0.98 yield an average of 3.88 tons per hectare in irrigated agriculture, while rainfed farming shows a recorded figure between 3.6 and 4.7 at 1.08. Provincial assessments indicate that temperature and precipitation are the key factors influencing crop productivity in regions characterized by cooler temperatures and higher rainfall. Specifically, the peak barley yield in irrigated areas was observed with spring rainfall exceeding 153 mm. Conversely, in tropical provinces with limited rainfall, humidity and evaporation emerge as the primary determinants of crop yields. In these regions, the highest barley yields were noted where evaporation levels surpassed 227 mm. Moreover, provinces located on the slopes of the Alborz and Zagros mountains are the best locations for wheat and chickpea cultivation, and the southeastern regions of Iran are not suitable for barley and lentils. The findings of this research have significant implications for water resource management, facilitating the identification of cultivation patterns across diverse climatic conditions and enhancing strategies for crop yield optimization.

An ecological framework to index crop yields using productivity and Ecosystem Fit: A case study from India

On the global scale, agricultural crop yields have decreased or plateaued over the last several decades. This suggests that the current focus on selecting crop varieties based on a plant’s light-use efficiency (photosynthetic and nitrogen-use-efficiency metrics) may not be sensitive to the site’s edaphic parameters, which limit growth. This study introduces a new framework to determine if crops can achieve higher yield potentials by assessing how plants adapt to the edaphic properties that impact growth, especially when contending with climate change. The new approach calculates an Ecosystem Fit index using a ratio of remotely sensed (or observed) total net primary productivity to the theoretical maximum productivity of the site. Then, it uses that index as a benchmark to judge quantitatively whether any new crop species or variety is improving potential biomass or economic yields at that specific site. It can also determine the best soil types for those crop varieties and monitor their potential adaptability relative to climate change over time. This study used a database of 356 spatially independent reference sites to develop this framework using a landcover classification of crops across 21 ecoregions and five biomes in India. It includes total net primary productivity data, theoretical maximum productivity potential, and soil and climatic data. This comparison showed that the light-use efficiency model, as intended, was not sensitive to variations in soil characteristics, temperature, or precipitation. Our framework showed significant differences in growth by soil type and precipitation and three significant productivity thresholds by soil type. The results of this study demonstrate that total crop productivity and Ecosystem Fit create a useful index for local land managers to assess growth and yield potentials across diverse edaphic landscapes and for decision-making with changing climates.

Harnessing Virtual Insights: A Computational Study on the Molecular Characterization of a Drought‐Inducible RNA‐Binding Protein for Enhancing Drought Tolerance in Mungbean (Vigna radiata L.)

AbstractAbiotic stresses, particularly drought and salinity, significantly impact agricultural crop yields, with mung bean (Vigna radiata L.) being no exception. This annual green legume, vital to agricultural systems in Asia, is generally resilient to limited water supply; however, severe drought conditions at critical growth stages can drastically diminish both its quality and yield. Drought stress negatively affects various morpho‐physicochemical properties, impeding plant growth and highlighting the need for sustainable approaches to enhance mung bean's drought tolerance through improved farming practices. In this study, we focused on identifying and understanding the molecular mechanisms underlying drought tolerance in mung beans. We retrieved the target gene sequence from the NCBI database, but found no direct sequence similarity, which led to the use of homology modeling. The structure of a known protein (PDB ID: 5T9P) was identified as the most suitable template. Motif prediction using the Motif Search tool highlighted specific motifs relevant to drought stress responses. To investigate potential drought‐tolerance mechanisms, we screened ABA agonists using Density Functional Theory (DFT) analysis. Molecular docking and MD simulations revealed the interactions between these ligands and the modeled protein, shedding light on how they might influence drought tolerance. We performed molecular dynamics (MD) simulations to assess the stability and conformational dynamics of the modeled protein across different time scales. Our findings underscore the potential of ABA agonists to modulate physiological responses and enhance drought resistance. This research offers valuable insights into the conformational dynamics of drought‐related proteins in mung beans, elucidating the molecular underpinnings of drought response mechanisms. By advancing our understanding of these processes, our findings provide novel strategies for improving breeding approaches aimed at enhancing crop resilience against water scarcity and other abiotic stresses. This work promises to mitigate the detrimental effects of drought on mung bean yields and supports sustainable agricultural practices, thereby contributing to global food security.

Assessing pollinator abundance and services to enhance agricultural sustainability and crop yield optimization in the Qilian Mountains

ContextPollination services are critical to crop production and human livelihoods, linking natural ecosystems directly to agricultural production systems. However, pollination services and pollinators are under constant threat from land-use changes and various other environmental pressures. ObjectiveIn this study, employing a case study of the entire Qilian Mountains in northwest China, the distribution of nectar sources in different land use types in the study area was determined through field survey and literature. MethodsBased on 1990–2020 land use data, crop yield data, crop prices, pollinating bee species and nectar plants data. We used the Gross Ecological Product (GEP) accounting and InVEST model of pollination services and assessed the status and trends of pollination services, the risks posed by land use changes and environmental pressures, and suggested potential solutions for mitigating identified risks. Results and conclusionsThe results of the study showed that (1) nectar sources and nesting areas' potential distribution closely correlates with land use types; (2) the Pollinator abundance index (PAI) is above 0.30, a high level, and the Pollination potential index (PPI) is between 0.15 and 0.30, a medium level, with both indices generally increasing over the past 40 years; (3) Human economic activities and land management policies had the most significant impact on pollination services, with 15.57 % and 14.02 %, respectively. Climate change (temperature, precipitation, extreme events) and invasive alien species had relatively minor impacts, accounting for 0.14 % and 0.15 %, respectively. (4) The Qilian Mountains will face a new risk, whether monoculture or the expansion of pollinator-dependent crops could lead to habitat homogenization issues, potentially affecting pollinator abundance and diversity. SignificanceWe recommend that future plans emphasize the provision of pollinator nesting resources along with floral resources, restoration of semi-natural and natural habitats adjacent to crops, adjustment of cropping patterns, and implementation of agricultural diversification, which will help to ensure pollinator diversity and sustainability of agroecosystem pollination services.

Streamlining YOLOv7 for Rapid and Accurate Detection of Rapeseed Varieties on Embedded Device.

Real-time seed detection on resource-constrained embedded devices is essential for the agriculture industry and crop yield. However, traditional seed variety detection methods either suffer from low accuracy or cannot directly run on embedded devices with desirable real-time performance. In this paper, we focus on the detection of rapeseed varieties and design a dual-dimensional (spatial and channel) pruning method to lighten the YOLOv7 (a popular object detection model based on deep learning). We design experiments to prove the effectiveness of the spatial dimension pruning strategy. And after evaluating three different channel pruning methods, we select the custom ratio layer-by-layer pruning, which offers the best performance for the model. The results show that using custom ratio layer-by-layer pruning can achieve the best model performance. Compared to the YOLOv7 model, this approach results in mAP increasing from 96.68% to 96.89%, the number of parameters reducing from 36.5 M to 9.19 M, and the inference time per image on the Raspberry Pi 4B reducing from 4.48 s to 1.18 s. Overall, our model is suitable for deployment on embedded devices and can perform real-time detection tasks accurately and efficiently in various application scenarios.

Assessing the efficiency of using precision farming technology and remote monitoring of weather conditions in the activities of agricultural enterprises

The object of this study was the technology of precision agriculture and remote monitoring of weather conditions. The task to evaluate the effectiveness of using precision farming and precision irrigation technologies in the activities of agricultural enterprises under different conditions, in particular, different climatic and weather conditions, has been considered. To solve the task, a hardware-software system for smart irrigation and remote monitoring of weather conditions in the activities of agricultural enterprises during the cultivation of agricultural crops was designed and described. Results of the system's performance were analyzed in the activities of the Ukrainian agricultural company, which grew potatoes of various varieties in the Kyiv oblast (Ukraine) from 2021 to 2023. The results show that the average yield of potatoes of different varieties without irrigation for three years of observation was 29.74 t/ha, with irrigation – 48.99 t/ha, and with smart irrigation – 55.26 t/ha. At the same time, in the latter case, water, human, and financial resources were saved. The increase in yield with smart irrigation compared to yield with conventional irrigation over the three years of observation was on average 12.8 %. According to the results of the implementation of the hardware-software system for smart irrigation and remote monitoring of weather conditions in Ukraine, the effect of the possible implementation of this system by agricultural companies in the Republic of Kazakhstan was analyzed. The forecast of the average yield of potatoes for the period from 2024 to 2026 was built based on the model of linear weighted moving average, taking into account corrections in the case of using smart irrigation. Data on potato yield from 1990 to 2023 were chosen as the basis. The use of smart irrigation according to the described technology could increase the yield of potatoes of various varieties on average from 31.71 t/ha to 35.78 t/ha in comparison with the forecast values of yield without irrigation at the level of 19.25 t/ha. This confirms the need to apply the transfer of precision farming technologies to increase the yield of agricultural crops, in particular potatoes, and the productivity of agricultural companies

Mathematical Modeling for Predicting Growth and Yield of Halophyte Hedysarum scoparium in Arid Regions under Variable Irrigation and Soil Amendment Conditions

Growing degree days (GDDs) and leaf area index (LAI) greatly influence the growth and yield of many crops grown in arid regions. Therefore, variation in LAI due to GDD can provide a theoretical basis for predicting crop growth, water consumption, plant development, and yield in arid agriculture via the development of mathematical growth models. This study described the relationship between plant biomass production and variation in LAI due to GDD in arid regions under different types of irrigation (fresh water and saline water) and soils amended with different substances (manure+sandy soil, compost+sandy soil, clay+sandy soil, and sandy soil). Mathematical models for LAI were established for GDDs. In addition, different water quality irrigation techniques were used as independent variables to calculate the LAI of halophytic plants (Hedysarum scoparium) in arid regions under different soil amendment treatments. Furthermore, mathematical models for plant biomass production were developed by using the LAI and GDDs. For this purpose, Logistic, Gaussian, modified Gaussian, and Cubic polynomial models were used. Modified Gaussian and Cubic polynomial models are the best among all developed models, but Cubic polynomial models are more suitable among all developed models because of their simple quadratic equations that can be solved by using the first derivative. It was observed that with increased salt concentration in the irrigation water, the growth of per plant production decreased. However, soil amendments like manure and compost enhance salt tolerance against salt stress and enable plants to sustain their growth. Furthermore, Hedysarum scoparium attains maximum LAI when its GDD is about 1117.5 °C under both irrigation regimes and in all soil amendment treatments. It was concluded that these predicted mathematical models can provide crucial insights for enhancing production in arid regions by using eco-friendly soil amendments to improve water use efficiency across diverse types of water irrigation.

Hydrogel in Drylands: A Review

Significant amounts of water and nutrients are held in reserve by hydrogels due to their three-dimensional polymeric network. The capacity of hydrogels to absorb water lowers the frequency of irrigation, which is beneficial for agricultural application. When applied to soil, it functions well as a nutrient mobilizer and a reservoir for water. Applying hydrogel will be a profitable and advantageous way to boost agricultural crop yield and sustainability in settings where moisture is scarce. Because of their three-dimensional polymeric network, hydrogels can store large amounts of water and nutrients. Because hydrogels can absorb water, less irrigation is required, which is advantageous for agricultural use. It works well as a water reservoir and nutrient mobilizer when added to soil. In environments where moisture is limited, applying hydrogel will be a profitable and beneficial strategy to increase agricultural crop productivity and sustainability.

Assessment of the effect of climate change on the yields and water footprint of crops in arid area

ABSTRACT Investigating the actual amount of water used by agricultural crop yield is very important. This research evaluates the impact of climate change on water demand, yields, and green and blue water footprint of barberry and jujube plants under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. The results show that the amount of precipitation in the future under three scenarios varies from −13.08 to 5.13 mm during the future period. It shows maximum temperature changes from 1.07 to 1.25 °C and minimum temperature changes from 1.15 to 1.54 °C. The maximum and minimum temperatures under all three scenarios have an increasing trend compared to the base period. The results show that the values of water requirement, blue water footprint, green water footprint, total water footprint, and finally the relative values of both crop yields increase in all three scenarios in the future period (2022–2038) compared to the base period (2021–2005). Furthermore, according to the three proposed scenarios, the water footprint of the barberry crop in the future is estimated to be 98.74% blue water and 1.24% green water. The jujube crop's water footprint consists of 98.81% blue water and 1.20% green water.

The implications of various seed priming treatments on seed yield in proso millet (Panicum miliaceum)

The experiment was conducted during rainy (kharif) season of 2021 and winter (rabi) season of 2022 at Annamalai University, Annamalai Nagar, Chidambaram, Tamil Nadu to identify suitable seed priming technique, soaking duration and priming agents for halo, osmo, and bio-priming in proso millet (Panicum miliaceum L.). To increase crop yield and sustainability in present-day agriculture, seed priming continues to be essential by utilizing scientific knowledge and technical breakthroughs. The experiment was laid out in factorial completely randomized design (FCRD) with three replications. The fresh seeds of proso millet cv. CO4 were imposed for halo, osmo, and bio priming treatments, viz. Agent for halo-priming: Control, hydropriming, KH2PO4 (1 and 2%), ZnSO4 (1 and 2%), and CaCl2 (1 and 2%); Agent for osmo-priming: Control, hydropriming, NaCl (1 and 2%), Mannitol (1 and 2%), and PEG (-10 and -15 Bar); Agent for bio-priming: Control, hydropriming, Azospirillum (15 and 20%), P. fluorescens (LF) (10 and 15%), Prosophis LE (5 and 10%), and soaked in equal volumes for 6 and 8 h. The results revealed that for halo priming, seeds soaked in 2% KH2PO4 for 8 h; for osmo priming seeds soaked in 2% mannitol for 8 h and for bio-priming seeds soaked in 15% P. fluorescens (LF) for 8 h outperformed other treatments. Field experiments were also conducted to determine the productivity of seed primed proso millet. The results showed that the crop performance was superior in seeds primed with 2% KH2PO4 for 8 h during kharif season than rabi.

Quantitative phenotyping of crop roots with spectral electrical impedance tomography: a rhizotron study with optimized measurement design

BackgroundRoot systems are key contributors to plant health, resilience, and, ultimately, yield of agricultural crops. To optimize plant performance, phenotyping trials are conducted to breed plants with diverse root traits. However, traditional analysis methods are often labour-intensive and invasive to the root system, therefore limiting high-throughput phenotyping. Spectral electrical impedance tomography (sEIT) could help as a non-invasive and cost-efficient alternative to optical root analysis, potentially providing 2D or 3D spatio-temporal information on root development and activity. Although impedance measurements have been shown to be sensitive to root biomass, nutrient status, and diurnal activity, only few attempts have been made to employ tomographic algorithms to recover spatially resolved information on root systems. In this study, we aim to establish relationships between tomographic electrical polarization signatures and root traits of different fine root systems (maize, pinto bean, black bean, and soy bean) under hydroponic conditions.ResultsOur results show that, with the use of an optimized data acquisition scheme, sEIT is capable of providing spatially resolved information on root biomass and root surface area for all investigated root systems. We found strong correlations between the total polarization strength and the root biomass (R2=0.82\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2 = 0.82$$\\end{document}) and root surface area (R2=0.8\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2 = 0.8$$\\end{document}). Our findings suggest that the captured polarization signature is dominated by cell-scale polarization processes. Additionally, we demonstrate that the resolution characteristics of the measurement scheme can have a significant impact on the tomographic reconstruction of root traits.ConclusionOur findings showcase that sEIT is a promising tool for the tomographic reconstruction of root traits in high-throughput root phenotyping trials and should be evaluated as a substitute for traditional, often time-consuming, root characterization methods.

A comparison of random forest based models for agricultural crop yield prediction

A comparison of random forest based models for agricultural crop yield prediction

Density of soil composite composition in a changing magnetic field

The advancement of technical means for determining soil density in precision (controlled) agriculture necessitates the enhancement of non-destructive flow interaction methods. There is also a need to improve methods for assessing soil density, as existing deviations from optimal values adversely affect the yield of agricultural crops. This research aims to establish the density of the composites in the granulometric composition of soil by determining the self-induction voltage in a changing magnetic field. The research methods are adapted to determine the relationships of Larmor precession under variable inductive current for the density of each composite in the granulometric soil composition. Experimental investigations were conducted by measuring the self-induction voltage of a solenoid acting as a sensor for each of the composites located within the core. The processing of experimental results was carried out according to the principles of mathematical statistics and probability theory, using approximations in the Excel and Statistica software packages. Based on the results of the experimental investigations, models of the relationships between the density ρ (1.0-1.5 g/cm3) of soil composites and the self-induction voltage eL=184-192 mV, with a generator voltage of 5 V and determination coefficients R2=0.95-0.99, have been established. These models enable the assessment of soil density with high accuracy, thereby facilitating the optimisation of agronomic processes. It has been determined that these models can be used with a high degree of reliability as calibration characteristics for the design of technical means for flow-based non-destructive measurement of the density of the main soil types in Ukraine. Furthermore, it has been identified that future research should focus on a more in-depth investigation of the relationships between magnetic permeability and the agrophysical characteristics of soil within the locally defined inductive field of the sensor (solenoid). An applied aspect of the obtained results is the further development of adaptive machinery and monitoring systems for soil conditions, aimed at achieving optimal cultivation indicators, as well as their utilisation by research institutions and instrument-making enterprises

MODELING THE EFFECTS OF PESTS AND PESTICIDE ON CROP YIELDS IN A MULTIPLE CROPPING SYSTEM

Pest infestation poses a significant threat to agricultural crop yields, and to control it, farmers spray chemical pesticides. The persistent use of these chemical agents not only leads to pesticide residues within crops but also exerts collateral damage on the beneficial pest population. In this research work, we formulate a nonlinear mathematical model to assess the impacts of pesticide on crop yields within a multiple cropping system. Model analysis illustrates that crop consumption rates destabilize, and the spraying rate of pesticide stabilizes the system. Furthermore, we determine conditions for the global stability of the coexisting equilibrium and conduct a global sensitivity analysis to identify model parameters that significantly influence pest population density. Our findings emphasize that, for effective pest population control and enhanced crop yields, farmers should choose either pesticides with a high pest abatement rate or those with a higher pesticide uptake rate. Considering the spraying rate of pesticide as time-dependent, we also suggest an optimal control strategy to minimize the pest population and associated costs. We provide analytical results backed by numerical simulations implemented through the non-standard finite difference scheme to support our findings.