Optimal Plant Research Articles

Chemical plant optimization layout based on the domino hazard index considering the fixed hazard unit outside the available area

The layout of chemical plants is a significant step to promote the safe and efficient operation of a chemical factory. In the actual production process, the chemical process plant layout may be affected by immovable hazards outside the available layout area. In this paper, the external hazard unit is arranged as one of the chemical process plants. The domino effect potential of all chemical process plants is quantified by using the classical domino hazard index (DHI). A layout optimization model considering the effects of external hazard units has been proposed to obtain the optimal plant layout from economic and safety aspects. The feasible layout solutions are obtained under the orientation, boundary and non-overlapping constraints by the non-dominated sorting genetic algorithm II (NSGA-Ⅱ). The proposed model is further analyzed through a hypothetical case of three storage tanks outside the acrylic acid (AA) production plant layout area. The results show that the total cost range for the optimal solution is 650348–1365443 $, with corresponding DHI values ranging from 29 to 129. The plant with larger DHI value will be arranged to far away from the external hazard units to obtain a safer layout. This work can provide rational layout solutions for the layout designers to make different decisions according to the actual situation.

Towards a better understanding of auxin response factors for improving cereal crops

The auxin response factor (ARF) is the key component of phytohormone auxin signal pathway and is crucial in regulating a plurality of functions throughout plant life cycle. Although ARFs’ structure and function have been well studied in Arabidopsis, such knowledge is far from being sufficient for cereal crops, especially wheat, rice and maize. This review is based on a comprehensive retrospection into the studies on ARFs in the three cereal crops, consisting of four parts: (1) characterization of the domains of 23, 25 and 33 ARF family members in wheat, rice and maize, respectively; (2) revision of nomenclatures for previously reported ARFs to the family numbers based on sequence alignment, and summary of ARFs’ functions including the regulation of agronomic traits and response to biotic/abiotic stresses; (3) highlight of general regulatory models for fundamental physiological and reproductive traits from miRNA-ARFs, IAA-ARF-LBD, IAA-ARF-Auxin response gene, and IAA-ARF-ERF (4) prospects to promising future ARF research for anticipated agronomic traits. Hopefully, the review will facilitate the understanding of ARF functions in the three cereal crops and promote its application in molecular breeding for achieving optimal plant architecture, higher yield, and wide adaptability.

Soybean response to high‐speed planting in Mississippi

AbstractMississippi soybean [Glycine max (L.) Mer.] producers are under pressure to plant as much land as possible within narrow planting windows. The 5‐year average planting progress is 45% of the total at the end of the optimal soybean planting window. New metering and seed delivery technology claims faster planting without sacrificing singulation, stand, or yield, but these tools need to be validated before recommendation. This study aimed to quantify soybean response to planting speeds using a precision planter (John Deere MaxEmerge 2 row units retrofitted with Ag Leader SureSpeed and SureForce) and a mechanical planter (John Deere 1700 ground‐driven mechanical planter equipped with eSet meters) for a total of 7 site‐years across Mississippi. In 2022, both planters were evaluated at four actual ground speeds of 7.9, 10.8, 13.5, and 16.4 km h−1 in a 2 × 4 factorial design. The experimental design was modified in 2023, where the mechanical planter served as the current farmer practice check at 9.7 km h−1and the precision planter speeds were 9.7, 14.5, and 17.7 km h−1 at research station sites and 9.7 and 14.5 km h−1 at an on‐farm site. Across sites, increased planting speed generally increased plant spacing, in‐row spacing variability, and decreased plant population. However, increased speed did not affect soybean yield. The precision planter at 17.7 km h−1 was no different from the mechanical planter in terms of soybean plant population, spacing, and yield in 2023. Results suggest soybean producers can plant soybean at 17.7 km h−1 without compromising yield.

Assessing the influence of planting time and fertilization on growth, flowering, yield and soil properties of chrysanthemum

ObjectivesTo identify optimal planting time for maximizing growth and flowering, and assess the impacts of various fertilization techniques on plant growth and soil health in chrysanthemum. MethodsThe investigation was conducted at Dr. Y.S. Parmar University of Horticulture and Forestry in Nauni, Solan, Himachal Pradesh, India from 2022 to 2023. The study aimed to investigate the impact of various planting times and fertilization schedules on the yield, quality, and soil health characteristics of chrysanthemum. Different planting times from 15 June to 30 August under organic and inorganic fertilization regimes were evaluated. Data related to vegetative growth, flowering, soil chemical and biological properties were recorded. ResultsPlantingon15 June along with inorganic fertilization resulted in improved vegetative characters like plant height (74.37 cm) and plant spread (30.56 cm), flowering characters like cut flower stem length (64.07 cm), stem strength (10.40°), flower diameter (11.80 cm) and duration of flowering (22.50 days) and soil chemical properties like available N, P, K (326.53, 40.36 and 359.48 kg/ha, respectively). However, planting on June 15 combined with organic fertilization led to enhancement in soil microbiological properties, including bacterial count (138.33 cfu/g soil), fungal count (31.75 cfu/g soil), actinomycetes count (62.46 cfu/g soil), microbial biomass (52.62 µg/g soil) as well as vase life (19.70 days). ConclusionIn the present study, it was found that planting time and fertilization significantly impacted chrysanthemum growth, yield and soil properties. The 15 June planting with inorganic fertilization boosted growth, yield and macronutrient content, while organic fertilization on the same date enhanced soil microflora. Flowering was influenced by planting time and organic fertilization showed promise as an alternative to chemical fertilization. Optimizing planting schedules and using organic fertilizers would lead to sustainability and offer economically viable alternatives to conventional crop management.

Identifying the key meteorological factors to marketable tuber rate of potato: A 5-year field experiment in North China

ContextPotato is one of the staple food crops in China. Optimized agronomic management could enhance potato yield, while the economic benefits of farmers depend on both the yield and marketable tuber rate (MTR) of potato. The key meteorological factors to the variation of MTR of potato and the relationship between potato yield and MTR remain unclear. ObjectiveThe study aims to explore the key meteorological factors to the variation of MTR of potato and the relationship between potato yield and MTR. MethodsA 5-year (20172021) rainfed field experiment with 3-planting date and 3-cultivar maturity of potato was conducted to investigate the optimum combination of planting date and cultivar maturity for both high yield and MTR under different year types and the relationship between potato yield and MTR. The meteorological factors during three growth stages including planting to 10 d before the tuberization, the tuber formation period (10 d before the tuberization to 10 d after the tuber bulking), 10 d after the tuber bulking to maturity were used to identify the key meteorological factors to yield and MTR of potato. ResultsThroughout the experimental period, there were large variations in the fresh tuber yield and MTR of potato ranging between 4.827 t ha−1 and 061 %, respectively. Early- to middle planting of middle maturing cultivars under warm-wet years resulted in higher MTR, yield, and economic benefit compared to other combinations. Under warm-dry years, while early planting of early- to middle-maturing cultivars could achieve higher yield and MTR, farmers still suffer economic losses under most combinations of planting date and cultivar maturity. On the whole, there was a synergistical enhancement between yield and MTR of potato (R2=0.86, P<0.01). The precipitation and growing degree day (GDD) during tuber formation period (TF), precipitation before TF, and the interaction of precipitation and mean air temperature after TF could account for 77 % of the variations in total tuber yield while marketable tuber rate could be determined by the precipitation from planting to 10 d after the tuber bulking, the HDD during TF, and the interaction of precipitation and mean air temperature before TF (R2=0.79). Conclusions:Total tuber yield and MTR of potato could be synergistically enhanced by optimizing planting date and cultivar maturity. Compared with total tuber yield, MTR influences economic benefit of potato more significantly. Implications:These findings could serve as a valuable reference for determining the optimal planting date and cultivar maturity of potato under different hydrothermal year types to enhance the MTR, yield, and economic benefit of rainfed potato planting and provide an important implication on potato model improvement.

Greenhouse farming: Hydroponic vertical farming- Internet of Things (IOT) Technologies: An updated review

This review paper of literature highlights the importance of greenhouse urban farming technology, hydroponics, aeroponics, aquaponics, vertical farming and applications of Internet of Things (IOT) technologies. The greenhouse farming is a well known modern agriculture technology for optimal plant growth. Hydroponics, a soilless cultivation technique using nutrient solutions under controlled conditions, is used for growing vegetables, high-value crops, and flowers. Vertical farming is a popular trend in hydroponics that involves stacking multiple layers of plants in a vertical arrangement. This farming method saves space, reduces water usage, and increases yields per square foot of growing area. In comparison to conventional greenhouse cultivation, hydroponics requires less fertilizer, pesticides, and water due to the precise control over their distribution. The term “Internet of Things (IOT)” is a system of interconnected computing devices, sensors, objects, microcontrollers, and cloud servers that can transmit data across a network and control other devices remotely without human intervention. A better management of nutrient solution in hydroponic systems requires optimum pH, electrical conductivity (EC), or ions concentration. The three main greenhouse farming gases (GHGs) emissions in hydroponics hi-tech urban farming are nitrous oxide, methane and carbon dioxide (CO2). To measure the levels of CO2 in hydroponics, the more intermediate to advanced grower can use a CO2 monitoring and controllers system. Hydroponic vertical farming is in infancy in India. Although hydroponic vertical farming units for production of crops like strawberry, lettuce and other leafy vegetables, foliage and flowers are functioning in major metros of India. However, the organized hydroponic vertical farms for production of food crops are not available in India. Therefore, only a few successful hydroponic vertical farms have been built in India mainly due to the initial high price tag on construction and the cost of maintaining them afterwards. Another disadvantage is carbon footprint of hydroponic vertical farming is very high, and discourages its applications.

Game analysis of future rice yield changes in China based on explainable machine-learning and planting date optimization

ContextGlobal warming's escalating severity necessitates sophisticated approaches for predicting rice yield. Research QuestionCombining crop models with data-driven techniques, such as machine learning, can more effectively grasp the complex interplay of variables influencing crop growth. It remains a significant challenge to balance accuracy and interpretability in such hybrid models. MethodsThe research integrated the Decision Support System for Agrotechnology Transfer (DSSAT) with statistical and machine learning models respectively, to assess rice yield changes in China under four future Shared Socio-economic Pathway (SSP). SSPs are scenarios that integrate socioeconomic trends with greenhouse gas emissions and radiative forcing pathways, which affect the phenology and yield of rice. The Shapley Additive Explanation (SHAP) method was employed to interpret the model, effectively determining the interplay among variables influenced rice yields. Mitigated the negative impacts of climate change on rice yield through the planting date optimization. ResultsProjections indicate significant rice yield losses in China without CO2, worsening with increased radiative forcing (p < 0.001). Considering rising CO2, single-season rice yields are projected to increase by 0.1–3.6 %, early rice by 4.6–9.5 %, while late rice yields are still decrease by 2.3–8.8 %. The rising CO2 can offset yield losses for single and early rice but not for late rice. The hybrid approach which combined the Random Forest (RF) with the DSSAT performed best in predicting rice yield. Studies showed that rising temperatures caused rice yield losses in China, yet we found that Growing Degree Days (GDD) exerted a more negative impact (p < 0.001). In high-precipitation regions, deep soil moisture is more influential than shallow soil moisture, whereas the reverse was true in drier areas (p < 0.001). Advancing planting dates for early and single rice and delaying for late rice can increase yields (p < 0.001). Adjusting to optimal planting dates, single-season rice yields increased by 3.3–6.3 %, early rice increased by 9.7–18.3 %, while late rice still decreased by 1.0–4.7 %. ConclusionsWithout considering the impact of CO2, significant rice yield losses in China are projected. Even with the fertilization effect of CO2, rice yields remain negatively impacted by climate change. However, implementing appropriate measures, such as optimizing planting dates, can help Chinese rice production benefit under changing climate. ImplicationsThis study offers insights into balancing accuracy and interpretability in hybrid models and provides guidance for local policymakers to address future climate change.

Impact of Smart Greenhouse Using IoT for Enhanced Quality of Plant Growth

Greenhouses play a crucial role in manipulating environmental conditions for optimal plant growth. While existing greenhouses enhance control over environmental factors, manual controls such as watering and humidity regulation often lead to suboptimal production and increased costs. This study proposes the development of a smart greenhouse with an automatic control system using fuzzy logic, specifically fuzzy Sugeno, to regulate watering and lighting based on soil moisture, temperature, and light intensity. The system's architecture involves sensor inputs, microcontroller processing, and the activation of actuators, such as UV lights and water pumps. Fuzzy logic is applied to interpret soil moisture and temperature inputs and determine optimal irrigation durations. The system's functionality is tested and validated through functional testing, Blynk application testing, and fuzzy Sugeno testing. Results indicate the successful implementation of the proposed smart greenhouse system. Functional testing demonstrates accurate sensor readings, including temperature and soil moisture. The Blynk application enables real-time monitoring and control of environmental conditions. Fuzzy Sugeno testing validates the irrigation control system, with an average error rate of 1.3%, affirming the system's alignment with desired specifications. Plant testing in different conditions showcases the effectiveness of the smart greenhouse in supporting plant growth and development.

Can green firebreaks help balance biodiversity, carbon storage and wildfire risk?

Green firebreaks (strategically placed plantings of low-flammability vegetation) are designed to reduce the rate of fire spread and thereby increase the suppressibility of fires. Successful examples have led to some fire-prone regions investing heavily in the establishment of green firebreaks as a method of reducing fire risk while improving biodiversity and carbon storage. However, beyond small-scale case studies there has been little research quantitatively exploring the interactions among biodiversity, carbon, and wildfire risk in relation to green firebreaks. Here, we combine a Bayesian Network (BN) analysis, and fire simulations in PHOENIX RapidFire (hereafter Phoenix), to identify planting designs that reduce wildfire risks while also providing positive biodiversity and carbon outcomes. Using a BN analysis, we prioritised optimal planting designs as the combination of elements (e.g., stem density, distance from houses, shrub design, age etc.) that delivered the greatest increase in biodiversity and carbon while reducing fire risk to people and property for eight sites across south-eastern Australia. We ranked combinations of planting designs, prioritising house, and life loss first, to identify optimal designs. Optimal planting designs varied among sites, although the design elements that best reduced risk to houses and lives were consistent. These elements included ‘scattered’ shrubs and planting densities of trees consistent with an open forest structure. Estimated fuel loads for the optimal planting design at each site were used to create a simulated revegetation area in Phoenix. We simulated fire behaviour in Phoenix across a grid of ∼1000 ignitions for each site, and for up to 54 historic weather conditions for a 'current fuel’ scenario (no green firebreaks present) compared with a ‘green firebreak fuel’ scenario. We found that the establishment of a green firebreak did not result in significant changes to fire behaviour at most sites. In some cases, it reduced risk to people and property, and where fire behaviour did change in terms of intensity, frequency, ember attack and overall risk, the differences relative to the current fuel scenario were less than two percent. Overall, simulated green firebreaks in most cases were found to provide biodiversity and carbon benefits without increasing fire risk. These findings illustrate their potential as an effective nature-based solution for managing multiple priorities; however, further testing of real plantings is required to evaluate this potential as an at-scale solution.

Effects of vetiver root on cracking of expansive soils and its mechanistic analysis

The study investigated the reinforcing effect of vetiver root on soil by conducting outdoor planting tests and indoor root tests. The cracking indexes of soil specimens with varying root contents were analyzed, and a statistical model was established to determine the relationship between the cracking indexes, the number of dry and wet cycles, and the root content. The study revealed the crack evolution law of vetiver-reinforced expansive soil. The study explored the mechanism of the vegetation root in inhibiting the cracking of expansive soil and determined the optimal planting density of vetiver grass through outdoor planting tests. The results indicate that: The surface crack rate (CR), total crack length (CL), and crack number (CN) in the root-soil specimen exhibited exponential growth with an increase in the number of wet and dry cycles. This growth was more pronounced during the first and second cycles. The vetiver root could effectively reduce soil crack formation, and the specimen's cracking resistance is positively correlated with the root content. With the root content increased, the CR, CN, and CL decreased. The logistic model is suited to the CL of added root soil. The logistic model is more suitable for the growth model of the CR of the expansive soil with low root content, while the Boltzmann model is more suitable for the growth model of the CR of the expansive soil with high root content. Width of crack (CW) is better suited to the DoseResp growth model. The Boltzmann model is more applicable to the CN in expansive soils with low reinforcement, while the logistic growth model is more suitable for the development of CN above 0.21% root content. The development of the crack network was influenced by two key factors: the root content and the number of wet and dry cycles. Under the condition of planting roots, the development of crack networks in expansive soil differs from that of expansive soil with added roots, and there is no clear pattern to follow. The inhibitory effect of the vetiver root on cracking of expansive soil is related to the planting density of vetiver.

Balancing Yield and Environmental Impact: Nitrogen Management and Planting Density for Rice in Southwest China

With growing concerns about global warming, it is crucial to adopt agronomic practices that enhance rice yields from paddy fields while reducing greenhouse gas (GHG) emissions for sustainable agriculture. An optimal nitrogen (N) fertilization rate and planting density are vital to ensure high rice yields, minimize GHG emissions, and understand emission behavior for better field management. We hypothesized that optimizing N application rates and planting density to improve nitrogen use efficiency (NUE) in rice cultivation would reduce resource losses and GHG emissions. To test this hypothesis, we implemented five treatments with a rice straw return cultural system: two planting densities (16 hills m−2 (traditional density, D1) and 20 hills m−2 (25% higher density, D2)) and three N application rates (no N fertilizer (N0), 180 kg N ha−1 (N1), and 144 kg N ha−1 (N2)). The control treatment (CK) was traditional planting density with no N fertilizer. The four new cropping modes were N1D1, N1D2, N2D1, and N2D2. We investigated the effects of N application rates and planting density on rice grain yield, NUE, and GHG emissions in multiple rice-growing seasons. The N1D2 treatment exhibited the highest grain yield over the three years, with a value of 10,452 kg ha−1, representing an increase of 12.2% compared to CK. Moreover, N uptake in N1D2 was the highest, averaging 39.2% (p &lt; 0.05) higher than CK, and 8.5%, 3.5%, and 2.8% (p &lt; 0.05) higher than N1D1, N2D1 and N2D2, respectively. N2D2 exhibited the highest NUE, with a value of 58.99 kg kg−1, surpassing all other treatments over the three years. GHG emissions, global warming potential (GWP), and greenhouse gas intensity (GHGI) in N2D2 were lower than in N1D1, N1D2, and N2D1. Additionally, reducing N application (comparing N1D1 to N2D1) and increasing plant density (comparing N1D1 to N1D2) improved N agronomic efficiency (NAE) and N partial productivity (PFPN). The negative correlation between the NAE and PFPN with GWP and GHG emissions further supports the potential for optimized N management and denser planting density to reduce environmental impact. These findings have important implications for sustainable rice cultivation practices in Southwest China and similar agroecosystems, emphasizing the need for integrated nutrient management strategies to achieve food security and climate change mitigation goals.

Effects of Additives and Planting Density on Silage Performance and Bacterial Community of Novel Sorghum bicolor × S. propinquum Hybrids

In the enhancement of Novel Sorghum bicolor × S. propinquum Hybrid utilization, optimal planting densities and silage methods remain elusive. This study assesses the effects of planting densities, cellulase (CE), Lactobacillus buchneri (LAB), and their combination (LC) on fermentation quality and bacterial diversity of the hybrid silage. The experiment was carried out in a completely random block design with four additives and five planting densities (M1, M2, M3, M4, M5) as follows (4 additives × 5 planting densities): a control group without additives (CK), a group treated with Lactobacillus buchneri (LAB), a group with cellulase (CE), and a group treated with a combination of LAB and CE (LC), maintaining triplicates per treatment. In this study, the additive treatment improved the fermentation quality of silage compared with the control. In the M2-LC group, the contents of crude protein (CP; 7.88%), ether extract (EE; 1.91%), and ash (7.76%) were the highest, while the pH (3.30) was the lowest. The water-soluble carbohydrate (WSC; 11.28%) content was the highest in the M3-CE group, the lactic acid (LA; 6.79%) content was the highest in the M4-CE group, and the acetic acid (AA; 7.71%) content was the highest in the M2-LAB group. Meanwhile, the neutral washing fiber (NDF; 53.17%) content was the lowest in the M5-CE group, the acid detergent fiber (ADF; 41.01%) content was the lowest in the M2-CE group, and the propionic acid (PA; 0.26%) content was the lowest in the M1-LAB group. Adding LC notably reduced bacterial diversity, boosted Lentilactobacillus, and curbed Proteobacteria. LAB and LC markedly improved amino acid metabolism over CE and CK. Conversely, beta-lactam resistance, flagellar assembly, and ascorbate/aldarate metabolism pathways were suppressed. In the future, we will explore a variety of additives and adjust the cutting height to improve its comprehensive quality, create an innovative path for silage production, promote the efficient use of agricultural resources, and provide high-quality feed for animal husbandry.

Molecular characterization and validation of zinc solubilization potential of bacteria isolated from onion (Allium cepa L.) rhizosphere

Zinc (Zn) is essential for optimal plant growth and nutrition, while zinc-solubilizing bacteria (ZSB) enhance plant accessibility to zinc by converting its insoluble forms into usable ones. The primary objective of this research was to isolate and identify Zn-solubilizing strains from the onion rhizosphere and evaluate their ability to solubilize different insoluble Zn compounds, including ZnO, ZnCO3, and Zn3(PO4)2. Of the nineteen bacterial isolates retrieved, fifteen demonstrated the ability to solubilize inorganic Zn compounds based on plate assay techniques. The Zn-solubilizing bacterial strains, selected for their qualitative properties, underwent quantitative testing in broth culture using atomic absorption spectrophotometry (AAS) and Field Emission-Scanning Electron Microscopy with Energy Dispersive X-ray Spectroscopy (FE-SEM-EDS). Seven bacterial isolates with a high capacity to solubilize Zn were identified based on 16S rRNA gene amplification and sequence analysis. These isolates were affiliated with Pantoea eucrina, Pantoea dispersa, Pseudomonas aeruginosa, Bacillus velezensis, and Pseudomonas fluorescens. Notably, this study is the first to report the potential of Pantoea eucrina as a ZSB. The strain Pantoea eucrina ZSC9 exhibited the maximum Zn solubilization index (8.85) and the highest soluble Zn content (624 mg L−1) among the three insoluble Zn compounds, with ZnO being the most effectively solubilized on the 10th day of incubation in ZnO-enriched basal medium. All bacterial isolates were more effective at solubilizing ZnO than ZnCO3 and Zn3(PO4)2, and this solubilization led to a significant drop in the pH of the broth. Pantoea eucrina ZSC9 showed the maximum pH reduction (3.82) in a ZnO-supplemented medium, indicating a negative correlation between broth pH and Zn solubilization. Seven selected ZSB strains were assessed for their potential to enhance onion growth under pot house conditions. The results showed that inoculating onion seedlings with these ZSB strains significantly improved their growth. Among the strains tested, ZSC9, ZSC10, and ZSC17 were the most effective, enhancing growth attributes such as plant height, bulb diameter, fresh and dry weight of bulbs, Zn uptake by the plant, and overall bulb yield compared to other strains. These findings suggest that these ZSB isolates have great potential as biofertilizers to enhance plant growth and development.

Nutrient Dynamics and Resource-Use Efficiency in Greenhouse Strawberries: Effects of Control Variables in Closed-Loop Hydroponics

The importance of implementing recirculating drainage for greenhouse strawberries is often overlooked because of the low electrical conductivity (EC) of drainage and transpiration despite the large area of cultivation in Korea. In this study, we analyzed the growth of strawberry crops and their water and individual nutrient use efficiency when using closed-loop hydroponics in greenhouses. The study consisted of two parts: Experiment (Exp) 1 and Exp 2, each of which employed a different closed-loop hydroponic control method. In Exp 1, the system was controlled solely based on the EC of the drainage mixed with raw water. In Exp 2, the nutrient solution (NS) was corrected according to the ion concentrations in the drainage, with correction intervals of 2 weeks, 4 weeks, and no correction, while the control treatment was open-loop. In Exp 1, a prolonged increase in drainage EC resulted in an imbalance in NS ion composition and reduced fruit yield. In Exp 2, the NS composition was stabilized through periodic nutrient correction, and the fruit yield of the closed-loop treatments did not differ from the open-loop treatment. However, the closed-loop with 2- and 4-week correction in Exp 2 showed 94% and 88% higher nutrient use efficiency (NUE), respectively, than an open-loop system. Among the closed-loop treatments, the 2- and 4-week correction intervals had 36% and 32% higher NUE than the no-correction treatment. Furthermore, the 2-week correction interval showed a 3% improvement in NUE compared to the 4-week interval. These findings highlight the importance of considering a shorter ion correction interval for optimal NUE and normal plant growth in recirculating hydroponic systems of strawberry cultivation.

A historical assessment of the number of days required to plant the Arkansas soybean crop

AbstractThe primary objective of this paper is to quantitatively analyze the number of acres planted per suitable fieldwork day and the total number of fieldwork days available for the complete planting of soybeans (Glycine max L.) in Arkansas. Total days to plant soybeans in each spring from 1980 to 2023 averaged about 33 days, indicating that roughly 33 days are expected, on average, from the month of April to May based on USDA‐NASS weekly crop progress and condition data. Furthermore, the average soybean planted acres is estimated at 106,736 acres per day within the study period. Recently, variations in this range appear more pronounced, likely due to the significant impact of extreme spring weather conditions. However, technological advancements have enabled soybean producers to plant crops as early as the first half of March or as late as June, contributing to the increased variability observed from year to year in recent times. Furthermore, Arkansas's optimal soybean planting window typically provides an average of 4.7 fieldwork days per week. These quantified estimates offer historical data on Arkansas's soybean planting performance and will prove highly valuable for informing future soybean planting strategies.

Development of Smart Hydroponics System using AI-based Sensing

This paper proposes a smart hydroponic system that operates automatically using a fuzzy logic algorithm, integrating IoT functionalities to support smart agriculture. The system allows for remote monitoring and control via the internet, providing real-time data on water levels, pH levels, temperature, and nutrient solution temperature. Precise dosing and temperature control are critical for optimal plant growth, and the system schedules temperature measurements to ensure stability. Unstable temperature can affect pH levels, thereby impacting nutrient absorption. The proposed system employs sensors to continuously monitor the electrical conductivity (EC) and pH levels of the nutrient solution. Fuzzy control is utilized to regulate the nutrient solution pump, automatically adjusting EC and pH levels to promote optimal plant growth. This approach reduces the time burden on producers and provides more precise control over the nutrient solution, resulting in improved growth outcomes. The main contributions of this work are as follows: the development and implementation of an AI-based system integrating a controller, IoT environment, fuzzy logic algorithm, and NFT (nutrient film technology) hydroponics; the creation of a user-friendly interface for farmers through the Smart-Hydroponic application, enabling hybrid monitoring and control of hydroponic farms; the establishment of an IoT-based cloud environment for sensor data monitoring; the implementation of a smart hydroponic system for nutrient sensing, monitoring, and control; and a comparative analysis between smart and conventional hydroponics based on morphological results.

A Comparative Study of Spacing and Integrated Nutrient Management on Growth, Yield and Economics of Okra

The present investigation was conducted at Horticultural Experimental Farm, B.A.C.A., Anand Agricultural University, Anand in kharif during 2022 and 2023. Ensuring balanced plant nutrients and spacing is essential for achieving optimal okra production. Optimal plant density supports uniform and healthy growth by effectively utilizing moisture, nutrients, and light, thereby maximizing okra yield. Single source of nutrients whether chemical fertilizers, organic manures or biofertilizers that cannot improve production, productivity and soil health. Sowing okra at 60 × 30 cm spacing observed better leaf area (433.06 and 568.01 cm2) and number of nodes per plant (15.57 and 23.46) at 60 and 90 DAS. While, maximum yield per plot (9.20 kg) and yield per hectare (14.19 t) was found in 45 × 30 cm spacing. Application of 25% RDF + 50% RDN through Vermicompost + NPK consortium 1L/ha recorded maximum yield per plot (9.41 kg) and yield per hectare (15.33 t). With respect to treatment combinations, sowing of okra at 60 × 30 cm spacing with application of 25% RDF + 50% RDN through Vermicompost + NPK consortium 1L/ha recorded the highest leaf area (458.88 and 598.27 cm2) at 60 and 90 DAS. However, maximum yield per plot (10.45 kg) and yield per hectare (16.13 t) was found in 45 × 30 cm spacing with application of 25% RDF + 50% RDN through Vermicompost + NPK consortium 1L/ha. On economic point of view, maximum net realization (218555.21 ₹/ha) and BCR (2.11) was obtained in 45 × 30 cm spacing with application of 25% RDF + 50% RDN through FYM + NPK consortium 1L/ha.

Strigolactone-gibberellin crosstalk mediated by a distant silencer fine-tunes plant height in upland cotton

Optimal plant height is crucial in modern agriculture, influencing lodging resistance and facilitating mechanized crop production. Upland cotton (Gossypium hirsutum) is the most important fiber crop globally; however, the genetic basis underlying plant height remains largely unexplored. In this study, we conducted a genome-wide association study to identify a major locus controlling plant height (PH1) in upland cotton. This locus encodes gibberellin 2-oxidase 1A (GhPH1) and features a 1133-bp structural variation (PAVPH1) located approximately 16 kb upstream. The presence or absence of PAVPH1 influences the expression of GhPH1, thereby affecting plant height. Further analysis revealed that a gibberellin-regulating transcription factor (GhGARF) recognizes and binds to a specific CATTTG motif in both the GhPH1 promoter and PAVPH1. This interaction downregulates GhPH1, indicating that PAVPH1 functions as a distant upstream silencer. Intriguingly, we found that DWARF53 (D53), a key repressor of the strigolactone (SL) signaling pathway, directly interacts with GhGARF to inhibit its binding to targets. Moreover, we identified a previously unrecognized gibberellin-SL crosstalk mechanism mediated by the GhD53-GhGARF-GhPH1/PAVPH1 module, which is crucial for regulating plant height in upland cotton. These findings shed light on the genetic basis and gene interaction network underlying plant height, providing valuable insights for the development of semi-dwarf cotton varieties through precise modulation of GhPH1 expression.

Enhancing Seed Yield of Daincha (Sesbania aculeata L.) New Variety TRY1 Through Optimum Spacing and Nutrient Management

Background: Increasing demand for leguminous green manure crop daincha seed is being noticed throughout the country mainly to restore the soil fertility. For maximizing seed production of newly released daincha variety, an optimum plant density per unit area and fertilizer application are an important management tool. The study was conducted to optimize the plant spacing and fertilizer dose for achieving higher seed yield of daincha variety TRY 1. Methods: Field experiments were conducted during Summer and Kharif 2023 to study the effect of different spacing and fertilizer doses on growth, yield and economics of new daincha variety TRY1. The treatments consisted of five different spacing (S1-75×30 cm, S2-60×30 cm, S3-75×45cm, S4-60×45 cm and S5-90×30 cm) in main plots and three fertilizer levels (F1-12.5:25:12.5 kg N, P2O5, K2O ha-1, F2-18.75:37.5:18.75 kg N, P2O5, K2O ha-1, F3-25:50:25 kg N, P2O5, K2O ha-1 and control) in sub plots were tested. The treatments were replicated thrice in split plot design. Result: Growing of daincha variety TRY 1 at 60×45 cm spacing produced significantly taller plants (223 cm), more number of branches per plant (15.9) and dry matter production (1766 kg ha-1), yield parameters viz., higher mean number of pods per plant (114), pod length (22.4 cm) and seeds per pod (36.7), seed yield (567 kg ha-1), mean net returns (Rs.24512 ha-1) and benefit cost ratio (2.36) than other spacing. Application of 25:50:25 kg N, P2O5, K2O ha-1 recorded significantly mean taller plants (217 cm), more number of branches per plant (19.3), dry matter production (1831 kg ha-1), nodules/plant (40.7), more number of pods /plant (121), pod length (23.7 cm) and seeds per pod (37.8), seed yield (572 kg ha-1), net returns (Rs.23369 ha-1) and benefit cost ratio (2.20) over other fertilizer doses. Thus, the optimum spacing of 60×45 cm and fertilizer dose of 25: 50:25 kg N, P2O5, K2O ha-1 could be recommended for getting higher growth, yield parameters, seed yield, net returns and benefit cost ratio of newly released daincha variety TRY 1.

Optimal Planting Time for Summer Tomatoes (Lycopersicon esculentum Mill.) Cropping in Korea: Growth, Yield, and Photosynthetic Efficiency in a Semi-Closed Greenhouse.

In Korea, greenhouses are traditionally used for crop cultivation in the winter. However, due to diverse consumer demands, climate change, and advancements in agricultural technology, more farms are aiming for year-round production. Nonetheless, summer cropping poses challenges such as high temperatures, humidity from the monsoon season, and low light conditions, which make it difficult to grow crops. Therefore, this study aimed to determine the best planting time for summer tomato cultivation in a Korean semi-closed greenhouse that can be both air-conditioned and heated. The experiment was conducted in the Advanced Digital Greenhouse, built by the National Institute of Agricultural Sciences. The tomato seedlings were planted in April, May, and June 2022. Growth parameters such as stem diameter, flowering position, stem growth rate, and leaf shape index were measured, and harvesting was carried out once or twice weekly per treatment from 65 days to 265 days after planting. The light use efficiency and yield per unit area at each planting time was measured. Tomatoes planted in April showed a maximum of 42.9% higher light use efficiency for fruit production and a maximum of 33.3% higher yield. Furthermore, the growth form of the crops was closest to the reproductive growth type. Therefore, among April, May, and June, April is considered the most suitable planting time for summer cultivation, which is expected to contribute to reducing labor costs due to decreased workload and increasing farm income through increased yields. Future research should explore optimizing greenhouse microclimates and developing crop varieties tailored for summer cultivation to further enhance productivity and sustainability in year-round agricultural practices.