Higher Crop Yields Research Articles

Влияние стимуляторов роста на продуктивность зерновой кукурузы

One of the most effective ways to increase grain yield per hectare is the introduction of corn for grain into the grain crop rotation, since corn is a high-yielding crop and a good precursor for spring breads, as well as a soil fertility restorer and a drought-resistant plant. Therefore, the introduction of new cultivation technologies and modern hybrids of grain corn is of great relevance at the present time. The purpose of our research was to optimize the use of growth stimulants in the cultivation of various hybrids of grain corn in the northern zone of the Krasnodar Territory. Field experiments were conducted in the production conditions of Zarya LLC in the Krylovsky district of the Krasnodar Territory in 2023 and 2024. In field experiments, three corn hybrids were used as research objects: KSS 5291, P 8834 and GS 370. In the field experiment, the scheme included options for studying the effect of pre-sowing treatment of seeds and leafy top dressing with growth stimulants on the growth, development and yield of corn. The largest root mass of grain corn was formed in the GS-370 hybrid on plots with a growth stimulant Energy M and amounted to 3,874 t/ha, which turned out to be 692 kg/ha, or 22% more than the minimum value. The maximum dry biomass in the experiment was observed in the GS-370 hybrid on plots using the growth stimulator Energy M and was 10.948 t/ha, that is, by 2.291 t/ha or 26% more than the minimum value. The maximum net photosynthesis productivity (BPF) of corn was formed in hybrid P 8834 on plots without the use of growth stimulants and was equal to 4,616 g/m2 day, that is, by 413 g/m2 day or 9.8% more than the minimum value. The highest economic yield was formed in the GS-370 hybrid, ranging from 7.08 t/ha on plots without the use of growth stimulants to 7.84 t/ha on variants using pre-sowing seed treatment and two leaf fertilizing with the Energia M stimulant.

Impact of Hybrid Varieties on the Productivity of Rice Cultivation: The Case of Sindh, Pakistan

Agriculture is a very important sector and crucial for poverty reduction and employment generation. The use of hybrid seeds leading to increased yield of crops and farmers’ income has been widely discussed in the literature. Through this study, an effort has been made to identify factors that impact obtaining high crop yield of rice by using the hybrid varieties. This study focuses on the use of hybrid varieties in the rice crop and presents a comparison in yield with the traditional open-pollinated varieties. The plot level information collected from eight major rice-producing districts of Sindh province of Pakistan was used to conduct this study and a propensity score matching technique was used to estimate the results of the study by using plot level variation between hybrid varieties and open-pollinated varieties. The results of the study showed significant improvement in rice yield with the adoption of hybrid rice varieties in the study area. This study highlights that the farmers have achieved a higher average yield of rice by using hybrid seeds as compared to open-pollinated varieties. Besides the crop yield, the overall income and profit of farmers have also increased by using hybrid seeds in the Sindh province. The study illustrates different interesting patterns relating to different characteristics of the household cultivating rice crops, different plot-level characteristics, and their comparison with hybrid and traditional varieties.

Long-Term Straw Returning Enhances Phosphorus Uptake by Zea mays L. through Mediating Microbial Biomass Phosphorus Turnover and Root Functional Traits.

The intensive use of chemical fertilizers in China to maintain high crop yields has led to significant environmental degradation and destabilized crop production. Returning straw to soil presents a potential alternative to reduce chemical fertilizer requirements and enhance soil fertility. This study investigates the effects of different nitrogen (N) input levels and straw additions on crop phosphorus (P) uptake and soil P availability based on a long-term N-fertilizer trial. The treatments included no fertilizer input (CK), conventional (NPK), reduced NPK (0.75NPK), and straw-amended (SNPK) treatments. Results indicate that SNPK significantly enhances shoot P uptake and crop yields by 43.7-61.9% and 29.3-39.6%, respectively. The SNPK treatment improved rhizosphere P availability and increased the phosphorus activation coefficient (PAC) by 1.72-fold compared to NPK alone. The enhanced soil P availability under SNPK was primarily attributed to an abundance of functional microbes, leading to higher P storage in the microbial biomass P pool and its turnover. Additionally, SNPK promoted root exudate and phosphate-mobilizing microbes, enhancing P mobilization and uptake. Nitrogen fertilization primarily influenced root functional traits related to P acquisition. These findings provide valuable insights for developing effective fertilizer management strategies in maize-oilseed rape rotation systems, emphasizing the benefits of integrating straw with chemical fertilizers.

Design and implementation of a portable snapshot multispectral imaging crop-growth sensor.

The timely and accurate acquisition of crop-growth information is a prerequisite for implementing intelligent crop-growth management, and portable multispectral imaging devices offer reliable tools for monitoring field-scale crop growth. To meet the demand for obtaining crop spectra information over a wide band range and to achieve the real-time interpretation of multiple growth characteristics, we developed a novel portable snapshot multispectral imaging crop-growth sensor (PSMICGS) based on the spectral sensing of crop growth. A wide-band co-optical path imaging system utilizing mosaic filter spectroscopy combined with dichroic mirror beam separation is designed to acquire crop spectra information over a wide band range and enhance the device's portability and integration. Additionally, a sensor information and crop growth monitoring model, coupled with a processor system based on an embedded control module, is developed to enable the real-time interpretation of the aboveground biomass (AGB) and leaf area index (LAI) of rice and wheat. Field experiments showed that the prediction models for rice AGB and LAI, constructed using the PSMICGS, had determination coefficients (R²) of 0.7 and root mean square error (RMSE) values of 1.611 t/ha and 1.051, respectively. For wheat, the AGB and LAI prediction models had R² values of 0.72 and 0.76, respectively, and RMSE values of 1.711 t/ha and 0.773, respectively. In summary, this research provides a foundational tool for monitoring field-scale crop growth, which is important for promoting high-quality and high-yield crops.

Perennial cultivation of green pepper provides pest suppression, ecosystem development and high crop yield

Perennial cultivation (PC) provides economic benefits to farmers and environmental benefits to society such as soil protection. However, little is known about the effects of PC on soil pest occurrence. This 4-year study used overwintered green pepper (GP) and root-knot nematodes (RKN) as a model plant–soil pest system. We compared the levels of RKN, its antagonists, and other organisms in strip-shaped PC microplots to those of annual cultivation (AC) and no-tillage AC (NT) microplots. To promote RKN occurrence, we inoculated the ends of all microplots with RKN in the spring of year 3 of study. During autumn in years 3 and 4, we determined abundance of RKN and antagonistic animals, DNA operational taxonomical units (OTUs) counts of antagonistic microbes. The results showed that the rate of RKN occurrence was lower in PC and NT than in AC due to minimal soil movement, rather than to suppression by antagonists, although some antagonists such as collembolan and nematode-trapping fungi increased in PC. Moreover, plant biomass was higher in PC than in NT and AC during fruiting period due to persistence of the plant body over years.We also found signs of ecosystem development in PC, which is an essential background for pest suppression: the maturity index (a measure of disturbance) of the nematode community, fungal diversity, frequency of plant and animal symbionts/parasites were greater in PC than in NT or AC. These findings suggested that ecosystem development had commenced within the 4-year study period. We conclude that the long-lasting plant body and undisturbed soil environment are specific to PC, and could provide agronomical and ecological benefits.

Identification and expression analysis of Phosphate Transporter 1 (PHT1) genes in the highly phosphorus-use-efficient Hakea prostrata (Proteaceae).

Heavy and costly use of phosphorus (P) fertiliser is often needed to achieve high crop yields, but only a small amount of applied P fertiliser is available to most crop plants. Hakea prostrata (Proteaceae) is endemic to the P-impoverished landscape of southwest Australia and has several P-saving traits. We identified 16 members of the Phosphate Transporter 1 (PHT1) gene family (HpPHT1;1-HpPHT1;12d) in a long-read genome assembly of H. prostrata. Based on phylogenetics, sequence structureand expression patterns, we classified HpPHT1;1 as potentially involved in Pi uptake from soil and HpPHT1;8 and HpPHT1;9 as potentially involved in Pi uptake and root-to-shoot translocation. Three genes, HpPHT1;4, HpPHT1;6and HpPHT1;8, lacked regulatory PHR1-binding sites (P1BS) in the promoter regions. Available expression data for HpPHT1;6 and HpPHT1;8 indicated they are not responsive to changes in P supply, potentially contributing to the high P sensitivity of H. prostrata. We also discovered a Proteaceae-specific clade of closely-spaced PHT1 genes that lacked conserved genetic architecture among genera, indicating an evolutionary hot spot within the genome. Overall, the genome assembly of H. prostrata provides a much-needed foundation for understanding the genetic mechanisms of novel adaptations to low P soils in southwest Australian plants.

(Invited) Carbon Nanodots for Improved Plant Genetic Engineering

Anthropogenic climate change has made the need for more nutritious and stress-tolerant plants indisputable for providing food security. Climate change has decreased the global agricultural productivity by 21% since 1961, and will continue to drop crop yields by 7.4% for every degree-Celsius increase in temperature. CRISPR genetic engineering can enable previously inaccessible crop tolerances to meet the demand for nutritionally-balanced and high-yielding crops. However, plant biomolecule delivery and transformation are two important bottlenecks that prevent plant genetic engineering from reaching its full potential.Nanomaterials have already shown promise to deliver genetic cargoes into plant somatic cells for transient expression of proteins1. For most applications in agriculture, however, biomolecules need to be delivered to cells that can give rise to next generation of edited plants, such as germ or stem cells. These cell types are extremely challenging to penetrate and deliver cargoes using traditional approaches, hence forcing researchers to use undifferentiated tissues to transform and go through an arduous regeneration process in tissue culture. This time consuming, inefficient, and highly species dependent approach has been a critical obstacle in plant biology and bioengineering.We have synthesized small (~3 nm) and positively charged (+35 mV) carbon nanodots (CNDs) from glycerol and polyethyleneimine (PEI) using the microwave method. These CNDs showed high entry efficiency into plant stem cells and ability to deliver DNA cargoes into a model plant Nicotiana benthamiana without affecting stem cell viability and differentiation capability. This is a first-time demonstration of nanoparticle uptake by plant stem cells and cargo expression in these cell types. Once we perform CRISPR gene editing in these stem cells, the edited germ cells and seeds will develop automatically from the stem cells, facilitating the creation of edited and improved new plant varieties.

Changes in phosphorus-related performance attributes of dryland winter wheat cultivars released between the 1940s and 2010s in Shaanxi Province, China.

Water and nutrients are two main determinants of wheat yield, which are vital for maintaining high crop yields. In the present study, the effects of water and phosphate fertilization on wheat yield, photosynthetic parameters, water productivity and phosphate use efficiency were investigated. Five dryland wheat cultivars from the 1940s to the 2010s that are widely cultivated in Shaanxi Province, China, were used. Experiments were conducted from 2019 to 2022 using two irrigation levels (normal rainfall and no precipitation after the reviving stage) and two phosphorus application levels (0 and 100 kg ha-1). Compared with old cultivars ('Mazha'), the grain yield of modern cultivars ('Changhan 58') was 89.24% higher and was closely correlated with chlorophyll index, leaf area index, photosynthetic rate and tillers. With the replacement of cultivars, the phosphorus content, water potential and phosphatase activity of wheat leaves increased. Considering water-phosphorus interactions, the water use efficiency and phosphorus use efficiency of wheat showed a significant positive correlation. Our findings indicate that modern wheat cultivars are more responsive to phosphorus. Further analysis revealed that modern varieties have evolved two phosphorus absorption strategies in response to phosphorus deficiency - namely, the formation of a phosphorus supply source, which may result in larger numbers of green organs; and an increase in phosphorus sinks, which tended to activation and transport of plant phosphorus. Our results may thus contribute to water conservation, increased yields and the development of strategies for efficient phosphorus fertilization. © 2024 Society of Chemical Industry.

AI-powered revolution in plant sciences: advancements, applications, and challenges for sustainable agriculture and food security

Artificial intelligence (AI) is revolutionizing plant sciences by enabling precise plant species identification, early disease diagnosis, crop yield prediction, and precision agriculture optimization. AI uses machine learning and image recognition to aid ecological research and biodiversity conservation. It plays a crucial role in plant breeding by accelerating the development of resilient, high-yielding crops with desirable traits. AI models using climate and soil data contribute to sustainable agriculture and food security. In plant phenotyping, AI automates the measurement and analysis of plant characteristics, enhancing our understanding of plant growth. Ongoing research aims to improve AI models’ robustness and interpretability while addressing data privacy and algorithmic biases. Interdisciplinary collaboration is essential to fully harness AI’s potential in plant sciences for a sustainable, food-secure future.

Effect of integrated vermicompost and chemical nitrogen fertilizer for productivity of bread wheat (Triticum aestivum L.)

The study was conducted at Horo district on six (6) farmers’ fields to investigate the effect of integrated vermicompost and chemical nitrogen fertilizer for bread wheat and the soil nutrient content. The treatments consist of without any fertilizer, recommended chemical NP fertilizer, 100% N equivalent vermicompost + recommended P2O5, 75% N equivalent Vermicompost + 25% recommended N + recommended P2O5, 50% equivalent Vermicompost + 50% recommended N + recommended P2O5 and 25% equivalent Vermicompost + 75% recommended N + recommended P2O5. The experimental design was a randomized complete block design (RCBD) with three replications. Dendea seed variety with a rate of 150 Kg ha-1 was used for the trial. ANOVA analysis for after harvesting soil parameters showed significant (P<0.05) differences among treatments for available phosphorus, organic carbon, exchangeable acidity and cations exchange capacity while pH and exchangeable Ca and Mg were not statistically significant among the treatments. ANOVA showed that the treatments significantly influenced all collected yield and yield related parameters of the crop. Relatively the highest crop yield and yield related traits were obtained from T2 (recommended chemical fertilizer) while the lowest was from control (without fertilizer). The highest net income (91971.70 birr/ha) was obtained from 25% equivalence vermicompost + 75% recommended N + recommended P2O5. To have more confidence in the technology; it should be verified in the same area with the same management procedure.

How Can Soil Quality Be Accurately and Quickly Studied? A Review

Evaluating soil quality is crucial for ensuring the sustainable use of agricultural lands. This review examines the definition, evaluation methods, indicator selection, and relevant case studies. The concept of soil quality supplements soil science research by deepening our understanding of soils and aiding in the allocation of resources as agriculture intensifies to meet rising global demand. Soil quality provides a framework for educating stakeholders about the essential functions of soils and offers a tool for assessing and comparing different management techniques. Regular evaluation of soil quality is vital for maintaining high crop yields and addressing the gap between production and consumption. Nowadays, many researchers have explored machine learning (ML) and deep learning (DL) techniques and various algorithms to model and predict soil quality with satisfactory results. These chosen indicators can be influenced by chemical, biological, or physical features. This paper compares ML and DL with traditional methods, examining their features, limitations, different categories of machine learning, and their applications in soil quality assessment. Finally, we show that predicting soil quality has the potential to be extremely accurate and efficient with ML and DL. This distinguishes the application of DL and ML from other approaches since they can anticipate the soil quality index without the need for more intricate computations. Our suggestion for future studies is to evaluate soil quality over broader regions and predict it by using more accurate, modern, and faster methods, using a variety of activation functions and algorithms.

Rewiring gene circuitry for plant improvement.

Aspirations for high crop growth and yield, nutritional quality and bioproduction of materials are challenged by climate change and limited adoption of new technologies. Here, we review recent advances in approaches to profile and model gene regulatory activity over developmental and response time in specific cells, which have revealed the basis of variation in plant phenotypes: both redeployment of key regulators to new contexts and their repurposing to control different slates of genes. New synthetic biology tools allow tunable, spatiotemporal regulation of transgenes, while recent gene-editing technologies enable manipulation of the regulation of native genes. Ultimately, understanding how gene circuitry is wired to control form and function across varied plant species, combined with advanced technology to rewire that circuitry, will unlock solutions to our greatest challenges in agriculture, energy and the environment.

Utilizing machine learning and bioinformatics analysis to identify drought-responsive genes affecting yield in foxtail millet

Drought stress is a major constraint on crop development, potentially causing huge yield losses and threatening global food security. Improving Crop's stress tolerance is usually associated with a yield penalty. One way to balance yield and stress tolerance is modification specific gene by emerging precision genome editing technology. However, our knowledge of yield-related drought-tolerant genes is still limited. Foxtail millet (Setaria italica) has a remarkable tolerance to drought and is considered to be a model C4 crop that is easy to engineer. Here, we have identified 46 drought-responsive candidate genes by performing a machine learning-based transcriptome study on two drought-tolerant and two drought-sensitive foxtail millet cultivars. A total of 12 important drought-responsive genes were screened out by principal component analysis and confirmed experimentally by qPCR. Significantly, by investigating the haplotype of these genes based on 1844 germplasm resources, we found two genes (Seita.5G251300 and Seita.8G036300) exhibiting drought-tolerant haplotypes that possess an apparent advantage in 1000 grain weight and main panicle grain weight without penalty in grain weight per plant. These results demonstrate the potential of Seita.5G251300 and Seita.8G036300 for breeding drought-tolerant high-yielding foxtail millet. It provides important insights for the breeding of drought-tolerant high-yielding crop cultivars through genetic manipulation technology.

Examining the Correlation between the Inorganic Nano-Fertilizer Physical Properties and Their Impact on Crop Performance and Nutrient Uptake Efficiency.

The physical properties of nano-fertilizers (NFs) are important in determining their performance, efficacy, and environmental interactions. Nano-fertilizers, due to their small size and high surface area-to-volume ratio, enhance plant metabolic reactions, resulting in higher crop yields. The properties of nano-fertilizers depend on the synthesis methods used. The nanoparticle's nutrient use efficiency (NUE) varies among plant species. This review aims to analyze the relationship between the physical properties of NF and their influence on crop performance and nutrient uptake efficiency. The review focuses on the physical properties of NFs, specifically their size, shape, crystallinity, and agglomeration. This review found that smaller particle-sized nanoparticles exhibit higher nutrient use efficiency than larger particles. Nano-fertilizer-coated additives gradually release nutrients, reducing the need for frequent application and addressing limitations associated with chemical fertilizer utilization. The shapes of nano-fertilizers have varying effects on the overall performance of plants. The crystalline structure of nanoparticles promotes a slow release of nutrients. Amorphous nano-fertilizers improve the NUE and, ultimately, crop yield. Agglomeration results in nanoparticles losing their nanoscale size, accumulating on the outer surface, and becoming unavailable to plants. Understanding the physical properties of nano-fertilizers is crucial for optimizing their performance in agricultural applications.

CRISPR/Cas9 opens new horizon of crop improvement under stress condition

Plants are exposed to a myriad of stresses, stemming from abiotic and biotic sources, significantly threatening agricultural productivity. The low crop yield, coupled with the global burden of population has resulted in the scarcity of quality food, exacerbating socio-economic issues like poverty, hunger, and malnutrition. Conventional breeding methods for the generation of stress-tolerant plants are time-consuming, limit genetic diversity, and are not sustainable for the consistent production of high-yielding crops. In recent years, the use of high-throughput, genome editing (GE) technique has revolutionized the crop-improvement paradigm, ushering greater prospects for agricultural progress. Among these tools, the Clustered regularly interspaced short palindromic repeat (CRISPR), and its associated nuclease protein Cas9, have appeared as a ground-breaking technology, allowing precise knockout (KO), upregulation, and downregulation of target gene expression. Apart from its high efficacy and speed, this programmable nuclease offers exceptional specificity with minimal off-target effects. Here in, we aim to review the latest findings on the application of the CRISPR/Cas9 genome editing tool for generating resilience in plants against environmental stresses.

A Dynamic Process-Based Method for Screening Salt-Tolerant and High-Yielding Crop Varieties

Maize, the most important cereal crop worldwide, is moderately sensitive to salt stress. Given the challenges of soil salinization, developing methods to screen and cultivate salt-tolerant maize varieties is vital for enhancing food security. Among the options, process model-based assisted screening is an effective method for faster and more thorough screening of salt-tolerant varieties. In this study, a method for quickly screening salt-tolerant and high-yielding crop varieties is proposed by combining a coupled model of crop growth and water–salt response with fitness function. Then, this method was applied to the saline areas of the Hetao Plain in China to demonstrate its applicability. This study includes three parts. Firstly, field trial data from 10 commonly grown maize varieties in Hetao Plain (i.e., Xianyu 335, Yinyu 238, Jindan 73, Deke 622, DK-815, Heyu 157, Xianyu 1321, Jinrun 919, Tianci 19, and Xianyu 1225) were used to calibrate the model for different maize varieties. Moreover, model accuracy was evaluated using four indexes including the regression coefficient (b), coefficient of determination (R²), root mean square error (RMSE), and Nash–Sutcliffe efficiency coefficient (NSE). Secondly, scenario simulations were conducted using the calibrated model by setting nine initial salinity scenarios to simulate daily dynamic crop growth and soil water–salt changes for the 10 maize varieties. Finally, salt-tolerant and high-yielding maize varieties were screened based on the fitness function method during crop growth periods. The results showed that the simulation model was applicable and functioned effectively for all 10 maize varieties in the region, with the determination coefficient (R2) and Nash–Sutcliffe efficiency coefficient (NSE) of simulated plant height and leaf area index being above 0.90. Furthermore, the R2 of soil water content, soil electrical conductivity, and groundwater depth are 0.51, 0.52, and 0.60, respectively. Afterward, the fitness function values were calculated to bridge the linkage between simulated indicators and scenarios to screen varieties step by step according to the predetermined percentage of screening. Jindan 73, Xianyu 1225, and DK-815 were eventually determined as the most suitable salt-tolerant and high-yielding maize varieties. Therefore, the above results show that the proposed method is suitable for saline crop variety screening with flexibility and applicability.

Regulatory Networks Underlying Plant Responses and Adaptation to Cold Stress.

Cold is a key determinant for plant growth and flowering time as well as an important environmental factor limiting plant growth and development. Recent studies have revealed the complex regulatory networks associated with plant responses to cold and identified their interconnections with signaling pathways related to light, the circadian clock, plant hormones, and pathogen defense. In this article, we review recent advances in understanding the molecular basis of cold perception and signal transduction pathways. We also summarize recent developments in the study of cold-responsive growth and flowering. Finally, we propose future directions for the study of long-term cold sensing, RNA secondary structures in response to cold, and the development of cold-tolerant and high-yield crops.

Models of osmotic stress as a tool for proteomics and metabolomics of legume seeds

Drought poses a significant challenge to the sustainable development of modern agriculture and to the achievement of high crop yields. Water deficit causes osmotic stress and triggers plant physiological responses characterized by reduced water potential, diminished stomatal conductance, and decreased photosynthetic efficiency. Long-term adaptation to osmotic stress entails intricate metabolic rearrangements, leading to the accumulation of osmoprotectants, activation of antioxidant systems, and increased biosyntheses of stress-protective proteins. The severity and duration of drought, along with plant genotype and developmental stage, influence the plant response to stress, consequently affecting crop yield and quality. Particularly in the context of legumes, which are crucial for human and animal nutrition, understanding adaptive strategies to water deficit is essential for the cultivation of drought-resistant genotypes, primarily because these crops predominantly thrive in semi-arid regions. Proteomics and metabolomics approaches, in turn, serve as valuable tools, offering critical insights into the molecular dynamics governing plant responses to drought stress. Furthermore, the use of reliable drought simulation models is imperative for the effective evaluation of legume response to water scarcity, aiding the cultivation of drought-tolerant varieties. This review highlights the perspectives of utilizing different osmotic stress models to investigate proteome and metabolome alteration within seeds of food legumes.

Site Specific Nutrient Management (SSNM): Principles, Key Features and its Potential Role in Soil, Crop Ecosystem and Climate Resilience Farming

As the world population expands, farming practices have intensified to meet increased food demand. Modern crop varieties now require higher levels of fertilization compared to traditional organic methods, which are insufficient to meet these new demands. However, inefficient nutrient management and imbalances in field nutrient levels have led to various crop issues. To achieve higher agricultural output and productivity, there has been a notable increase in the application of chemical fertilizers, which unfortunately contributes to soil and water pollution, degradation of soil fertility, and nutrient imbalances. In response, modern technologies are being leveraged to promote sustainable food production and foster balanced agricultural development. Site specific nutrient management (SSNM) aims to empower farmers to adjust fertilizer application dynamically to meet the nutrient requirements of high-yielding crops, bridging the gap between natural nutrient sources like soil, crop residues, manure, and irrigation water. It is based on the principles of 4Rs: right product, right dose, right time and right place. Modern technologies like Optical sensors, Crop Manager, Remote Sensing, Nutrient Expert®, fertility mapping, Active Canopy Sensors, Variable rate technologies etc. are used to enhance the potential of SSNM to higher level. The focus is on optimizing nutrient application timing and rates to maximize crop yield and nutrient efficiency, thereby enhancing the economic value of harvest per unit of fertilizer used, rather than simply increasing or decreasing fertilizer use.

Solar irradiance prediction with variable time lengths and multi-parameters in full climate conditions based on photovoltaic greenhouse

Photovoltaic power generation can provide energy for greenhouses and achieve high quality and high yield of crops. In reality, solar irradiance is fluctuating and intermittent. Thus, the key to ensure efficient photovoltaic power use under greenhouse environmental conditions is to provide an accurate prediction of solar irradiance. Yet, currently studies on irradiance prediction in terms of the variable time lengths, multi-parameter and full climate conditions are rather limited. To improve the comprehensive performance of the prediction model, this paper proposes models of irradiance time series prediction such as Pyraformer, Informer, Transformer and TimesNet. Those models were tested based on the synergistic combination of weather conditions (WC), sunshine time accumulation (STA/h), instantaneous total irradiance (ITI/(W/m2)) and irradiance daily accumulation (IDA/(MJ/m2)). The model performance was rigorously evaluated with 9 prediction lengths, 5 training days, 4 seasons and 5 days for 9 weather conditions. The results showed that the Transformer model had the best overall prediction performance for STA, ITI, IDA and WC at different time steps of 10 min to 24 h. All models were suitable for predicting time series within 1 h. However, TimesNet model was not suitable for predicting time series with steps outside 1 h. On the other hand, by using the sun combination dataset, the Transformer model had the best performance at a time step of 10 min. The mean absolute error (MAE), mean-square error (MSE), root mean squared error (RMSE) and coefficient of determination (R2) of ITI were 0.118 W/m2, 0.059 W/m2, 0.243 W/m2 and 93.9 %, respectively. When exploring the minimum dataset, with the increase of data samples, the prediction effect of TimesNet showed an increasing trend. While, Transformer had the best prediction effect for the dataset with one year of use. When exploring seasonality, Pyraformer model had the best prediction effect on winter and summer, and TimesNet had the best prediction effect on autumn and spring. Local prediction of 9 climate conditions showed that the effects of snow and dust storm were not ideal. The research results showed that the characterization factors that were closely linked to irradiance. The prediction scheme proposed in this paper combined the advantages of different time steps, different factor combination datasets, different data volumes and seasonality, which greatly improves the generalization ability of the model. This study can provide a reference for irradiance prediction and more refined management of photovoltaic (PV) greenhouse.