Crop Conditions Research Articles

Changes in root nodules dynamics under mixed cropping with varying nutrient management of berseem crop and succeeding fodder cowpea

Intercropping is a sustainable way to achieve the growing demands for food and fodder of expanding population of tropical areas. Combined application of nutrient sources along with inclusion of fodder crops in cropping system will influence soil as well as plant characteristics above ground as well as below it. These sustainable approaches may increase nutrient use efficiency by rhizosphere management at soil and plant level giving better understanding of soil plant atmosphere continuum (SPAC). The present study was conducted to evaluate the effect of nutrient management and seed ratios on dynamics of root nodules of berseem as well as succeeding crop cowpea at Agronomy Research farm, National Dairy Research Institute, Haryana, India. Intensive root studies had been done regarding root parameters in berseem as well as residual succeeding crop cowpea in this research. The study revealed that 100%RDF(Recommended dose of Fertilizer)+PGPR (Plant Growth Promoting Rhizobacteria) had given maximum number of nodules (118.60) and other traits for three cuttings in terms of nutrient management and sole berseem in terms of seed ratios number of nodules (112.92), whereas 50%RDF (Recommended dose of Fertilizer) +50% FYM (Farm Yard Manure)-(62.5) and sole berseem (54.30) at harvest had shown better results in terms of residual activity on roots nodules of succeeding crop cowpea where number of nodules (62.5) and fresh wt. of root nodules (622.1 mg) at harvest in 50%RDF + 50% FYM. This research revealed that employing organic sources of nutrients like FYM (Farm Yard Manure) and biofertilizers like PGPR (Plant Growth Promoting Rhizobacteria) along with inorganics in correct proportions enhances the root traits of leguminous fodder crops and reduce sole dependency on harmful agricultural inputs. Hence, this would fulfill the long-term nutritional requirement of crop and preserve crop status in terms of quality and quantity.

A review on seed priming to combat climate variability in agriculture

Global agriculture faces immense challenges due to climate change, which causes unpredictable weather patterns, decreased agricultural productivity, and decreased food security. Seed priming is critical in combating climate variability because it has emerged as a promising method for improving seed germination and agricultural resilience. This review evaluates the efficiency of several seed priming techniques, including hydro-priming, halo-priming, osmo-priming, bio-priming, chemical priming, and hormone priming. These techniques improve seedling vigor, stress tolerance, and overall crop yield. Seed priming increases germination rates and resistance to biotic and abiotic stresses, such as salinity and drought, while improving agricultural output and disease resistance. Seed priming reduces the demand for chemical pesticides and fertilizers by increasing soil quality and nutrient absorption, which supports sustainable agriculture. This review highlights the potential benefits of seed priming as a practical, affordable, and practical strategy to reduce the negative effects of climatic variability on agriculture. Future studies should focus on developing the best priming techniques for diverse crop varieties and conditions, as well as examining the combined impacts of various priming strategies. Seed priming will be crucial to preserving food security and agricultural sustainability in the face of ongoing climate change.

Proximal Sensing for Characterising Seaweed Aquaculture Crop Conditions: Optical Detection of Ice-Ice Disease

Crop monitoring is a fundamental practice in seaweed aquaculture. Seaweeds are vulnerable to several threats such as ice-ice disease (IID) causing a whitening of the thallus due to depigmentation. Crop condition assessment is important for minimizing yield losses and improving the biosecurity of seaweed farms. The recent influence of modern technology has resulted in the development of precision aquaculture. The present study focuses on the exploitation of spectral reflectance in the visible and near-infrared regions for characterizing the crop condition of two of the most cultivated Eucheumatoids species: Kappaphycus alvareezi and Eucheuma denticulatum. In particular, the influence of spectral resolution is examined towards discriminating: (a) species and morphotypes, (b) different levels of seaweed health (i.e., from healthy to completely depigmented) and (c) depigmented from silted specimens (thallus covered by a thin layer of sediment). Two spectral libraries were built at different spectral resolutions (5 and 45 spectral bands) using in situ data. In addition, proximal multispectral imagery using a drone-based sensor was utilised. At each experimental scenario, the spectral data were classified using a Random Forest algorithm for crop condition identification. The results showed good discrimination (83–99% overall accuracy) for crop conditions and morphotypes regardless of spectral resolution. According to the importance scores of the hyperspectral data, useful wavelengths were identified for discriminating healthy seaweeds from seaweeds with varying symptoms of IID (i.e., thalli whitening). These wavelengths assisted in selecting a set of vegetation indices for testing their ability to improve crop condition characterisation. Specifically, five vegetation indices (the RBNDVI, GLI, Hue, Green–Red ratio and NGRDI) were found to improve classification accuracy, making them recommended for seaweed health monitoring. Image-based classification demonstrated that multispectral library data can be extended to photomosaics to assess seaweed conditions on a broad scale. The results of this study suggest that proximal sensing is a first step towards effective seaweed crop monitoring, enhancing yield and contributing to aquaculture biosecurity.

Prediction of Soil Temperature in Wheat Field Using Machine Learning Models

ABSTRACT Wheat is a very vital cereal crop in the development of a nation’s economy and agricultural sector. A crucial element that profoundly affects the growth and productivity of wheat is the soil temperature. As an insulating layer, straw mulch controls the soil temperature. Field measurement of the soil temperature is time-consuming and costly; therefore, the present study investigates the applicability of random forest (RF), support vector machine (SVM) and generalized regression neural network (GRNN) models for estimating soil temperature in wheat fields covered with straw mulch using meteorological, soil cover and agronomical parameters. A field study was conducted on the wheat cultivar with two soil cover treatments for measuring the soil temperature at three depths (5, 50 and 80 cm). Soil plant analysis development value; soil cover and depth; daily meteorological data such as average air temperature, average relative humidity, sunshine hours and vapor pressure were used as inputs for machine learning models in training and testing. Statistical performance indicators showed applicability of three models with a root mean square error (RMSE) ranging from 0.245–0.595°C, 0.867–0.886°C and 0.557–0.812°C, Nash-Sutcliffe efficiency (NSE) ranging from 0.917–0.987, 0.823–0.836 and 0.804–0.935 and Lin’s concordance correlation coefficient (CCC) ranging from 0.949–0.991, 0.887–0.897 and 0.881–0.957 for the RF, SVM and GRNN, respectively. Results indicated better performance of the RF model for predicting the soil temperature at different depths in the case of both crop and soil cover conditions. Also, the uncertainty analysis indicated the better performance of RF with R-factor and P-factor value of 0.40 and 0.84, respectively.

Analysis of the Status of Irrigation Management in North Carolina

Farmers in North Carolina are turning to irrigation to reduce the impacts of droughts and rainfall variability on agricultural production. Droughts, rainfall variability, and the increasing demand for food, feed, fiber, and fuel necessitate the urgent need to provide North Carolina farmers with tools to improve irrigation management and maximize water productivity. This is only possible by understanding the current status of irrigated agriculture in the state and investigating its potential weaknesses and opportunities. Thus, the objective of this study was to perform a comprehensive analysis of the current state of irrigation management in North Carolina based on 15-year data from the Irrigation and Water Management Survey by the United States Department of Agriculture–National Agricultural Statistics Service (USDA-NASS). The results indicated a reduction in irrigation acres in the state. Also, most farms in the state have shifted to efficient sprinkler irrigation systems from gravity-fed surface irrigation systems. However, many farms in North Carolina still rely on traditional irrigation scheduling methods, such as examining crop conditions and the feel of soil in deciding when to irrigate. Hence, there are opportunities for enhancing the adoption of advanced technologies like soil moisture sensors and weather data to optimize irrigation schedules for improving water efficiency and crop production. Precision techniques and data-based solutions empower farmers to make informed, real-time decisions, optimizing water use and resource allocation to match the changing environmental conditions. The insights from this study provide valuable information for policymakers, extension services, and farmers to make informed decisions to optimize agricultural productivity and conserve water resources.

A Comprehensive Review of Potential Adaptation and Mitigation Strategies in Agriculture in the Current Climate Change Scenarios

Climate change has a major impact on agriculture worldwide, but in certain regions, like India, this impact is more pronounced than in others. India's large population, heavy reliance on natural resources, and lack of coping mechanisms make it difficult for the country to adapt to the changing climate. India has experienced a notable warming trend over the past century, with an approximate 0.60 °C increase in temperature. This trend is anticipated to continue, possibly having catastrophic consequences for agricultural productivity and food security. Climate change is predicted to result in increasingly variable growing conditions for many crops, making it more challenging for farmers to schedule the planting and harvesting of their crops. Rapid reaction times and coordination are required to mitigate the consequences of climate change on Indian agriculture. This means implementing adaptation tactics, such as cultivating crop varieties that can tolerate high temperatures, promoting water-efficient irrigation techniques, preserving soil health, and enhancing climate resilience through integrated farming systems. India can lessen the negative effects of climate change on its agriculture sector and guarantee food security for its growing population by taking proactive steps and implementing sustainable farming practices.

A Bayesian approach for estimating the uncertainty on the contribution of nitrogen fixation and calculation of nutrient balances in grain legumes

BackgroundThe proportion of nitrogen (N) derived from the atmosphere (Ndfa) is a fundamental component of the plant N demand in legume species. To estimate the N benefit of grain legumes for the subsequent crop in the rotation, a simplified N balance is frequently used. This balance is calculated as the difference between fixed N and removed N by grains. The Ndfa needed to achieve a neutral N balance (hereafter θ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ heta$$\\end{document}) is usually estimated through a simple linear regression model between Ndfa and N balance. This quantity is routinely estimated without accounting for the uncertainty in the estimate, which is needed to perform formal statistical inference about θ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ heta$$\\end{document}. In this article, we utilized a global database to describe the development of a novel Bayesian framework to quantify the uncertainty of θ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ heta$$\\end{document}. This study aimed to (i) develop a Bayesian framework to quantify the uncertainty of θ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ heta$$\\end{document}, and (ii) contrast the use of this Bayesian framework with the widely used delta and bootstrapping methods under different data availability scenarios.ResultsThe delta method, bootstrapping, and Bayesian inference provided nearly equivalent numerical values when the range of values for Ndfa was thoroughly explored during data collection (e.g., 6–91%), and the number of observations was relatively high (e.g., ≥100\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ge 100$$\\end{document}). When the Ndfa tested was narrow and/or sample size was small, the delta method and bootstrapping provided confidence intervals containing biologically non-meaningful values (i.e. < 0% or > 100%). However, under a narrow Ndfa range and small sample size, the developed Bayesian inference framework obtained biologically meaningful values in the uncertainty estimation.ConclusionIn this study, we showed that the developed Bayesian framework was preferable under limited data conditions ─by using informative priors─ and when uncertainty estimation had to be constrained (regularized) to obtain meaningful inference. The presented Bayesian framework lays the foundation not only to conduct formal comparisons or hypothesis testing involving θ\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ heta$$\\end{document}, but also to learn about its expected value, variance, and higher moments such as skewness and kurtosis under different agroecological and crop management conditions. This framework can also be transferred to estimate balances for other nutrients and/or field crops to gain knowledge on global crop nutrient balances.

Influence of Bacterial Fertilizers on the Structure of the Rhizospheric Fungal Community of Cereals South of Western Siberia

The general lack of knowledge on the conditions of Western Siberia (Omsk region) and the taxonomic diversity of zonal soils determines the relevance of these studies. The research was carried out in order to study the effect of complex biologics on the taxonomic diversity of the fungal component of the microbiome of the rhizosphere of cereals and the phytosanitary condition of crops in the southern forest-steppe (meadow-chernozem soil) and subtaiga (gray forest soil) zones of the Omsk Irtysh region (Western Siberia). This work was carried out in 2022–2023, using laboratory studies in combination with field experiments and metagenomic and statistical analyses. The objects of research were varieties of cereals and grain forage crops of Omsk selection: soil microorganisms. The scheme of the experiment involved the study of the following options: varieties of cereals (factor A): spring soft wheat—Omsk 42, Omsk 44, Tarskaya 12; durum wheat—Omsk coral; barley—Omsk 101; oats—Siberian hercules; bacterial preparation for seed inoculation (factor B) without the drug—Mizorin and Flavobacterin. The sampling of the plant rhizosphere for metagenomic analysis was carried out during the earing phase (July). For the first time, the taxonomic composition of the fungal community was determined based on the analysis of amplicon libraries of fragments of ribosomal operons of ITS2 fungi during colonization of crop roots by nitrogen-fixing bacteria in various soil and climatic zones of the Omsk region. The fungal component of the microbiome was analyzed in two zones of the Omsk region (southern forest-steppe and subtaiga). The five dominant phyla of soil fungi were located in the following decreasing series: Ascomycota (about 70%) &gt; Mortierellomycota (about 7%) &gt; Basidiomycota (about 5%) &gt; Mucoromycota (3%) &gt; Chytridiomycota (1%). The five main genera of fungi inhabiting the rhizosphere of cereals are located in a decreasing row: Giberella (6.9%) &gt; Mortierella (6.6%) &gt; Chaetomium (4.8%) &gt; Cladosporium (3.8%) &gt; Rhizopus (3.3%). The predominantly positive effect of biologics of associative nitrogen fixation on the fungal community of the soil (rhizosphere) of experimental sites located in different soil and climatic zones has been established. During seed bacterization, the growth of saprotrophic fungal genera was noted in relation to the control variants Pseudogymnoascus, Chloridium, Clonostachys, Trihoderma, etc., and the fungicidal properties of bacterial strains introduced into the soil were actively manifested relative to phytopathogenic fungi of the genera Alternaria, Blumeria, Fusarium, etc. According to the results of determining the number of infectious structures of Rhizoctonia solani, it was found that the population of the soil with viable cells of this pathogen was 1–3 pcs/g (below the threshold of harmfulness, PV 20 pcs/g of soil), which indicates a favorable phytosanitary situation with respect to the pathogen. The fungicidal effect of the applied bacterial fertilizers on Rhizoctonia solani could not be detected. The number of Bipolaris sorokiniana varied depending on the drug used. In the conditions of the southern forest-steppe zone of the Omsk region (meadow-chernozem soil), the greatest fungicidal effect was noted in Flavobacterin application variants on wheat of the Omsk 42 variety, durum wheat of the Omsk coral variety, and barley; the decrease in conidia relative to the control was 73, 35, and 29%, respectively. In the subtaiga zone of the Omsk Irtysh region (gray forest soil), as in the southern forest-steppe zone, pre-sowing bacterization of seeds with Flavobacterin led to a decrease in Bipolaris sorokiniana in the rhizosphere of wheat of the Omsk 42 variety by 18%, and oats by 27%, to control. The use of the drug Mizorin in some variants of the experiment led to an insignificant decrease in the harmful fungus or had no effect at all.

Thermodynamic Sustainability Assessment of Turkish Cash and Feed Crops: Evaluations and Recommendations

ABSTRACTThis work analyzes the sustainability status of cash crops (rice, wheat, and sunflower) and feed crops (silage and clover) cultivation in Turkey through thermodynamic sustainability assessment technique, emergy analysis (EA). EA offers insights into agricultural system's dependence on external resources, environmental loading, and renewability by classifying the resources that drive agricultural production as renewable, nonrenewable, and purchased. Data belonging to 17 agricultural systems evaluated in this work are gathered through survey method by interviewing the farmers. EA results reveal that production of cash crops is essentially unsustainable due to high dependence on externally purchased inputs fertilizer, diesel, and nonrenewable input water. Of cash crops, rice production has the lowest system renewability and the highest environmental loading. In feed crops, silage production is mainly unsustainable and clover production is in transition state in terms of sustainability. Feed crop production creates less environmental loading than cash crop production mainly due to being partially integrated with husbandry. Coinciding with this, the level of organic fertilization is found to be the determining factor in the sustainability status of the feed crop systems. Transforming these systems to sustainable ones requires increasing system's self‐sufficiency through enhanced circularity (increased nutrient recycling) and functioning of natural processes. Consequently, we recommend growing crops in polycultures rather than monocultures to benefit from sister plant and predator–prey relations, integrating crop production with animal rearing (natural fertilization) and utilizing waste‐origin bioenergy. For resource efficiency, we suggest adopting methods such as drip irrigation and utilization of biodegradable polymer‐coated fertilizer grains to prevent fertilizer runoffs.

Response of Malt Barley to NPSZnB and Urea Fertilizers Across Soil Types and Agro-ecologies, Arsi Zone of Oromia, South-Eastern Ethiopia

Field trials were conducted on farmers’ fields during the 2017, 2018, and 2019 cropping seasons to determine the optimum NPSZnB and urea fertilizer rates for selected crop, soil, and climatic conditions, as well as to assess the economic feasibility of blended and urea fertilizer rates for malt barley by varying levels of NPSZnB fertilizer (0, 100, 150, 200, 250 kg ha-1) and recommended NP in combined RCBD with three replications on growth performance and yield. The post-harvest soil analysis results of experimental sites revealed that the use of treatments significantly (p&lt; 0.05 and p&lt; 0.001) altered pH, total N, available P, and organic matter for samples taken from malt barley crop experimental sites. Application of different fertilizer levels significantly affected post-harvest pH and organic carbon contents. A significant improvement was observed in soil chemical contents compared to the contents of the soil before treatment application at Lemu-Bilbilo district. Combined levels of NPSZnB and urea fertilizer rates significantly affected grain and above-ground biomass yields at Lemu-Bilbilo and Kofele districts. The maximum grain and biomass yield (6197 and 11018 kg ha-1) in 2019 and minimum (3646 and 6875 kg ha-1) in the 2018 cropping season were obtained, respectively at Lemu-Bilbilo district up on the application of NPSZnB and urea fertilizers. Similarly, significant grain and biomass yield (5554 and 10412 kg ha-1) were obtained from the application of 250 + 200 and 250 + 150 kg ha-1 NPSZnB with urea fertilizer, respectively. The maximum grain and biomass yield (5244 and 10092 kg ha-1) in 2019 and minimum (3477 and 7736 kg ha-1) in 2017 and 2018 cropping season were obtained, respectively at Kofele district from the application of NPSZnB and urea fertilizers. Similarly, a significant grain and biomass yield (5125 and 9826 kg ha-1) were obtained from the application of 250 + 150 and 250 + 200 kg ha-1 NPSZnB + urea, respectively.

원주이씨 해남문중의 『용하도록』과 지역사 정보

In this article, we analyzed the 『Yonghadorok(用下都錄)』 owned by WonjuLee clan(原州李氏) and examined the area of Durak(斗落) in the Haenam region , the problem of place names, the opening of Mokpo Port and the rice crop house, and the drought in the Haenam region during the Japanese colonial period. The average area of 1 durak in 『Yonghadorok』 is 214 pyeong(坪), which is larger than the average of 200 pyeong in Haenam Country. And out of the 12 places analyzed, there are as many as 4 places that deviate from 200 pyeong to 10% error of 180~220 pyeong, and among them, 2 places are close to 300 pyeong. Thus, it can be seen that even within the same municipality there was a great deviation in the area of 1 durak. 『Yonghadorok』 conveys micro-place names that have not been recorded in the existing research. Accordingly, the analysis of Yonghagi(用下記) can be said to be local history information that can supplement the study of place names at dong(洞) levels. On the other hand, if you look at the 『Yonghadorok』, the same place name is written in different Chinese characters depending on the writer, which shows that even the local literate class was not well versed in the origin of the place names in their own region. The Haenam(海南) area belongs to the Mokpo cultural area, where Mokpo Port can be easily accessed by ship. With the opening of the port of Mokpo Port, the price of rice skyrocketed. Specifically, the price of rice in Haenam and Yeongam(靈巖) region soared 2 times and 2.42 times, respectively. The crop status of Yonghagi is an indirect data that can grasp the drought at the city and county level as a time series. As a result of the analysis, through in 1901, 1912, 1938, and 1942~1943, when droughts occurred in Jeolla Province(全羅道), in Haenam region the average or abundant crops were harvested. As a result, it was possible to specifically observe that the weather-related information at the Province(道) level cannot be applied to the city and county level as it is.

Towards Optimising the Derivation of Phenological Phases of Different Crop Types over Germany Using Satellite Image Time Series

Operational crop monitoring applications, including crop type mapping, condition monitoring, and yield estimation, would benefit from the ability to robustly detect and map crop phenology measures related to the crop calendar and management activities like emergence, stem elongation, and harvest timing. However, this has proven to be challenging due to two main issues: first, the lack of optimised approaches for accurate crop phenology retrievals, and second, the cloud cover during the crop growth period, which hampers the use of optical data. Hence, in the current study, we outline a novel calibration procedure that optimises the settings to produce high-quality NDVI time series as well as the thresholds for retrieving the start of the season (SOS) and end of the season (EOS) of different crops, making them more comparable and related to ground crop phenological measures. As a first step, we introduce a new method, termed UE-WS, to reconstruct high-quality NDVI time series data by integrating a robust upper envelope detection technique with the Whittaker smoothing filter. The experimental results demonstrate that the new method can achieve satisfactory performance in reducing noise in the original NDVI time series and producing high-quality NDVI profiles. As a second step, a threshold optimisation approach was carried out for each phenophase of three crops (winter wheat, corn, and sugarbeet) using an optimisation framework, primarily leveraging the state-of-the-art hyperparameter optimization method (Optuna) by first narrowing down the search space for the threshold parameter and then applying a grid search to pinpoint the optimal value within this refined range. This process focused on minimising the error between the satellite-derived and observed days of the year (DOY) based on data from the German Meteorological Service (DWD) covering two years (2019–2020) and three federal states in Germany. The results of the calculation of the median of the temporal difference between the DOY observations of DWD phenology held out from a separate year (2021) and those derived from satellite data reveal that it typically ranged within ±10 days for almost all phenological phases. The validation results of the detection of dates of phenological phases against separate field-based phenological observations resulted in an RMSE of less than 10 days and an R-squared value of approximately 0.9 or greater. The findings demonstrate how optimising the thresholds required for deriving crop-specific phenophases using high-quality NDVI time series data could produce timely and spatially explicit phenological information at the field and crop levels.

Biochar modulates the antioxidant system and hormonal signaling in tobacco under continuous-cropping conditions

ABSTRACT As an important economic crop, tobacco faces the common challenges of continuous cropping due to the limited amount of arable land. Biochar has certain preventive and control effects on continuous cropping obstacles and can improve crop yield and quality. This study aims to explore the regulation of the antioxidant system and hormone signaling in tobacco under continuous cropping conditions by biochar. We studied the impact of biochar (Nicotiana tabacum LJ911 and LY101) on plant physiological functions and transcriptomes of tobacco under continuous cropping conditions. The results showed that biochar regulates photosynthetic gas exchange parameters in both tobacco varieties. Biochar significantly increased the activity of antioxidant enzymes in the leaves of both tobacco varieties under continuous-cropping conditions. Additionally, biochar up-regulated endogenous abscisic acid, auxin, and jasmonic acid in the leaves of both tobacco varieties, with higher levels observed in LY101 than in LJ911. Through Weighted Gene Co-expression Network Analysis (WGCNA), it was found that the tobacco antioxidant system and hormones showed a significant response. Biochar improved photosynthetic efficiency, enhanced antioxidant capacity, and promoted tobacco growth through hormone synthesis and signaling. In summary, biochar application can serve as a promising strategy to mitigate the challenges associated with continuous cropping in tobacco cultivation.

Machine Learning Monitoring Model for Fertilization and Irrigation to Support Sustainable Cassava Production: Systematic Literature Review

The manual and time-consuming nature of current agronomic technology monitoring of fertilizer and irrigation requirements, the possibility of overusing fertilizer and water, the size of cassava plantations, and the scarcity of human resources are among its drawbacks. Efforts to increase the yield of cassava plants &gt; 40 tons per ha include monitoring fertilization approach or treatment, as well as water stress or drought using UAVs and deep learning. The novel aspect of this research is the creation of a monitoring model for the irrigation and fertilizer to support sustainable cassava production. This study emphasizes the use of Unnamed Aerial Vehicle (UAV) imagery for evaluating the irrigation and fertilization status of cassava crops. The UAV is processed by building an orthomosaic, labeling, extracting features, and Convolutional Neural Network (CNN) modeling. The outcomes are then analyzed to determine the requirements for air pressure and fertilization. Important new information on the application of UAV technology, multispectral imaging, thermal imaging, among the vegetation indices are the Soil-Adjusted Vegetation Index (SAVI), Leaf Color Index (LCI), Leaf Area Index (LAI), Normalized Difference Water Index (NDWI), Normalized Difference Red Edge Index (NDRE), and Green Normalized Difference Vegetation Index (GNDVI).

Customized Plant Growth Promotion with Soil- and Cultivar-Compatible Microbial Biofertilizers

Organic fertilizers and microbial biofertilizers are now widely recognized to effectively complement traditional mineral fertilizers for plant growth. The present study shows that bio-organic fertilizers can be enhanced by the addition of functional plant-growth-promoting rhizobacteria (PGPR) that provide additional benefits to plants. We hypothesized that not all beneficial soil bacteria are functional in different farm soils and plant varieties; hence, the most effective PGPR that are suitable to each farm’s individual cropping conditions were selected. Five different field soils and their respective crops were tested for compatibility with six microbial biofertilizers (including three new bacterial strains) to supplement a commercially available bio-organic fertilizer. In pot trials with lucerne plants, four out of the six microbial treatments led to significant (p &lt; 0.05) growth promotion benefits (up to 79.8% more leaves and dry weight) compared to mock-treated or bio-organic fertilizer-only-treated control plants. A trial with industrial hemp demonstrated that compatibility with PGPR occurs in a cultivar-specific manner, leading to growth promotion ranging from −3.4% to 68.9%, with each cultivar displaying a preference for a different PGPR. Finally, pot trials with Rhodes grass and two different soils demonstrated high yield increases compared to control plants, with Bacillus amyloliquefaciens 33YE being most effective for one soil and Bacillus velezensis UQ9000N/Pseudomonas lini SMX2 for the other soil. Yield advantages reduced after several cuts of grass, but a repeat biofertilizer treatment at 69 days after the initial treatment restored high yield advantages, with the same PGPR again being most effective. These results demonstrate the importance of customization of microbial inoculants to identify the most compatible PGPR–cultivar–soil interaction. The customization of microbial biofertilizers to soils and plant cultivars, combined with complementary fertilizer applications, can potentially lead to more reliable and more sustainable agricultural practices.

Spatial-spectral feature extraction for in-field chlorophyll content estimation using hyperspectral imaging

In-situ leaf chlorophyll content (LCC) estimation based on hyperspectral imaging (HSI) is crucial to track the growth status of crops for field management. However, spatial and spectral features of HSI data, suffering from interference of growth dynamic effect and soil, pose the challenge on accuracy and robustness of LCC estimation in several years and growth stages. Therefore, a joint spectral-spatial feature extraction method was proposed by cascade of three-dimensional convolutional neural network (3DCNN) and long short-term memory (LSTM) to reduce the interference for optimising the LCC estimation. Firstly, crop pixels were separated from soil with vegetation index segmentation method. Secondly, when raw images and segmented pixels were input, sensitive bands were selected by random frog (RF bands), and 3DCNN-LSTM was used to extract the joint spectral-spatial features. Finally, models established by RF bands, 3DCNN and 3DCNN-LSTM were compared, and robustness in individual years and stages was validated. Results showed that RF bands and 3DCNN obtained RP2 of 0.76 and 0.84 when not segmented. After segmentation, performance of 3DCNN improved (RP2 = 0.85) compared to RF bands (RP2 = 0.80). Spectral-spatial features by 3DCNN reduced the interference of soil. 3DCNN-LSTM without and with segmentation obtained good performance with RP2 of 0.95 and 0.96, and the proposed method could reduce the image segmentation process. The optimal model achieved RP2 above 0.93 in individual years (RP2 = 0.96 in 2021, RP2 = 0.94 in 2021) and RP2 in the range of 0.87–0.97 at individual stages. This paper provides a method to track growth variability between soil and crop for the LCC estimation optimisation.

Measuring the Influence of Key Management Decisions on the Nitrogen Nutritional Status of Annual Ryegrass-Based Forage Crops

Measuring the Influence of Key Management Decisions on the Nitrogen Nutritional Status of Annual Ryegrass-Based Forage Crops

Estimation of macro and micronutrient concentrations in ‘valencia’ orange leaves at different phenological stages using multispectral image analysis and multiple regression

ABSTRACT This study aimed at estimating the macro- and micronutrient concentrations in leaves of Valencia oranges (Citrus sinensis [L.] Osbeck) through multiple regression analysis using multispectral images and vegetation indices (VIs) selected through the stepwise regression method. The data were collected in a commercial orchard of Valencia oranges in El Barretal, Tamaulipas, Mexico. The leaf samples were analysed in a laboratory to determine the concentrations of N, P, K, Ca, Mg, Cu, Fe, Mn, and Zn using traditional chemical methods; the relative chlorophyll values were obtained using a chlorophyll metre (SPAD502 Plus). Multi-spectral images of the tree canopy were acquired using a UAV equipped with a multispectral camera with five wavelengths (blue, green, red, red edge, and near-infrared); based on these data, 85 VIs were calculated. To select the predictor variables that were most relevant for the model, 90 candidate variables were divided into three groups with 30 VIs grouped by nutrients based on the highest and lowest absolute values found in the correlation matrix. Heteroscedasticity and autocorrelation were also assessed through the Breusch-Pagan and Durbin-Watson tests, respectively. The selected variables were used in multiple regression analysis. The resulting models were validated by mean absolute error (MAE), root-mean-square error (RMSE), mean absolute percentage error (MAPE), and coefficient of determination ( R 2 ). The prediction models showed satisfactory results, with values R 2 between 0.79 and 0.95 and relatively low (<10) MAE and RMSE for all nutrients, except Fe ( < 30 ) in all phenological stages; MAPE values were lower than 0.5% for all nutrients. This study showed that multispectral imaging combined with multiple regression analysis effectively predicts nutrient levels in the leaves of Valencia oranges in various phenological stages. This study presents a new perspective on agricultural systems, offering an effective alternative to assess the nutritional conditions of crops.

Host status of cover crops for the management of Meloidogyne izalcoensis in coffee orchards infested with root-knot nematodes

Summary The root-knot nematode Meloidogyne izalcoensis was described parasitising coffee trees and causing damage in the Izalco volcano region, Sonsonate, El Salvador. In Brazil, it was detected in coffee roots in the municipality of Indianópolis, Minas Gerais state (MG). Intercropping cover crops is a traditional practice in Brazilian coffee orchards, and the use of plants that are non-hosts or antagonistic to the nematodes is a very promising strategy for coffee in infested areas. Due to the scarce information on the host status of M. izalcoensis, the aim of this study was to evaluate 24 plants of different species of cover crops and select the non-hosts or poor hosts. Two assays were conducted at different times, and all plants were inoculated with 10 000 eggs. After 90 days, the following parameters were determined: fresh root weight, reproduction factor (RF) and final reaction. Plants with RF &lt; 1.0 were classified as non-hosts (NH), those with RF &gt; 1.0 were classified as hosts and using statistical analyses as: good host (GH), intermediate host (IH) and poor host (PH). Most of the botanical species tested were classified as NH or PH to M. izalcoensis: cotton, rice, white oat, black oat, ryegrass, corn, three species of grass, two cultivars of millet, two wheat cultivars, Crotalaria breviflora, C. ochroleuca, C. spectabilis, jack bean, gray mucuna bean and two species of rice grass. Only tomato and bean were classified as GH, and C. juncea and soybean were classified as IH. The plants identified as non-hosts or poor hosts can be recommended for intercropping or for crop rotation in Brazilian coffee orchards in regions infested by M. izalcoensis.

Crop health assessment through hierarchical fuzzy rule-based status maps

Precision agriculture is evolving toward a contemporary approach that involves multiple sensing techniques to monitor and enhance crop quality while minimizing losses and waste of no longer considered inexhaustible resources, such as soil and water supplies. To understand crop status, it is necessary to integrate data from heterogeneous sensors and employ advanced sensing devices that can assess crop and water status. This study presents a smart monitoring approach in agriculture, involving sensors that can be both stationary (such as soil moisture sensors) and mobile (such as sensor-equipped unmanned aerial vehicles). These sensors collect information from visual maps of crop production and water conditions, to comprehensively understand the crop area and spot any potential vegetation problems. A modular fuzzy control scheme has been designed to interpret spectral indices and vegetative parameters and, by applying fuzzy rules, return status maps about vegetation status. The rules are applied incrementally per a hierarchical design to correlate lower-level data (e.g., temperature, vegetation indices) with higher-level data (e.g., vapor pressure deficit) to robustly determine the vegetation status and the main parameters that have led to it. A case study was conducted, involving the collection of satellite images from artichoke crops in Salerno, Italy, to demonstrate the potential of incremental design and information integration in crop health monitoring. Subsequently, tests were conducted on vineyard regions of interest in Teano, Italy, to assess the efficacy of the framework in the assessment of plant status and water stress. Indeed, comparing the outcomes of our maps with those of cutting-edge machine learning (ML) semantic segmentation has indeed revealed a promising level of accuracy. Specifically, classification performance was compared to the output of conventional ML methods, demonstrating that our approach is consistent and achieves an accuracy of over 90% throughout various seasons of the year.