Improve Crop Yield Research Articles

Improving Crop Yield through Increasing Carbon Gain and Reducing Carbon Loss.

Photosynthesis is a process where solar energy is utilized to convert atmospheric CO2 into carbohydrates, which forms the basis for plant productivity. The increasing demand for food has created a global urge to enhance yield. Earlier, the plant breeding program was targeting the yield and yield-associated traits to enhance the crop yield. However, the yield cannot be further improved without improving the leaf photosynthetic rate. Hence, in this review, various strategies to enhance leaf photosynthesis were presented. The most promising strategies were the optimization of Rubisco carboxylation efficiency, the introduction of a CO2 concentrating mechanism in C3 plants, and the manipulation of photorespiratory bypasses in C3 plants, which are discussed in detail. Improving Rubisco's carboxylation efficiency is possible by engineering targets such as Rubisco subunits, chaperones, and Rubisco activase enzyme activity. Carbon-concentrating mechanisms can be introduced in C3 plants by the adoption of pyrenoid and carboxysomes, which can increase the CO2 concentration around the Rubisco enzyme. Photorespiration is the process by which the fixed carbon is lost through an oxidative process. Different approaches to reduce carbon and nitrogen loss were discussed. Overall, the potential approaches to improve the photosynthetic process and the way forward were discussed in detail.

Multifractal analysis of maize and soybean DNA

This paper investigates the complexity of DNA sequences in maize and soybean using the multifractal detrended fluctuation analysis (MF-DFA) method, chaos game representation (CGR), and the complexity-entropy plane approach. The study aims to understand the patterns and structures of these DNA sequences, which can provide insights into their genetic makeup and improve crop yield and quality. The results show that maize and soybean DNA sequences exhibit fractal properties, indicating a complex and self-organizing structure. We observe the persistence trend between sequences of base pairs, which indicates long-range correlations between base pairs. We also identified the stochastic nature of the DNA sequences of both species.

Annual winter crop distribution from MODIS NDVI timeseries to improve yield forecasts for Europe

Crop yield forecasts allow policy makers to anticipate market behaviour and regulate prices. Annual updates on which crops are grown where can improve crop yield forecast accuracy. Existing efforts to map crops across the European Union resulted in late-season map availability or short time series that do not meet forecasting requirements. We propose a new approach to retrieve annual winter crop maps and improve forecasting efforts by identifying pixels with dominant winter crop signals using moderate resolution imagery. These pixels are distinguished from summer crop signals based on their senescence date. When this date precedes the theoretical maturity date of a winter crop, expressed in GDD, the pixel is labelled as having a dominant winter crop signal. Our 2018 map accurately identified 77% and 83% of dominantly winter-crop area, when compared to farmers’ declaration data and a high-resolution crop map for Europe, respectively. While the resulting annual winter crop maps underestimated winter crop area, derived region-specific annual NDVI profiles better described winter crop phenology as compared to the use of static maps. Regression analysis between these regional NDVI profiles and statistical wheat yield data indicates that our annual maps help explain more yield variability than static maps, with an RMSE reduction of 3% for the EU27 as whole. The proposed approach is applicable to long historical timeseries and provides maps before the end of the agricultural season. Those maps positively impact crop yield description, notably in eastern, northern, and northeastern European regions.

Effects of Controlled-release Blended Fertilizer on Crop Yield and Greenhouse Gas Emissions in Wheat-maize Rotation System

The increasing use of nitrogen fertilizers exerts extreme pressure on the environment (e.g., greenhouse gas emissions, GHGs) for winter wheat-summer maize rotation systems in the North China Plain. The application of controlled-release fertilizers is considered as an effective measure to improve crop yield and nitrogen fertilizer utilization efficiency. To explore the impact of one-time fertilization of controlled-release blended fertilizer on crop yield and GHGs of a wheat-maize rotation system, field experiments were carried out in Dezhou Modern Agricultural Science and Technology Park from 2020 to 2022. Five treatments were established for both winter wheat and summer maize, including no nitrogen control (CK), farmers' conventional nitrogen application (FFP), optimized nitrogen application (OPT), CRU1 (the blending ratio of coated urea and traditional urea on winter wheat and summer maize was 5:5 and 3:7, respectively), and CRU2 (the blending ratio of coated urea and traditional urea on winter wheat and summer maize was 7:3 and 5:5, respectively). The differences in yield, nitrogen fertilizer utilization efficiency, fertilization economic benefits, and GHGs among different treatments were compared and analyzed. The results showed that nitrogen application significantly increased the single season and annual crop yields of the wheat-maize rotation system (P < 0.05). Compared with those of FFP, the CRU1 and CRU2 treatments increased the yields of summer maize by 0.4% to 5.6%, winter wheat by -5.4% to 4.1%, and annual yields by -1.1% to 3.9% (P > 0.05). N recovery efficiency (NRE), N agronomic efficiency (NAE), and N partial factor productivity (NPFP) were increased by -8.6%-43.4%, 2.05-6.24 kg·kg-1, and 4.24-10.13 kg·kg-1, respectively. Annual net income increased by 0.2% to 6.3%. Nitrogen application significantly increased the annual emissions of soil N2O and CO2 in the rotation system (P < 0.05) but had no effect on the annual emissions of CH4 (except for in the FFP treatment in the first year). The annual total N2O emissions under the CRU1 and CRU2 treatments were significantly reduced by 23.4% to 30.2% compared to those under the FFP treatment (P < 0.05). Additionally, nitrogen application significantly increased the annual global warming potential (GWP) of the rotation system (P < 0.05), but the intensity of greenhouse gas emissions was reduced due to the increase in crop yields. Compared with that under FFP, the annual GWP under the CRU1 and CRU2 treatments decreased by 9.6% to 11.5% (P < 0.05), and the annual GHGs decreased by 11.2% to 13.8% (P > 0.05). In summary, the one-time application of controlled-release blended fertilizer had a positive role in improving crop yield and economic benefits, reducing nitrogen fertilizer input and labor costs, and GHGs, which is an effective nitrogen fertilizer management measure to promote cleaner production of food crops in the North China Plain.

Revolutionizing Crop Nutrition: Exploring Nano Fertilizers in Agriculture

Revolutionizing Crop Nutrition: Exploring Nano Fertilizers in Agriculture

Halophilic microbial formulations for improving crop productivity and soil health under saline Vertisols of Gujarat

A study was carried out during winter (rabi) seasons of 2018 and 2019 at research farm of ICAR-Central Soil Salinity Research Institute, Regional Research Station, Bharuch, Gujarat to find out the effect of various halophilic microbial formulations in combination of organic manure and chemical fertilizers on winter (rabi) wheat (Triticum aestivum L.) productivity under saline Vertisols. Experiment was conducted in a randomized block design (RBD) comprised of 5 treatments, viz. T1 , Un-inoculated + Vermicompost @2.5 t/ha + 100% RDF (recommended dose of fertilizers); T2 , Un-inoculated + Vermicompost @2.5 t/ha + 75% RDF; T3 , Vermicompost @2.5 t/ha + 75% RDF + Halo-Azo inoculation; T4 , Vermicompost @2.5 t/ha + 75% RDF + Halo-PSB inoculation; and T5 , Vermicompost @2.5 t/ha + 75% RDF + Mix of Halo-Azo and Halo-PSB inoculation, replicated thrice. Results showed that application of different halophilic microbial formulations like Halo-Azo (Azotobacter sp. and Bacillus sp.) and Halo-PSB (consortia of Eneterobacter and Brevibacterium sp.) with vermicompost @2.5 t/ha + 75% RDF (recommended dose of fertilizers), provided at par yield with full dose (120 : 60 : 00; N : P : K kg/ha) of fertilizers, thus able to reduce 25% chemical fertilizer dose. Halo-Azo formulation performed better for improving crop yield as compared to Halo-PSB formulation. Combined application of Halo-Azo and Halo-PSB with vermicompost @2.5 t/ha and 75% RDF performed the best and provided significantly higher grain yield (2.75 t/ha) as compared to other treatments. Single and combined application of Halo-Azo and Halo-PSB with vermicompost @2.5 t/ha and 75% RDF also improved soil health parameters and significant results, i.e. declined EC (2.46 dS/m) and pH (7.33), improved organic carbon (0.69%) and nutrient availability (available N: 299.8 kg/ha and P: 28.7 kg/ha) were obtained. Results showed that integration of halophilic microbial formulations with organics and chemical fertilizers was able to improve wheat yield up to 15% than conventional farming under saline Vertisols of Gujarat.

Advancements in Biofertilizers for Pulse Crops: From Single-Strain Inoculants to Microbiome-Based Technologies

The application of biofertilizers in pulse crop agriculture has garnered significant attention as an eco-friendly and sustainable alternative to conventional chemical fertilizers. This comprehensive review aims to elucidate the progression of biofertilizer technologies from traditional single-strain inoculants to contemporary microbiome-based strategies. Pulse crops, such as beans, lentils, chickpeas, and peas, are pivotal in global agriculture due to their high protein content, nitrogen-fixing ability, and role in improving soil health. However, optimizing their productivity necessitates effective nutrient management, where biofertilizers play a crucial role. Initially, biofertilizer development concentrated on single-strain inoculants, predominantly Rhizobium species, which establish symbiotic relationships with leguminous plants to enhance nitrogen fixation and improve crop yields. Despite their proven benefits, single-strain inoculants often exhibit variable performance influenced by soil conditions, climatic factors, and interactions with indigenous soil microbiota. This variability highlights the need for more consistent and resilient biofertilizer solutions. Recent advances in microbiome research have paved the way for the development of microbiome-based biofertilizers. These advanced formulations leverage the synergistic interactions among diverse microbial communities, including bacteria, fungi, and archaea, to provide a more comprehensive and stable enhancement of soil fertility and plant growth. Microbiome-based approaches recognize the complexity of plant-microbe-soil interactions, offering improved nutrient cycling, increased plant stress resilience, and better adaptation to various environmental conditions. The benefits of microbiome-based biofertilizers are manifold, encompassing enhanced nutrient availability, improved plant health, and sustainable soil management. However, their development and application pose several challenges, including understanding the intricate dynamics of microbiome interactions, developing precise formulation and application methods, and navigating regulatory landscapes. Addressing these challenges requires multidisciplinary research and innovation, integrating genomics, metagenomics, and advanced data analytics to unravel the complexities of soil microbiomes. This includes the potential for personalized biofertilizer formulations tailored to specific soil and crop conditions, enhancing the efficacy and scalability of biofertilizer technologies.

OsLCD3 interacts with OsSAMS1 to regulate grain size via ethylene/polyamine homeostasis control.

A fundamental question in developmental biology is how to regulate grain size to improve crop yields. Despite this, little is still known about the genetics and molecular mechanisms regulating grain size in crops. Here, we provide evidence that a putative protein kinase-like (OsLCD3) interacts with the S-adenosyl-L-methionine synthetase 1 (OsSAMS1) and determines the size and weight of grains. OsLCD3 mutation (lcd3) significantly increased grain size and weight by promoting cell expansion in spikelet hull, whereas its overexpression caused negative effects, suggesting that grain size was negatively regulated by OsLCD3. Importantly, lcd3 and OsSAMS1 overexpression (SAM1OE) led to large and heavy grains, with increased ethylene and decreased polyamines production. Based on genetic analyses, it appears that OsLCD3 and OsSAMS1 control rice grain size in part by ethylene/polyamine homeostasis. The results of this study provide a genetic and molecular understanding of how the OsLCD3-OsSAMS1 regulatory module regulates grain size, suggesting that ethylene/polyamine homeostasis is an appropriate target for improving grain size and weight.

Exploring natural genetic variation in photosynthesis-related traits of barley in the field.

Optimizing photosynthesis is considered an important strategy for improving crop yields to ensure food security. To evaluate the potential of using photosynthesis-related parameters in crop breeding programs, we measured chlorophyll fluorescence along with growth-related and morphological traits of 23 barley inbred lines across different developmental stages in field conditions. The photosynthesis-related parameters were highly variable, changing with light intensity and developmental progression of plants. Yet, the variation in photosystem II quantum yield observed among the inbred lines in the field largely reflected the variation in CO2 assimilation properties in controlled climate chamber conditions, confirming that the chlorophyll fluorescence-based technique can provide proxy parameters of photosynthesis to explore genetic variation under field conditions. Heritability (H2) of the photosynthesis-related parameters in the field ranged from 0.16 for the quantum yield of non-photochemical quenching to 0.78 for the fraction of open photosystem II center. Two parameters, the maximum photosystem II efficiency in the light-adapted state (H2=0.58) and the total non-photochemical quenching (H2=0.53), showed significant positive and negative correlations, respectively, with yield-related traits (dry weight per plant and net straw weight) in the barley inbred lines. These results indicate the possibility of improving crop yield through optimizing photosynthetic light use efficiency by conventional breeding programs.

Cutting-Edge of Genotyping by Sequencing (GBS) for Improving Drought and Salinity Stress Tolerance in Watermelon (Citrullus lanatus L.): A Review

AbstractGenotyping by sequencing (GBS) is a very helpful approach and one of the most useful techniques for examining and analysing the genetic variation of various lines and varieties. GBS technology was used to identify single nucleotide polymorphisms (SNPs) and assess genetic variation in several watermelon accessions. GBS application in watermelon breeding programs has recently become a popular technique among many breeders. Watermelon (Citrullus lanatus L.) is a warm-season crop that is widely cultivated for its delicious fruit. And it is one of the most economically significant crops in the world. However, watermelon cultivation is frequently hampered by abiotic stressors such as drought and salinity. Recently, there has been a growing body of research on the mechanisms that allow watermelon to tolerate these stresses and improve crop yield. Generally, cucurbits are beneficial to human health, they provide necessary minerals, fibre, and nutrient components. Therefore, this review demonstrates the cutting edge of using GBS technology to identify genetic design of several features in watermelon to improve abiotic stresses (drought and saline). The application of the GBS technique has provided a distinct advantage in watermelon breeding studies. Based on GBS approach, many new candidate genes in watermelon lines control a variety of traits including saline and drought tolerance, fruit rind color, disease tolerance, nutrient components, size, and fruit shape were discovered. Modern breeding techniques are being used to develop economically viable vegetable crops that will meet customer preferences and needs. Further research is needed to enhance watermelon production.

Optimizing Maize Yield and Resource Efficiency Using Surface Drip Fertilization in Huang-Huai-Hai: Impact of Increased Planting Density and Reduced Nitrogen Application Rate

Improving crop yield and resource utilization efficiency is essential for agricultural productivity. In the Huang-Huai-Hai maize region of China, optimizing planting density, nitrogen (N) application, and fertilization methods are key strategies for enhancing maize yield and N use efficiency. However, traditional approaches have often hindered these improvements. To address this issue, we conducted a study in Baoding, Hebei, from 2022 to 2023, focusing on planting density, the N application rate, and the fertilization method on grain yield, N use efficiency, water use efficiency (WUE), and economic benefits. The trial involved two planting densities: 6.0 × 104 plants ha−1 (D1, typical local density) and 9.0 × 104 plants ha−1 (D2). Five N application rates were tested: 0 (N0), 120 kg ha−1 (N1), 180 kg ha−1 (N2), 240 kg ha−1 (N3), and 300 kg ha−1 (N4). The control treatment (D1N4) utilized the local planting density and traditional fertilization methods. Our findings revealed a positive correlation between the maize yield and N application rate, with the maximum yields (13.78–13.88 t ha−1), high WUE (24.42–29.85 kg m−3), agronomic efficiency of N (AEN) (18.11–19.00 kg kg−1), and economic benefits (2.44 × 104–2.47 × 104 CNY ha−1) observed with D2N3 and surface drip fertilization. This was significantly higher than the yield and resource efficiency of traditional fertilization methods and saved fertilizer and production costs. Therefore, adopting surface drip fertilization, adjusting planting density, and optimizing N application rates proved effective in enhancing maize yield and resource utilization efficiency in the Huang-Huai-Hai maize region.

Insights into Wheat Genotype‒Sphaerodes mycoparasitica Interaction to Improve Crop Yield and Defence against Fusarium graminearum: An Integration of FHB Biocontrol in Canadian Wheat Breeding Programmes.

Fusarium head blight (FHB) is a major threat to wheat crop production and food security worldwide. The creation of resistant wheat cultivars is an essential component of an integrated strategy against Fusarium graminearum, the primary aetiological agent that causes FHB. The results of this study show that the deployment of proto-cooperative interactions between wheat genotypes and mycoparasitic biocontrol agents (BCAs) can improve crop yield and plant resistance in controlling the devastating effects of FHB on wheat agronomic traits. A Fusarium-specific mycoparasite, Sphaerodes mycoparasitica, was found to be compatible with common and durum wheat hosts, thus allowing the efficient control of F. graminearum infection in plants. Four genotypes of wheat, two common wheat, and two durum wheat cultivars with varying FHB resistance levels were used in this greenhouse study. The BCA treatments decreased FHB symptoms in all four cultivars and improved the agronomic traits such as spike number, spike weight, seed weight, plant biomass, and plant height which are vital to grain yield. Conversely, the F. graminearum 3ADON chemotype treatment decreased the agronomic trait values by up to 44% across cultivars. Spike number, spike weight, and seed weight were the most improved traits by the BCA. A more measurable improvement in agronomic traits was observed in durum wheat cultivars compared to common wheat.

Imagining AI-driven decision making for managing farming in developing and emerging economies

Agriculture is the backbone of numerous developing and emerging economies, supporting millions of livelihoods and playing a crucial role. The transformative integration of AI-driven tools, big-data analytics, and advanced technologies, such as drones and satellites, can transform agricultural decision-making in these developing and emerging economies and globally. Acknowledging the intricate web of biophysical and socioeconomic factors that influence agricultural systems, we highlight the pressing need for innovative approaches. We explore the critical role of big-data analytics, Computational Fluid Dynamics (CFD), Machine Learning (ML), and remote sensing in transforming the sector, with a focus on enhancing efficiency, sustainability, and productivity. In this context, continuous real-time monitoring becomes essential, allowing farmers to manage their agricultural systems with precision. Implementing AI-driven tools and devices enables the collection and analysis of data in real time, leading to timely decisions that can significantly improve crop yields and resource management. This study discusses the impacts of big-data analytics on agriculture, including reduced pesticide use and improved crop yields. It presents concrete examples of AI applications in crop and animal farming, emphasizing precision irrigation, fertilizer management, animal health monitoring, and reproductive efficiency. We advocate for customizing these technologies to suit local contexts, which can lead to ensuring deep integration into developing and emerging economies' agricultural practices. In conclusion, the paper calls for collaborative efforts from governments, the private sector, and educational institutions to make these technologies accessible, affordable, and culturally relevant.

Paradise by the far-red light: Far-red and red:blue ratios independently affect yield, pigments, and carbohydrate production in lettuce, Lactuca sativa.

In controlled environment agriculture, customized light treatments using light-emitting diodes are crucial to improving crop yield and quality. Red (R; 600-700 nm) and blue light (B; 400-500 nm) are two major parts of photosynthetically active radiation (PAR), often preferred in crop production. Far-red radiation (FR; 700-800 nm), although not part of PAR, can also affect photosynthesis and can have profound effects on a range of morphological and physiological processes. However, interactions between different red and blue light ratios (R:B) and FR on promoting yield and nutritionally relevant compounds in crops remain unknown. Here, lettuce was grown at 200 µmol m-2 s-1 PAR under three different R:B ratios: R:B87.5:12.5 (12.5% blue), R:B75:25 (25% blue), and R:B60:40 (40% blue) without FR. Each treatment was also performed with supplementary FR (50 µmol m-2 s-1; R:B87.5:12.5+FR, R:B75:25+FR, and R:B60:40+FR). White light with and without FR (W and W+FR) were used as control treatments comprising of 72.5% red, 19% green, and 8.5% blue light. Increasing the R:B ratio from R:B87.5:12.5 to R:B60:40, there was a decrease in fresh weight (20%) and carbohydrate concentration (48% reduction in both sugars and starch), whereas pigment concentrations (anthocyanins, chlorophyll, and carotenoids), phenolic compounds, and various minerals all increased. These results contrasted the effects of FR supplementation in the growth spectra; when supplementing FR to different R:B backgrounds, we found a significant increase in plant fresh weight, dry weight, total soluble sugars, and starch. Additionally, FR decreased concentrations of anthocyanins, phenolic compounds, and various minerals. Although blue light and FR effects appear to directly contrast, blue and FR light did not have interactive effects together when considering plant growth, morphology, and nutritional content. Therefore, the individual benefits of increased blue light fraction and supplementary FR radiation can be combined and used cooperatively to produce crops of desired quality: adding FR increases growth and carbohydrate concentration while increasing the blue fraction increases nutritional value.

Greater mesophyll conductance and leaf photosynthesis in the field through modified cell wall porosity and thickness via AtCGR3 expression in tobacco.

Mesophyll conductance (gm) describes the ease with which CO2 passes from the sub-stomatal cavities of the leaf to the primary carboxylase of photosynthesis, Rubisco. Increasing gm is suggested as a means to engineer increases in photosynthesis by increasing [CO2] at Rubisco, inhibiting oxygenation and accelerating carboxylation. Here, tobacco was transgenically up-regulated with Arabidopsis Cotton Golgi-related 3 (CGR3), a gene controlling methylesterification of pectin, as a strategy to increase CO2 diffusion across the cell wall and thereby increase gm. Across three independent events in tobacco strongly expressing AtCGR3, mesophyll cell wall thickness was decreased by 7%-13%, wall porosity increased by 75% and gm measured by carbon isotope discrimination increased by 28%. Importantly, field-grown plants showed an average 8% increase in leaf photosynthetic CO2 uptake. Up-regulating CGR3 provides a new strategy for increasing gm in dicotyledonous crops, leading to higher CO2 assimilation and a potential means to sustainable crop yield improvement.

Ragi Pest Control

The rise of global population has put increasing pressure on the agriculture industry to meet the demand for food. However, the growing use of pesticides and insecticides in conventional farming practices has caused significant harm to the environment and human health. Thus, there is a growing interest in using sustainable agriculture practices that reduce the use of these harmful chemicals. One such practice is pest detection, which enables farmers to detect pests in their crops before they cause significant damage. In this context, this project aims to develop a pest detection system using IoT and Arduino. The system will be designed to detect pests in crops through a combination of sensors and machine learning algorithms. The system will consist of an Arduino microcontroller, soil moisture sensors, temperature and humidity sensors, infrared sensors or camera modules, and a Wi-Fi or Bluetooth module. The sensors will collect data on soil moisture levels, temperature, humidity, and pest activity. The data will be sent to a cloud-based server or database for analysis and visualization. The infrared sensor or camera module will detect the presence of pests in the crops. The system will use machine learning algorithms to distinguish between pests and other objects, such as leaves or debris. When pests are detected, the system will alert the farmer through a buzzer or LED connected to the Arduino board. The farmer can then take appropriate action, such as applying pesticide or removing infested plants. The pest detection system has the potential to reduce the use of harmful pesticides and insecticides in agriculture, as farmers will be able to identify pests before they cause significant damage. The system will also provide farmers with real-time information on pest activity, enabling them to take proactive measures to control pests and reduce crop damage. Additionally, the system can track and store the pest detection data over time, allowing farmers to monitor trends and patterns in pest activity. In conclusion, this project proposes the development of a pest detection system using IoT and Arduino that will enable farmers to monitor pest activity in their crops in real-time. The system has the potential to reduce the use of harmful chemicals in agriculture and improve crop yield while ensuring sustainable and environmentally friendly practices.

Hyperfidelis: A Software Toolkit to Empower Precision Agriculture with GeoAI

The potential of artificial intelligence (AI) and machine learning (ML) in agriculture for improving crop yields and reducing the use of water, fertilizers, and pesticides remains a challenge. The goal of this work was to introduce Hyperfidelis, a geospatial software package that provides a comprehensive workflow that includes imagery visualization, feature extraction, zonal statistics, and modeling of key agricultural traits including chlorophyll content, yield, and leaf area index in a ML framework that can be used to improve food security. The platform combines a user-friendly graphical user interface with cutting-edge machine learning techniques, bridging the gap between plant science, agronomy, remote sensing, and data science without requiring users to possess any coding knowledge. Hyperfidelis offers several data engineering and machine learning algorithms that can be employed without scripting, which will prove essential in the plant science community.

Evaluating machine learning models and identifying key factors influencing spatial maize yield predictions in data intensive farm management

Understanding the relationships between crop yields, soil properties, weather patterns and input applications is important for optimizing agricultural production. Data variation analysis using statistical and machine learning (ML) approaches can help identify and understand the practices that optimize yield. The objectives of this study were (i) to evaluate the predictive accuracy of selected ML models for estimating grain yields in on-farm maize (Zea mays L.) trials with different combinations of seeding and fertilizer rates in a commercial field, and (ii) to investigate the ability of ML models to assist in identifying yield-limiting factors in the same field. Multiple linear regression, multilayer perceptron, decision tree, and random forest (RF) ML models were trained and tested using crop management and soil from a data-intensive farm management (DIFM) trial and remotely sensed data. The dataset consisted of multiple subplot treatment observations of crop management, soil properties and normalized difference vegetation index (NDVI), linked to final grain yield for the 2019/2020 and 2020/2021 seasons. The RF had the best combination of high correlation (R2 = 0.69 and 0.80) and low error (MAPE = 5.4 and 8.4% and RMSE = 0.69 and 0.95 t ha−1) when compared to other models for both seasons. Feature importance analysis revealed that urea application was consistently the most critical variable and explained yield variations to the greatest extent, whereas soil phosphorus (P), plant population, and sodium in 2020, and soil P, soil pH, clay content, and plant population in 2021 emerged as the most influential factors for explaining yields. This study concluded that the RF model was the best for spatial yield predictions using DIFM trial datasets. There was also variability between seasons in yield limiting factors resulting from temporal variations in growing conditions. To effectively apply insights from yield prediction models, it is crucial that the variables incorporated into these models have a significant connection to yield and the findings can be translated into actionable management decisions. The DIFM trials combined with ML can play an important role in advancing the field of precision agriculture by providing valuable insights into the complex interactions between crops, soils, and management practices, and identifying new opportunities for improving crop yields and environmental sustainability.

Mitigating salt stress by conditioning seeds with ultraviolet light (UV-C) in white oats (Avena sativa L.)

ABSTRACT Seed conditioning with ultraviolet light (UV-C) might (1) improve crop yield and quality, (2) reduce the use of agrochemicals during cultivation, and (3) increase plant survival in high salinity environments. The aim of this study was to examine the effects of UV-C conditioning of white oat seeds at two doses (0.85 and 3.42 kJ m−2) under salinity stress (100 mM NaCl). Seeds were sown on germination paper and kept in a germination chamber at 20°C. Germination and seedling growth parameters were evaluated after 5 and 10 days. Data demonstrated that excess salt reduced germination and initial growth of white oat seedlings. In all the variables analyzed, exposure of seeds to UV-C under salt stress exerted a positive effect compared to non-irradiated control. The attenuating influence of UV-C in germination was greater at 0.85 than at 3.42 kJ m−2. Thus, data indicate that conditioning white oat seeds in UV-C light produced greater tolerance to salt stress. These findings suggest that UV-C conditioning of white oat seeds may be considered as a simple and economical strategy to alleviate salt-induced stress.

Integration of nitrate and abscisic acid signaling in plants.

To meet the demands of the new Green Revolution and sustainable agriculture, it is important to develop crop varieties with improved yield, nitrogen use efficiency, and stress resistance. Nitrate is the major form of inorganic nitrogen available for plant growth in many well-aerated agricultural soils, and acts as a signaling molecule regulating plant development, growth, and stress responses. Abscisic acid (ABA), an important phytohormone, plays vital roles in integrating extrinsic and intrinsic responses and mediating plant growth and development in response to biotic and abiotic stresses. Therefore, elucidating the interplay between nitrate and ABA can contribute to crop breeding and sustainable agriculture. Here, we review studies that have investigated the interplay between nitrate and ABA in root growth modulation, nitrate and ABA transport processes, seed germination regulation, and drought responses. We also focus on nitrate and ABA interplay in several reported omics analyses with some important nodes in the crosstalk between nitrate and ABA. Through these insights, we proposed some research perspectives that could help to develop crop varieties adapted to a changing environment and to improve crop yield with high nitrogen use efficiency and strong stress resistance.