Optimum Crop Yield Research Articles

Predicting tomato water consumption in a hydroponic greenhouse: contribution of light interception models

In recent years, hydroponic greenhouse cultivation has gained increasing popularity: the combination of hydroponics’ highly efficient use of resources with a controlled environment and an extended growing season provided by greenhouses allows for optimized, year-round plant growth. In this direction, precise and effective irrigation management is critical for achieving optimal crop yield while ensuring an economical use of water resources. This study explores techniques for explaining and predicting daily water consumption by utilizing only easily readily available meteorological data and the progressively growing records of the water consumption dataset. In situations where the dataset is limited in size, the conventional purely data-based approaches that rely on statistically benchmarking time series models tend to be too uncertain. Therefore, the objective of this study is to explore the potential contribution of crop models’ main concepts in constructing more robust models, even when plant measurements are not available. Two strategies were developed for this purpose. The first strategy utilized the Greenlab model, employing reference parameter values from previously published papers and re-estimating, for identifiability reasons, only a limited number of parameters. The second strategy adopted key principles from crop growth models to propose a novel modeling approach, which involved deriving a Stochastic Segmentation of input Energy (SSiE) potentially absorbed by the elementary photosynthetically active parts of the plant. Several model versions were proposed and adjusted using the maximum likelihood method. We present a proof-of-concept of our methodology applied to the ekstasis Tomato, with one recorded time series of daily water uptake. This method provides an estimate of the plant’s dynamic pattern of light interception, which can then be applied for the prediction of water consumption. The results indicate that the SSiE models could become valuable tools for extracting crop information efficiently from routine greenhouse measurements with further development and testing. This, in turn, could aid in achieving more precise irrigation management.

Insights into molecular links and transcription networks integrating drought stress and nitrogen signaling.

Drought and the availability of nitrate, the predominant source of nitrogen (N) in agriculture, are major factors limiting plant growth and crop productivity. The dissection of the transcriptional networks' components integrating droght stress and nitrate responses provides valuable insights into how plants effectively balance stress response with growth programs. Recent evidence in Arabidopsis thaliana indicates that transcription factors (TFs) involved in abscisic acid (ABA) signaling affect N metabolism and nitrate responses, and reciprocally, components of nitrate signaling might affect ABA and drought gene responses. Advances in understanding regulatory circuits of nitrate and drought crosstalk in plant tissues empower targeted genetic modifications to enhance plant development and stress resistance, critical traits for optimizing crop yield and promoting sustainable agriculture.

Mineral Nitrogen Release Patterns in Various Soil and Texture Types and the Impact of Urea and Coated Urea Potassium Humate on Barley Biomass

Controlled-release urea fertilization is an innovative approach and effective means to reduce the loss of nitrogen and enhance fertilizer use efficiency to optimize crop yield while minimizing the environmental impact. The objective of this study was to investigate the dynamic process of mineral nitrogen (Nmin) release in Luvisols, Cambisols, Retisols, and Arenosols to understand the interaction between soil characteristics and mineral nitrogen release and evaluate the impact of conventional urea compared to coated urea potassium humate on barley biomass production. A pot experiment was conducted under glasshouse conditions (20 ± 2 °C and 60% Humidity). Five treatments including no fertilization as a control (C), 100 kg·N·ha−1 of urea (U100), 200 kg·N·ha−1 of urea (U200), 100 kg·N·ha−1 of coated urea potassium humate (UPH100), and 200 kg·N·ha−1 of coated urea potassium humate (UPH200) were applied to four soil and texture types. Our findings indicate that there are different patterns of mineral nitrogen release across the different soil and texture types. Ammonium levels reached their peak point in all soils within 2–7 days after application. On the other hand, the concentration of nitrate NO3−–N showed a linear increase over 45 days during the experiment. The Retisol, which had a sandy clay texture, obtained the highest concentration of mineral nitrogen in both forms (NH4 and NO3), while the sandy texture of Arenosol showed the lowest accumulation of mineral nitrogen and its forms. The application of potassium humate caused a delay of 1–4 days in the peak of soil ammonium, which at peak accounted for approximately 25–44% of the mineral nitrogen in the soil. Furthermore, the application of urea and coated urea potassium humate exhibited significant effects on barley biomass with an increase of approximately 14–91% compared to the unfertilized treatment (control). This research contributes to our understanding of nutrient dynamics in diverse soil environments and provides insights into optimizing sustainable fertilization strategies such as controlled-release fertilizer application. The implications of these findings highlight the significance of tailored nutrient management practices based on soil texture type, which can lead to improved agricultural productivity and environmental impact.

Effects of climate factors on wheat and maize under different crop rotations and irrigation strategies in the North China Plain

The North China Plain (NCP) is a crucial agricultural region for grain production in China, primarily focusing on wheat and maize cultivation. However, these crop yields are highly vulnerable to fluctuations in temperature and precipitation, with climate change being a significant factor. This study investigates the impact of climate factors on wheat and maize yields in the NCP under various crop rotations and water supply strategies. Using the Agricultural Production Systems sIMulator crop mechanism model, we evaluated the effects of different crop rotations and water supply strategies on wheat and maize yields. A comprehensive analysis of the simulated results determined the yield variation trends and their correlation and sensitivity to different climate factors. The findings revealed that precipitation levels over the past 40 years showed no significant trend, while radiation levels showed a significant decreasing trend, and annual mean maximum and minimum temperatures displayed significant increasing trends. Furthermore, the study found that irrigation practices and crop rotations substantially impact grain yield in the study area, with average yields ranging from 8105.5 kg ha−1 under rainfed conditions to 13 088.8 kg ha−1 under fully irrigated conditions. There was a statistically significant trend of increasing yields for fully irrigated Monocrop-Wheat and decreasing yields for fully irrigated Monocrop-Maize over the past 40 years. Sensitivity analysis also showed that rational rotation and irrigation can reduce grain yield sensitivity to climate change. In conclusion, the prudent use of rotation and irrigation can enhance food production resilience to climate change. These findings have significant implications for developing strategies to optimize crop yields and adapt to climate change in the NCP while considering trade-offs.

Future Rainfall Forecasting for Water Supply Management in Agriculture

Precipitation forecasting and water supply management enable farmers to make informed decisions, optimize crop yields, reduce risks, conserve resources, and contribute to food security and economic stability. Important for agriculture. Actual rainfall is often not sufficient to support plant growth. Therefore, the aforementioned knowledge will help farmers estimate the amount of water provided through different farming techniques. Accurate forecasting helps farmers manage water according to crop needs. This information is critical to maximizing agricultural productivity. This article examines the rainfall forecasting method by analysing key parameters such as maximum temperature, minimum temperature, maximum humidity, minimum humidity, evaporation, average wind speed, and wind direction for Pune district, Maharashtra. Furthermore, it takes into account the water requirements of commonly cultivated crops. This holistic analysis informs the decision-making process regarding crop irrigation, determining both the necessity and the appropriate volume of water allocation to the crops

Revolutionizing Agriculture through Soil-Driven Crop Selection

Abstract: Crop recommendation systems are becoming increasingly popular in agriculture due to their ability to optimize crop yield and improve efficiency. These systems rely on various factors such as soil quality, temperature, soil moisture, humidity, and pH to recommend suitable crops to grow in a particular area. The proposed project aims to develop a crop recommendation system that utilizes soil quality tests, temperature, soil moisture, humidity, and pH to recommend suitable crops to farmers. The system will use machine learning algorithms to analyze the data collected from the soil quality tests and environmental sensors to provide recommendations.

A Scientific Assessment of Integrated Nutrient Management Effects on the Growth and Productivity of Trichosanthes anguina L. (Snake Gourd)

A field-based experimental investigation was undertaken from March to July 2020 at the Horticultural Farm of Sher-e-Bangla Agricultural University (SAU) in Dhaka, Bangladesh, to assess the impact of integrated nutrient management on the growth and yield of snake gourd (Trichosanthes anguina L.). The study employed a Randomized Complete Block Design with three replications, incorporating a total of 11 nutrient combinations. Various growth, reproductive, and yield parameters, including vine length, number of leaves per plant, leaf dimensions, flowering onset, fruit development, and yield metrics, were evaluated. The results consistently demonstrated positive effects across these parameters in response to different nutrient combinations. The highest recorded yield of snake gourd, at 50.10 t ha-1, was observed in the T10 ((OM (CD5PM5V2 t ha-1) + N80P35K75S18 kg ha-1 + Mg7 kg ha-1 + Zn6 kg ha-1 + B2 kg ha-1) treatment, while the lowest yield, at 14.72 t ha-1, was associated with the T0 (control) treatment. This experiment highlights the prospective efficacy of integrated nutrient management methodologies in augmenting snake gourd production and optimizing crop yield. It provides valuable insights for the improvement of agricultural practices in the specified geographic region.

Assessment of Combined Reflectance, Transmittance, and Absorbance Hyperspectral Sensors for Prediction of Chlorophyll a Fluorescence Parameters

Photosynthesis is a key process in plant physiology. Understanding its mechanisms is crucial for optimizing crop yields and for environmental monitoring across a diverse range of plants. In this study, we employed reflectance, transmittance, and absorbance hyperspectral sensors and utilized multivariate statistical techniques to improve the predictive models for chlorophyll a fluorescence (ChlF) parameters in Hibiscus and Geranium model plants. Our objective was to identify spectral bands within hyperspectral data that correlate with ChlF indicators using high-resolution data spanning the electromagnetic spectrum from ultraviolet to shortwave infrared (UV–VIS–NIR–SWIR). Utilizing the hyperspectral vegetation indices (HVIs) tool to align importance projection for wavelength preselection and select the most responsive wavelength by variable importance projection (VIP), we optimized partial least squares regression (PLSR) models to enhance predictive accuracy. Our findings revealed a strong relationship between hyperspectral sensor data and ChlF parameters. Employing principal component analysis, kappa coefficients (k), and accuracy (Acc) evaluations, we achieved values exceeding 86% of the predicted ChlF parameters for both Hibiscus and Geranium plants. Regression models for parameters such as Ψ(EO), ϕ(PO), ϕ(EO), ϕ(DO), δRo, ρRo, Kn, Kp, SFI(abs), PI(abs), and D.F. demonstrated model accuracies close to 0.84 for R2 and approximately 1.96 for RPD. The spectral regions linked with these parameters included blue, green, red, infrared, SWIR1, and SWIR2, emphasizing their relevance for noninvasive evaluations. This research demonstrates the ability of hyperspectral sensors to noninvasively predict chlorophyll a fluorescence (ChlF) parameters, which are essential for assessing photosynthetic efficiency in plants. Notably, hyperspectral absorbance data were more accurate in predicting JIP-test-based chlorophyll a kinetic parameters. In conclusion, this study underscores the potential of hyperspectral sensors for deepening our understanding of plant photosynthesis and monitoring plant health.

Combining Straw Mulch with Nitrogen Fertilizer Improves Soil and Plant Physio-Chemical Attributes, Physiology, and Yield of Maize in the Semi-Arid Region of China.

Mulching and nitrogen (N) fertilization are the main drivers for sustainable crop production. The sole use of nitrogen fertilizer threatened both the physiology and production of maize in rain-fed areas. Therefore, we proposed that wheat straw mulching with N fertilization would increase maize yield by improving soil fertility, physiology, and nitrogen use efficiency. A two-year field study evaluated the effects of CK (control), N (nitrogen application at 172 kg ha-1), HS (half wheat straw mulch, 2500 kg ha-1), HS+N (half wheat straw, 2500 kg ha-1 plus 172 kg N ha-1), FS (full wheat straw, 5000 kg ha-1), and FS+N (full wheat straw, 5000 kg ha-1 plus 172 kg N ha-1) on maize growth, physiology, and biochemistry. Compared with the control, the FS+N treatment resulted in the increase of 56% photosynthetic efficiency, 9.6% nitrogen use efficiency, 60% nitrogen uptake, 80% soluble sugar, 59% starches, 48% biomass, and 29% grain yield of maize. In addition, the FS+N regime increased 47%, 42%, and 106% of soil organic carbon and available P and N content in comparison with the control. Maize grain and biomass yields were positively correlated with N uptake, photosynthesis, soil organic carbon, and soil available N and P contents. Conclusively, the use of wheat straw at 5000 kg ha-1, along with 172 kg N ha-1, is a promising option for building a sustainable wheat-maize cropping system to achieve optimal crop yield and improved plant and soil health in a semi-arid region of China.

Agricultural Sustainability in the Age of Deep Learning: Current Trends, Challenges, and Future Trajectories

Agriculture stands as the essential foundation of human sustenance, confronting the dual challenge of providing for a burgeoning global populace while safeguarding the integrity of the natural environment. This comprehensive review paper undertakes an exhaustive exploration of the continually evolving sphere of agricultural sustainability, traversing the multifaceted terrain of present-day trends, technological innovations, and the promising trajectories that lie ahead. From the vantage point of precision agriculture and climate-smart methodologies to the strategic integration of deep learning technologies, it offers a comprehensive examination of pioneering approaches that are redefining the agricultural domain. Within, it elucidates the intrinsic relationship between agriculture and sustainability, exemplifying how judicious resource management, the preservation of biodiversity, and the implementation of circular agricultural practices herald an epoch of conscientious agrarian practices. Moreover, this study casts an illuminative gaze toward the future of agriculture, wherein quantum intelligence, meta-learning, deep reinforcement learning, curriculum learning, intelligent nanothings, blockchain technology, and CRISPR gene editing converge to furnish innovative solutions. These solutions aspire to optimize crop yields, mitigate ecological footprint, and fortify global food security. As this academic voyage commences, it is incumbent to reiterate the pivotal assertion that sustainability in agriculture is not merely a desideratum; it is a compelling mandate, and the seeds of transformative innovation have been sown to recalibrate the world's approach to food production and environmental stewardship.

Pesticide biosensors: trends and progresses.

Pesticides, chemical substances extensively employed in agriculture to optimize crop yields, pose potential risks to human and environmental health. Consequently, regulatory frameworks are in place to restrict pesticide residue concentrations in water intended for human consumption. These regulations are implemented to safeguard consumer safety and mitigate any adverse effects on the environment and public health. Although gas chromatography- and liquid chromatography-mass spectrometry (GC-MS and LC-MS) are highly efficient techniques for pesticide quantification, their use is not suitable for real-time monitoring due to the need for sophisticated laboratory pretreatment of samples prior to analysis. Since they would enable analyte detection with selectivity and sensitivity without sample pretreatment, biosensors appear as a promising alternative. These consist of a bioreceptor allowing for specific recognition of the target and of a detection platform, which translates the biological interaction into a measurable signal. As early detection systems remain urgently needed to promptly alert and act in case of pollution, we review here the biosensors described in the literature for pesticide detection to advance their development for use in the field.

Biochar Application: An effective Measure in Improving the Fertility Status, Carbon Stock and Aggregate Stability of Eroded Soil

In search for an efficient means of building up the carbon stock, improving the fertility levels and aggregate stability of tropical soils for optimum crop yield, a field study was carried using different biochars and comparing the effects with inorganic fertilizer. The biochars were palm bunch biochar (PBB), saw dust biochar (SDM) and rice mill husk biochar (RMHB). Treatments consisted of 10 t/ha palm bunch biochar + 0.25t/ha poultry manure (T1), 10 t/ha rice mill husk biochar + 0.25t/ha poultry manure (T2), 10 t/ha saw dust biochar + 0.25t/ha poultry manure (T3), 500kg/ha N.P.K 15:15:15 fertilizer + 0.25t/ha poultry manure (T4) and plot without biochar + 0.25t/ha poultry manure (T5) (control plot). These were replicated five times on experimental plots of 4m2 in a randomized complete block design. Maize (Zea mays) was used as a test crop and data obtained were statistically analyzed using analysis of variance and correlation. Soils amended with biochars significantly improved soil pH, organic carbon, exchangeable bases and base saturation than non biochar fertilized soils. Saw dust biochar increased soil carbon stock by 95.1% against NPK fertilizer plots and control. There was 19% decrease in soil bulk density and 17% increase in soil pH with application of palm bunch biochar. Amending soils with palm bunch biochar increased soil organic carbon by 51.5%. The biochars increased the values of critical level of soil organic matter, modifies clay ratio and reduced the value of clay flocculation index more than NPK fertilized soils or control. Among the treatments, rice mill husk biochar recorded the highest maize cob weight though not significant with palm bunch biochar. Therefore, applying biochars on eroded soil is an effective measure of improving the stability, soil carbon stock as well as enhancing higher maize yield than inorganic fertilizer.

Deployment of an Artificial Intelligent Robot for Weed Management in Legumes Farmland

This groundbreaking research introduces an AI-based approach for revolutionizing weed management in legume farmland, addressing the limitations of traditional methods and introducing a new era of cost-effective and precise weed detection and removal. Traditional methods of removing weeds from farmland involving machinery or chemicals often resulted in high costs and imprecise outcomes. To address these challenges, an advanced image recognition algorithm was proposed, which harnessed smart machines to minimize costs and environmental risks. By utilizing computer vision technology, weeds were accurately identified and targeted for removal. A machine learning model was trained using relevant datasets to enable precise weed management. The AI-powered robot, equipped with advanced image recognition algorithms, demonstrated exceptional accuracy and speed, performing weed removal and decomposition 1.2 times faster than traditional manual labour. This breakthrough in weed management technology offers farmers a means to optimize crop yields, enhance food production, and minimize the environmental impact associated with chemical herbicides. A prototype of the robot was fabricated and evaluated in real-world farming conditions. Field tests were conducted on a bean farm and it’s demonstrated the robot's exceptional accuracy, with only a 2% deviation from the actual weed quantity. This research showcased the potential of AI-based weed management systems in legume farming, offering cost-effective and precise weed detection and removal. This research sets a precedent for the integration of AI in modern agriculture, driving the industry toward a more environmentally conscious and economically viable future. The AI-based weed management system empowers farmers, ensuring bountiful harvests, increased profitability, and a greener, more sustainable tomorrow while attention should be given to manufacturing this model for industrial and or commercial applications.

Shading and PAR under different density agrivoltaic systems, their simulation and effect on wheat productivity

The introduction of renewable energy to mitigate climate change and the need for sufficient land to increase food production are mutually exclusive. Agrivoltaic systems (AVS) integrate the production of agricultural crops and electric power on the same piece of land but the main issue is the reduced availability of light to crops below the AVS, which restricts optimum crop growth and yield. This study examines the radiation and shade distribution over the crop surface among three densities of photovoltaic (PV) panels {Partial density (PD), Half density (HD) and Full density (FD)} under the AVS. Wheat variety GW 496 was chosen to grow under the AVS with line sowing and drip irrigation. Among three densities of photovoltaic (PV) panels, the proportion of shaded area over the crop surface was found highest in full density plot and lowest in partial density plot. The shaded area under AVS varied from 24.1% to 75.4% of the total area. The shaded area under different density plots, adversely affects the availability of photosynthetically active radiation (PAR) over the crop surface. The reduction of PAR under the AVS was more affected by panel density. Maximum reduction in PAR under AVS was observed in FD plot and minimum in the PD plot. A sharp increase in the PAR level was observed from shading to open sun conditions. The lowest value of PAR level (56 μmol m−2 s1) was recorded in just below the panel’s array. The consistent overestimation of PAR was observed during the crop growing period in all the PVPs density plots. The result revealed that the grid points under shade conditions showed a better association between observed and simulated incident PAR than unshaded grid points. Furthermore, the simulation results revealed that the model modeled PAR better in PD plot. The study also found that wheat grown under no shade yielded 4.51 t/ha over reduced light either in PD (3.06 t/ha), HD (2.77 t/ha), and FD plots (2.62 t/ha). Shading treatments showed that the crop yield decreased with an increase in shading intensity.

Response of Direct Seeded Rice to Iron and Zinc Fertilization Strategies under Middle Gujarat Conditions

Direct seeded rice (DSR) cultivation has received considerable attention due to its economic and labor-saving benefits. However, inadequate nutrient availability, particularly micronutrients, remains a significant constraint in achieving optimal crop growth and yield in direct seeded rice system. This research paper presents a comprehensive analysis of the impact of iron and zinc fertilization on yield, productivity and nutritional quality of direct seeded rice. This experiment was conducted at Experimental Farm, Regional Research Station, Anand Agricultural University, Anand, Gujarat, during kharif season of the year 2022 with twelve treatments of iron and zinc fertilization strategies viz. nutripriming, foliar application and soil drenching which were replicated thrice in randomized block design (RBD). In the treatments of nutripriming, seed were soaked in 0.5% solutions of iron sulphate and zinc sulphate individually and also in combination for 12 hrs. In other treatments, solutions of 0.5% concentration of iron sulphate and zinc sulphate were applied through foliar application at 30 and 50 DAS and soil drenching. Soil drenching was done by adding nutrient solutions (0.5% of iron sulphate and zinc sulphate each) directly on the rice sown lines of the plots. Findings demonstrate that targeted application of micronutrients (Fe and Zn) significantly enhanced the DSR performance in form of growth parameters, yield attributes, yield and nutrient content of grain and straw. Among all the treatments, (T5) nutripriming of direct seeded rice with 0.5% iron sulphate plus 0.5% zinc sulphate for 12 hours before sowing along with recommended dose of fertilizers has been found best in terms of plant height, panicles per m2, yield and Fe and Zn content in seed and straw of rice.

Effect of Potassium Application Rates on Sugarcane Yield in Soils with Different Non-Exchangeable Potassium Reserves and Fixation Capacity

Reliable fertilizer recommendations should account for factors influencing nutrient supply, including non-exchangeable potassium (K) reserves and fixation capacity, to ensure optimum crop yields. The combined effects of non-exchangeable K reserves and fixation capacity of soils on crop response to K application has not been evaluated. This study evaluated the response of sugarcane yields to K application on two soils with contrasting combinations of non-exchangeable K reserves and fixation capacity. Potassium was applied at 0, 120, and 240 kg K ha−1 at the commencement of the field trials and after each harvest on an umbric Acrisol, which had low non-exchangeable K reserves and medium K fixation capacity, and a cutanic Acrisol, which had ‘very high’ non-exchangeable K reserves and fixation capacity. Sugarcane stalk and sucrose yield, leaf, and exchangeable K were measured for each season. In the umbric Acrisol, a lower sucrose yield was measured in the 240 kg K ha−1 treatment compared to the control for the plant crop, but this application rate had higher yields for the second ratoon. In contrast, there was no yield response to K application in cutanic Acrisol. This study indicated the importance of non-exchangeable K reserves and fixation capacity when calculating K requirements and conducting field calibration studies.

The Role of Value Orientations and Media Attention in Predicting the Personal Norm and Public Intention to Consume Produce of Urban Farms

ABSTRACT In a bid to strengthen food security, cities are embracing urban farming to produce food. It is believed that urban farming is a sustainable means of cultivating crops for urban populations. Urban farms are equipped with advanced technology to optimize crop yield. Meanwhile, multiple media contents have reviewed the risks and benefits of urban farming for consumers. Applying an extended norm-activation model, this study examined how the relationships among technological optimism, biospheric values, attention to media content on the risks and benefits of urban farming, and the norm-activation constructs predict the intention to consume the produce of urban farms. Results of a cross-sectional survey conducted in Singapore (N = 1209) showed that technological optimism and biospheric values influenced attention to media content. These variables in turn influenced personal norms and intention to consume through the norm-activation constructs.

Vertical Distribution of Forms of Sulphur in Relation to Physicochemical Properties of Lateritic Soils of Eastern India

Sulphur (S) deficiency is widespread in Indian soils, and it has been emerging as a major problem in rice-growing lateritic soils of West Bengal. The vertical distribution of S forms and their relationships with soil physicochemical properties were examined in 50 rice-growing locations of lateritic soils of West Bengal, India, for the current study. For this a total of one hundred fifty representative soil samples were collected from three depths and fifty representative locations of rice-based cropping systems of lateritic belt of West Bengal and analyzed for different fractions of S and important physicochemical properties using standard methodology. Most soils were sandy clay loam to clay loam, with low to medium levels of organic carbon, and strongly acidic to moderately acidic. A decrease in organic carbon across the depths was evident. Among the sulphur forms, organic S was dominant, and the contribution of water-soluble S was least throughout the soil profile. The studied sulphur forms followed the order: total S > organic S > heat soluble S > adsorbed S > sulphate S > water soluble S. Except for adsorbed S, most forms decreased with soil depth. The correlation study revealed that soil pH was positively and significantly correlated with water soluble (r = 0.431**) and sulphate S (r = 0.325*) in surface soils, with sulphate S both in mid-surface (r = 0.450**) and subsurface soils (r = 0.423**). Organic carbon showed a positive correlation with all the S forms throughout the profile. Both clay and silt content positively affected S forms, while sand content showed a negative relationship. Positive correlation among all the S forms observed along soil depth, except for a negative correlation between adsorbed S and sulphate S. This investigation will be helpful for sulphur management to optimize crop yields in lateritic soils.

A comparison of soil liming requirement methodologies in temperate, Northern European pedo‐climates

AbstractBackgroundLiming agricultural land is essential to optimise crop yield and soil nutrients. Despite the importance of pH management in agricultural soils, liming applications have been decreasing in the United Kingdom for decades. There is no comparison of contemporary and historical liming requirement (LR) methods for Northern European, temperate climate mineral soils high in organic matter (OM).AimsThe aims of this research were to thoroughly comparatively analyse current methodologies and to ascertain which soil characteristics contribute to LR reactions.MethodsAnalysis compared methods for determining liming values common in the United Kingdom (Scottish Agricultural College [SAC] look‐up chart, RothLime model), Europe and the United States (Shoemaker–McLean–Pratt, Sikora, Modified Mehlich buffers), and the 30‐min calcium hydroxide titration developed by the University of Georgia.ResultsRothLime and SAC highly underestimated the LR value in acidic soils. The buffers highly over or underestimated LRs. The UGA titration method is a cheap, easy and accurate method which could be utilised for high OM soils but requires further calculation development. The characteristics most associated with soil–lime reactions in this experiment were measures of exchangeability (cation exchange capacity and loss on ignition, and by proxy, lime buffering capacity).ConclusionsThere is an opportunity to create buffer calculators and titration equations adapted to high OM soils. These are suggested for further development, through a larger diversity of UK soil types grouped by buffering capacity ranges. Including soil exchangeability factors in lime management calculations may contribute to more accurate values and therefore better resource management. Increasing LR accuracy for site‐specific soil pH management, used in precision agriculture technologies, is a necessary tool for the conservation of natural resources like limestone, managing resource use efficiency, and for optimising yields.

Toward automated irrigation management with integrated crop water stress index and spatial soil water balance

AbstractDecision support systems intended for precision irrigation aim at reducing irrigation applications while optimizing crop yield to achieve maximum crop water productivity (CWP). These systems incorporate on-site sensor data, remote sensing inputs, and advanced algorithms with spatial and temporal characteristics to compute precise crop water needs. The availability of variable rate irrigation (VRI) systems enables irrigation applications at a sub-field scale. The combination of an appropriate VRI system along with a precise decision support system would be ideal for improved CWP. The objective of this study was to compare and evaluate two decision support systems in terms of seasonal applied irrigation, crop yield, and CWP. This study implemented the Spatial EvapoTranspiration Modeling Interface (SETMI) model and the Irrigation Scheduling Supervisory Control and Data Acquisition (ISSCADA) system for management of a center pivot irrigation system in a 58-ha maize-soybean field during the 2020 and 2021 growing seasons. The irrigation scheduling methods included: ISSCADA plant feedback, ISSCADA hybrid, common practice, and SETMI. These methods were applied at irrigation levels of 0, 50, 100, and 150% of the full irrigation prescribed by the respective irrigation scheduling method. Data from infrared thermometers (IRTs), soil water sensors, weather stations, and satellites were used in the irrigation methods. Mean seasonal irrigation prescribed was different among the irrigation levels and methods for the 2 years. The ISSCADA plant feedback prescribed the least irrigation among the methods for majority of the cases. The common practice prescribed the largest seasonal irrigation depth among the methods for three crop-year cases. The maize yield in rainfed was found to be significantly lower than the irrigated levels in 2020 since 2020 was a dry year. No significant differences were observed in crop yield among the different irrigation methods for both years. The CWP among the different irrigation methods ranged between 2.72 and 3.15 kg m−3 for 2020 maize, 1.03 and 1.13 kg m−3 for 2020 soybean, 3.57 and 4.24 kg m−3 for 2021 maize, and 1.19 and 1.48 kg m−3 for 2021 soybean. Deficit level (50%) had the largest irrigation water productivity in all crop-year cases in this study. The ISSCADA and SETMI systems were found to reduce irrigation applications as compared to the common practice while maintaining crop yield. This study was the first to implement the newly developed integrated crop water stress index (iCWSI) thresholds and the ISSCADA system for site-specific irrigation of maize and soybean in Nebraska.