Stages Of Crop Development Research Articles

Take a Deep BReath: Manipulating brassinosteroid homeostasis helps cereals adapt to environmental stress.

Global climate change leads to the increased occurrence of environmental stress (including drought and heat stress) during the vegetative and reproductive stages of cereal crop development. Thus, more attention should be given to developing new cereal cultivars with improved tolerance to environmental stress. However, during the development of new stress-tolerant cereal cultivars, the balance between improved stress responses (which occur at the expense of growth) and plant yield needs to be maintained. Thus, the urgent need for developing new cereal germplasm with improved stress tolerance could be fulfilled using semi-dwarf cereal mutants defective in brassinosteroid (BR) biosynthesis or signaling. BRs are steroid phytohormones that regulate various developmental and physiological processes throughout the plant life cycle. Mutants defective in BR biosynthesis or responses show reduced plant height (dwarfism or semi-dwarfism). Importantly, numerous reports indicate that genetic modification or biotechnological manipulation of BR biosynthesis or signaling genes in cereals such as rice (Oryza sativa), maize (Zea mays), wheat (Triticum aestivum), and barley (Hordeum vulgare), which are of crucial importance for global agriculture, may facilitate the development of cereal germplasm with improved stress tolerance. This review presents a comprehensive overview of the genetic manipulation of BR homeostasis in the above-mentioned cereal crops aimed at improving plant responses to various environmental stresses, such as drought, salinity, oxidative stress, thermal stress, and biotic stresses. We highlight target BR-related genes and the effects of genetic manipulation (gene editing, overexpression, silencing or miRNA-mediated regulation) on plant adaptability to various stresses and provide future perspectives.

High throughput phenotyping in soybean breeding using RGB image vegetation indices based on drone

This study investigates the effectiveness of high-throughput phenotyping (HTP) using RGB images from unmanned aerial vehicles (UAVs) to assess vegetation indices (VIs) in different soybean pure lines. The VIs were accessed at various stages of crop development and correlated with agronomic performance traits. The field research was conducted in the experimental area of the Mato Grosso do Sul Foundation, Brazil, with 60 soybean pure lines. RGB images were captured at multiple stages of development (28, 37, 49, 70, 86, 105, 115, and 120 days after sowing). We used a linear mixed effects model, with restricted maximum likelihood (REML)/best linear unbiased prediction (BLUP) methods, to estimate variance components and genetic correlations, and to predict genotypic values. Significant genetic differences were identified among genotypes for all agronomic traits evaluated (p< 0.001), with high accuracy and heritability for plant height, maturity at R8, and 100-seed weight. There was a significant genotype flight data interaction impact on VI expression, emphasizing the importance of timing data collection to enhance HTP with VIs in agronomic performance evaluation. In the early stages, the indices varied depending on the environment. On the other hand, the indices showed higher correlations with the traits of plant height and maturity at the R8 stage, at 105, 115, and 120 days after sowing. HTP with VIs based on RGB images from UAVs has proven to be more effective in the early and final stages of soybean development, providing essential information for the selection of superior genotypes. This study highlights the importance of the temporal approach in HTP, optimizing the selection of soybean genotypes and refining agricultural management strategies.

Mitigating Drought by Exogenous Potassium-mediated Improvements in Water Relation, Antioxidant Defense, Morpho-physiological and Biochemical Attributes of Black Gram [Vigna mungo (L.) Hepper

Background: Globally, drought stress (DS) incidence in early development and grain-filling stages of crops like black gram has posed a serious constraint to the growth and yield of legume crops. To ensure the food and nutritional security of the rising human population, requisites developing eco-friendly, pro-farmers and cost-effective DS mitigation strategies for imparting yield sustainability to black gram. Methods: In this trial, treatments included control group entailing control (normal watering), water spray (WA, positive control), control+50 mg L-1 K2SO4, control +100 mg L-1 K2SO4, control +150 mg L-1 K2SO4, while drought treatment included drought stress (plant exposed to 15-20% by suspending water supply), water spray (WA, positive control), drought +50 mg L-1 K2SO4, drought +100 mg L-1 K2SO4 and drought +150 mg L-1 K2SO4. Result: The results exhibited that the DS seriously declined plant growth, relative water content and water potential by 24.2% and 39.3%, respectively, inducing higher levels of malondialdehyde (MDA) content and hydrogen peroxide (H2O2) concentration that reduced cell membrane stability, stomatal conductance and photosynthetic rate, than the control. However, the foliar applied K significantly improved plant growth, plant water status, gas exchange and photosynthetic performance, chlorophyll content and antioxidant enzyme activity. Exogenous application of K further reduced lipid peroxidation, cell membrane injury and hydrogen peroxide by 12.7, 17.6 and 8.70%, respectively.

Survey and Disease Prevalence of Sorghum Zonate Leaf Spot in the Terai Region of Uttarakhand, India

Aims: The objective of the study was to conduct a disease survey to understand the regional dynamics of sorghum zonate leaf spot disease in the Uttarakhand Terai region. Study Design: The investigation was carried out using a stratified random sampling technique. Place and Duration of Study: GBPUA&amp;T, Pantnagar and Terai Region of Uttarakhand during Kharif 2024. Methodology: The survey was conducted from July to September 2024, covering several stages of sorghum crop development. A stratified random sampling technique was used to select fields, and observations were recorded on plants making five stops in a W-pattern to cover the whole field on foot. The disease incidence and severity were scored and recorded. Results: The survey covered 25 fields and disease incidence &amp; severity were recorded using a standard scale. The study found that the prevalence and severity of zonate leaf spot disease (G. sorghi) varied significantly between locations, with the highest disease incidence in Chikaghat (71.11%) and lowest in Kanthgri (43.56%) from Sitarganj block. Environmental parameters such as temperature, humidity, and rainfall were directly linked to the occurrence and severity of zonate leaf spots. Conclusion: The study concluded that zonate leaf spot disease is a significant problem in the Uttarakhand Terai Region. Developing efficient management techniques to reduce zonate leaf spot negative effects on crop productivity might be facilitated by knowledge of the distribution and contributing elements. To control zonate leaf spot in these areas, more study is required to investigate resistant cultivars and better agronomic methods.

A high-precision automatic diagnosis method of maize developmental stage based on ensemble deep learning with IoT devices

A high-precision automatic diagnosis method of maize developmental stage based on ensemble deep learning with IoT devices

SYMPATHIQUE: image-based tracking of symptoms and monitoring of pathogenesis to decompose quantitative disease resistance in the field

BackgroundQuantitative disease resistance (QR) is a complex, dynamic trait that is most reliably quantified in field-grown crops. Traditional disease assessments offer limited potential to disentangle the contributions of different components to overall QR at critical crop developmental stages. Yet, a better functional understanding of QR could greatly support a more targeted, knowledge-based selection for QR and improve predictions of seasonal epidemics. Image-based approaches together with advanced image processing methodologies recently emerged as valuable tools to standardize relevant disease assessments, increase measurement throughput, and describe diseases along multiple dimensions.ResultsWe present a simple, affordable, and easy-to-operate imaging set-up and imaging procedure for in-field acquisition of wheat leaf image sequences. The development of Septoria tritici blotch and leaf rusts was monitored over time via robust methods for symptom detection and segmentation, spatial alignment of images, symptom tracking, and leaf- and symptom characterization. The average accuracy of the spatial alignment of images in a time series was approximately 5 pixels (~ 0.15 mm). Leaf-level symptom counts as well as individual symptom property measurements revealed stable patterns over time that were generally in excellent agreement with visual impressions. This provided strong evidence for the robustness of the methodology to variability typically inherent in field data. Contrasting patterns in the number of lesions resulting from separate infection events and lesion expansion dynamics were observed across wheat genotypes. The number of separate infection events and average lesion size contributed to different degrees to overall disease intensity, possibly indicating distinct and complementary mechanisms of QR.ConclusionsThe proposed methodology enables rapid, non-destructive, and reproducible measurement of several key epidemiological parameters under field conditions. Such data can support decomposition and functional understanding of QR as well as the parameterization, fine-tuning, and validation of epidemiological models. Details of pathogenesis can translate into specific symptom phenotypes resolvable using time series of high-resolution RGB images, which may improve biological understanding of plant-pathogen interactions as well as interactions in disease complexes.

An integrated anthracnose management approach in Tommy Atkins mango cultivars in Cundinamarca - Colombia

Anthracnose, caused by Colletotrichum gloeosporioides in mango production, can lead to crop losses of 60 %. Synthetic fungicides constitute its leading management strategy. We evaluated combinations of different management practices to control anthracnose in a commercial Tommy Atkins mango grove in 2015 and 2016. We followed a randomized complete block experimental design with a subdivided plot arrangement composed of 12 treatments, three replicates per treatment, and one mango tree per replicate for 36 trees. Pruning was practiced at plot level (with and without pruning), nutrients were applied to subplots (soil fertilizer, foliar nutrient application, and no nutrient supply), and at sub-subplot level, three anthracnose management treatments were given (chemical, biological, and no treatment). In 2015 and 2016, the treatments involving natural or biological applications against anthracnose plus nutrient supply led to the most significant reductions in quiescent leaf infections and disease presence in flowers and fruits. In addition, pruning at specific crop development stages improved results. In light of our results, this integrated anthracnose management approach in mango production can deliver the expected results if implemented consistently.

Interaction of the Far-red Radiation Intensity and CO2 Concentration during Early Indoor Production Stages of Red Leaf Lettuce

Although far-red (FR) radiation (700–750 nm) is beyond the traditional photosynthetically active radiation (PAR) waveband (400–700 nm), recent findings suggest equivalent or similar photosynthetic action when applied along with shorter PAR wavelengths. Potential interactive effects of FR with other environmental factors that affect photosynthesis, such as CO2, have not been widely established. In the present study, the effects of four FR photon flux densities (0, 20, 40, or 60 µmol·m−2·s−1) substituting for red (R; 600–700 nm) in combination with three CO2 concentrations (400, 800, or 1200 µmol·mol−1) were investigated at three distinctive stages of young red lettuce production. We tested whether the photomorphogenic response of young plant leaves to FR would result in a higher light interception area, as well as whether interactive effects of CO2 with FR might modify the effects of FR alone. The total photon flux density (TPFD) was maintained at a low limiting intensity of 200 µmol·m−2·s−1 (daily light integral: 11.5 mol·m−2·d−1), consistent with indoor commercial setpoints for sole-source lighting. Although blue (B; 400–500 nm) was held constant at 20 µmol·m−2·s−1 for all treatments, different combinations of R and FR were adjusted. Red oakleaf lettuce (Lactuca sativa ‘Rouxai’) used as a model test crop was harvested at 14 days (small baby greens), 18 days (standard baby greens), and 22 days (teen greens) after sowing. Crop productivity was highest at 800 µmol·mol−1 CO2 for small and standard baby stages. At the teen green stage, plant productivity was similar for the two elevated CO2 concentrations. The FR substitution for R did not affect dry biomass accumulation at all CO2 concentrations tested, thus supporting equivalent photosynthetic action. A photomorphogenic effect of FR that elongated but did not increase the area of leaves persisted from the earliest to the most mature stage of crop development tested. The FR inclusion in the light recipe resulted in longer, thinner leaves with lower biomass compared to those without FR. At higher FR fluxes, purple pigmentation reduction occurred. However, the interaction of elevated CO2 with FR radiation counteracted purple pigment reduction caused by FR alone. Substitution of FR in the light recipe for indoor production of young ‘Rouxai’ lettuce at low limiting light intensity did not improve productivity, even in combination with elevated CO2.

Capillary rise quantification improves irrigation performance in pear orchards

The shallow water table in Río Negro and Neuquén valley causes a capillary rise (CR) that modifies the water content in soil profile. Therefore, irrigation performance is expected to be affected by the capillary water input into the root zone. The aim of this study was to evaluate the effect of CR on surface irrigation performance during 2020-2021 growing season in a pear orchard. In a Bartlett pear orchard planted in 2003, three irrigation moments were evaluated, and irrigation sheets were calculated to obtain efficiency. Water table level (WTL) was measured monthly in an open aquifer piezometer. CR was calculated with the software UPFLOW. Soil water content was measured with a Frequency Domain Reflectometry (FDR) sensor at: 0.20 m, 0.40 m, and 0.60 m. Water use efficiency (WUE) and water productivity were calculated using pear crop yield and the irrigation sheets applied and the crop water demand, respectively. WTL was shallower in spring than in the rest of the season. The mean depth fluctuated between 0.70-1.20 m during spring, affecting irrigation performance. Data of FDR deepest sensor showed an increase of soil moisture due to CR. Capillary contribution negatively affects irrigation efficiency if it is not included in the water balance. Irrigation schedules can be re-arranged considering soil moisture and CR. In this way, the necessary water sheets could be applied in each crop development stage adjusted to water demand. Improving irrigation performance and WUE enables a sustainable water management strategy in pear production.

Influence of crop development and fruit retention on the timing of crop maturity in Ultra-narrow row cotton

Influence of crop development and fruit retention on the timing of crop maturity in Ultra-narrow row cotton

Modelling Soil Moisture Content with Hydrus 2D in a Continental Climate for Effective Maize Irrigation Planning

In light of climate change and limited water resources, optimizing water usage in agriculture is crucial. This study models water productivity to help regional planners address these challenges. We integrate CROPWAT-based reference evapotranspiration (ETo) with Sentinel 2 data to calculate daily evapotranspiration and water needs for maize using soil and climate data from 2021 to 2023. The HYDRUS model predicted volumetric soil moisture content, validated against observed data. A 2D hydrodynamic model within HYDRUS simulated temporal and spatial variations in soil water distribution for maize at a non-irrigated site in Hungary. The model used soil physical properties and crop evapotranspiration rates as inputs, covering crop development stages from planting to harvest. The model showed good performance, with R² values of 0.65 (10 cm) and 0.81 (60 cm) in 2021, 0.51 (10 cm) and 0.50 (60 cm) in 2022, and 0.38 (10 cm) and 0.72 (60 cm) in 2023. RMSE and NRMSE values indicated reliability. The model revealed water deficits and proposed optimal irrigation schedules to maintain soil moisture between 32.2 and 17.51 V/V%. This integrated approach offers a reliable tool for monitoring soil moisture and developing efficient irrigation systems, aiding maize production’s adaptation to climate change.

Overcoming mechanistic limitations of process-based phenological models: A data clustering method for large-scale applications

Overcoming mechanistic limitations of process-based phenological models: A data clustering method for large-scale applications

Thermal sum accumulated in development stages of tomato crop for industrial processing

Tomato crop for industrial processing is very sensitive to weather variations. Therefore, the objective of this research was to quantify the duration (days and thermal sum) of each developmental stage of different hybrids in two different planting date. For this, 12 commercial hybrids were evaluated: H-1301, BS-P0033, CVR-8161, HM-7885, CVR-6116, HM-7883, H-1536, CVR-2909, TPX-26856, CVR-8126, CTI-35 and N-901, in Abadia de Goiás, Brazil, planted on 03/31/2020 and 05/26/2020. Experimental area with 3,120 m2, 4 blocks with 12 experimental plots each (12 hybrids), and each plot, formed by 3 double planting lines of 10 m, spaced 0.6 m x 1.2 m, with plants every 0.37 m, totaling 27 plants per line and 162 plants per plot. Plant phenology was monitored daily: phase I (planting to set), phase II (set to flowering), phase III (flowering to beginning of maturation) and phase IV (IVa beginning of maturation up to 50% of mature fruits and phase IVb (from 50% to 90% of mature fruits). The results show, on average of all hybrids, thermal sum of 1,394 and 1,364 oC obtained in 124.6 and 116.1 days of cycle, in first and second cycle, respectively. In first cycle, there was a difference in thermal sum between the hybrids in stage IV and in total cycle, which hybrids BS-P0033, CVR-8161, HM-7885, HM-7883, CVR-2909 and TPX-26856 accumulated from 81.29 to 112.88 oC in 6 to 9.3 days to complete maturation, while other hybrids accumulated from 122.85 to 148.11 oC in 10.3 to 13 days. The hybrids BS-P0033, CVR-8161, HM-7885 and HM-7883 had a shorter total cycle duration from 1,334.21 to 1,379.98 oC (119 to 123 days), while other hybrids variated of 1,391.45 to 1,437.05 oC (124.3 to 129 days). The stages of setting, vegetative and beginning of maturation up to 50% of ripe fruits, showed variation in thermal sum and duration of phase, depending on planting date. For the first cycle, the duration of average vegetative stage was 21.7% longer, accumulating 34.1% more oC, compared to the second cycle, providing greater plant height (43.8%) and higher productivity (19.8%). This shows that is need to change irrigation management strategy between transplanting date and crop development stages, making irrigation adjustments by thermal sum and not by number of days for development stage.

Metabolic Control of Sugarcane Internode Elongation and Sucrose Accumulation

The relationship between metabolic changes occurring in the developing internodes of sugarcane and the final yield and sugar characteristics is poorly understood due to the lack of integration between phenotypic and metabolic data. To address this issue, a study was conducted where sugarcane metabolism was modeled based on the measurement of cellular components in the top internodes, at two stages of crop development. The study also looked at the effects of Trinexapac-ethyl (Moddus®) on growth inhibition. The metabolome was measured using GC-analysis, while LC-MS/MS was used to measure proteome changes in the developing internodes. These data were then integrated with the metabolic rates. Regardless of the growth rate, internode elongation was restricted to the top five internodes. In contrast, sucrose and lignin accumulation was sensitive to the growth rate. Crossover plots showed that sucrose accumulation only occurred once the cell wall synthesis had slowed down. These data suggest that sucrose accumulation controlled a reduction in sucrose breakdown for metabolic activity and a reduction in demand for carbon for cell wall polysaccharide synthesis. This study also found that nucleotide sugar metabolism appears to be a key regulator in regulating carbon flow during internode development.

Enzyme activities and microbial nutrient limitations in response to digestate and compost additions in organic matter poor soils in the Marches, Italy

Enzyme activities and microbial nutrient limitations in response to digestate and compost additions in organic matter poor soils in the Marches, Italy

Hyperspectral crop image classification via ensemble of classification model with optimal training

Agriculture is a significant source of income, and categorizing the crop has turned into vital factor that aids more in the crop production sector. Traditionally, crop development stage determination is done manually by eye inspection. However, producing high-quality crop type maps using modern approaches remains difficult. In this paper, the hyperspectral crop image classification model is proposed that includes four stages, they are (a) preprocessing, (b) segmentation, (c) feature extraction and (d) classification. In the preprocessing step, the hyperspectral image is provided as input, where the filtering process will carried out using median filtering. The filtered image is then used as the segmentation’s input. The image is segmented in the segmentation step using the enhanced entropy-based fuzzy c-means technique. Subsequently, spectral spatial features and vegetation index-based features are derived from segmented images. The final step is the classification, where the ensemble of classification model will be used that includes models like Convolutional Neural Networks (CNN), Deep Maxout (DMO), Recurrent Neural Networks (RNN), and Bidirectional Gated Recurrent Unit (Bi-GRU), respectively. The proposed Self Improved Tasmanian devil Optimization (SI-TDO) approach has optimally adjusted the Bi-GRU model’s training weights to enhance ensemble classification performance. Finally, the effectiveness of the proposed SI-TDO method compared to the traditional algorithm is examined for several metrics. The SI-TDO obtained the greatest accuracy of 94.68% in training rate 80, while other existing models have the lowest ratings.

Increased global cropland greening as a response to the unusual reduction in atmospheric PM₂.₅ concentrations during the COVID-19 lockdown period

The devastating effects of COVID-19 pandemic have widely affected human lives and economy across the globe. There were significant changes in the global environmental conditions in response to the lockdown (LD) restrictions made due to COVID-19. The direct impact of LD on environment is analysed widely across the latitudes, but its secondary effect remains largely unexplored. Therefore, we examine the changes in particulate matter (PM₂.₅) during LD, and its impact on the global croplands. Our analysis finds that there is a substantial decline in the global PM₂.₅ concentrations during LD (2020) compared to pre-lockdown (PreLD: 2017-2019) in India (10-20%), East China (EC, 10%), Western Europe (WE, 10%) and Nigeria (10%), which are also the cropland dominated regions. Partial correlation analysis reveals that the decline in PM₂.₅ positively affects the cropland greening when the influence of temperature, precipitation and soil moisture are limited. Croplands in India, EC, Nigeria and WE became more greener as a result of the improvement in air quality by the reduction in particulates such as PM₂.₅ during LD, with an increase in the Enhanced Vegetation Index (EVI) of about 0.05-0.1, 0.05, 0.05 and 0.05-0.1, respectively. As a result of cropland greening, increase in the total above ground biomass production (TAGP) and crop yield (TWSO) is also found in EC, India and Europe. In addition, the improvement in PM₂.₅ pollution and associated changes in meteorology also influenced the cropland phenology, where the crop development stage has prolonged in India for wet-rice (1-20%) and maize (1-10%). Therefore, this study sheds light on the response of global croplands to LD-induced improvements in PM₂.₅ pollution. These finding have implications for addressing issues of air pollution, global warming, climate change, environmental conservation and food security to achieve the Sustainable Development Goals (SDGs).

Inoculation and tracking of beneficial microbes reveal they can establish in field-grown potato roots and decrease blemish diseases

In a three-year field study, we inoculated two potato varieties with a selection of four beneficial microbial strains (i.e. Rhizophagus irregularis MUCL41833, Trichoderma asperelloides A, Pseudomonas brassicacearum 3Re2-7 and Paraburkholderia phytofirmans PsJN), alone or in combination. Plants were grown under rainfed or irrigated conditions, and potato yield and development of several diseases were evaluated. The microbial inoculants were traced in the root system at different stages of crop development via molecular markers. Whatever the water supply, the inoculants had no effect on yield. Conversely, some of the inoculants were able to lower the incidence and/or severity of several blemish diseases, namely common scab-associated symptoms (CSAS) and silver scurf/black dot-associated symptoms (SSAS). Microbial consortia were more efficient in decreasing symptoms compared to single strain inoculations. The best control was obtained with the combination of R. irregularis and P. brassicacearum, which reduced the incidence of CSAS by 22% and severity of SSAS by 21%. Root tracking revealed that P. brassicacearum and P. phytofirmans PsJN were able to establish in the root system of the potato, while only P. brassicacearum was detected from emergence until flowering of the plants.

Foliar application of commercial humic substances for possible increase of nutrient status and yield of oilseed rape

Inconsistent results on the effects of humic substances (HS) on yield and nutrient status of important food crops, including oilseeds, confirm the need for further research on different HS products to match their use to actual field conditions. The aim of this study was to evaluate the effect of foliar application of commercially produced peat- and vermicompost-derived HS preparations on nutrient status and yield of spring oilseed rape (Brassica napus). The field experiment was carried out in Stende State Cereals Breeding Institute, Latvia, during the vegetation season of 2012, using the spring oilseed rape cultivar ‘Perfect’. Although foliar sprays of HS were applied during the critical stages of crop development from rapid growth to flowering, they were ineffective in improving the supply of the deficient nutrients (N, K, B, Zn, Cu) in leaves. Moreover, both tested HS products caused a decrease in Cu and B content in the seeds, which resulted in a negative trend in oilseed rape seed yield. Therefore, the conducted experiment demonstrated that foliar application of both HS preparations was ineffective to promote nutrient status and did not contribute to spring oilseed rape yield.

Towards the development of cover crop - reduced tillage systems without herbicides and synthetic fertilizers in onion cultivation: Promising but challenges remain

Towards the development of cover crop - reduced tillage systems without herbicides and synthetic fertilizers in onion cultivation: Promising but challenges remain