Stages Of Crop Development Research Articles

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

Ultra-narrow row (UNR) cotton, a production system with rows spaced less than 40 cm apart, has been proposed as a system for earlier maturity without substantial yield loss. However, trials in the U.S.A. and Australia have found maturity benefits difficult to achieve consistently. Studies undertaken in high input cotton systems that compared UNR to conventionally (1 m) spaced cotton found yield differences but failed to demonstrate differences in crop maturity. This paper examines crop development and fruiting dynamics of the two systems in more detail to understand why there were no differences in maturity. Results showed that lack of difference in maturity between the row spacings was not influenced by differences in the time to reach crop development stages nor by lower fruit retention of early bolls in the UNR plants. Node production and fruiting site production on a per plant basis were significantly slower in the UNR plants from early in the growing season. Slower node and fruiting site production delayed maturity in the UNR plants as they set fewer fruit within the same period of time compared to plants grown in conventional spaced rows. The number of fruiting sites produced per plant was highly dependent on the amount of dry matter per plant. Fruiting site production was reduced in the UNR crop because each plant produced less total dry matter, and hence plant development was slower. To further help understand the reasons for outcomes generated in these studies, and how UNR could possibly mature earlier without impacting yield, a conceptual modelling framework was developed to integrate seasonal patterns in fruiting site production, retention, and boll growth. A key component of this framework is translating from a per plant basis to an area basis. The analysis showed that for individual UNR plants to mature earlier and maintain yield on an area basis, early node production and fruiting site production must proceed at a similar rate to conventionally spaced crops. In this scenario 90 % of final yield would be present 14 days earlier in the UNR crop compared to conventionally spaced crops. To realise any benefits of potential earlier maturity further research is needed to explore genetic or management interventions that might avoid the early competitive stress response that slows node development in UNR.

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

Process-based phenological models use thermal requirement (TR) defined by planting date, temperature and photoperiod to predict crop developmental stages. The TR value for a specific developmental stage for a given variety is often presumed to be constant regardless of environmental conditions. We calibrated and compared 12 phenological models using 27-year of observation data of one unique rice (Oryza sativa L.) variety ('Shanyou63') from 46 sites in southern China. Our findings indicated that shifts in environmental conditions significantly affected TR values, e.g., standard deviations of TR at physiological maturation ranged from 83 to 167 °C d. Clustering sites together minimized environmental heterogeneity, and thus minimized the differences in TR for different phenological stages. When the increased from 1 to 24, simulation errors for the 12 models showed a significant decrease across all developmental stages, from 1.8 to 1.4 days for tillering, from 5.3 to 3.7 days for jointing, from 5.6 to 3.9 days for booting, from 4.7 to 3.3 days for heading, and from 6.6 to 3.7 days for physiological maturation. Furthermore, our findings indicate that models featuring a three-segment piecewise linear temperature response function provide a more precise prediction. In contrast, models incorporating a Beta temperature response function have not performed well. This difference is attributed to different mechanisms used to describe the response of crop development rate to temperature, particularly at non-optimum temperatures. The impact of the photoperiod response function on prediction accuracy became significant with the expansion of scale. Our results demonstrate that clustering method effectively compensates for the lack of crop adaptation processes in common phenological models, leading to significantly improved phenological prediction accuracy in regions with environmental diversity.

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

Digestate and its derived composts are valuable organic fertilizers for growing vegetables and field crops. However, the impacts on enzymatic activities and microbial resources acquisition pattern are not well addressed. This study investigated ecoenzymatic activities and microbial resource acquisition patterns following these organic fertilizations for sunflower cultivation. The experiment involved applying digestate, five compost types derived from digestate, and inorganic N fertilizer (NINORG), with a control plot having no treatment. Activities of nine enzymes related to C-, N-, P-, and S- acquiring were measured at three development stages: seedling, pre-blooming, and harvest. Across all treatments, resource allocations, especially for C-, N-, and P- acquisitions, increased with plant development, correlating positively with microbial biomass, soil organic carbon, and soil nutrients contents, showing the heightened energy and nutrient sources stimulated microbial growth, prompting increased resource allocation to meet elevated demands, particularly in later crop developmental stages. Stoichiometric analysis revealed greater investments in P compared to N and C, persisting throughout the study period, with P acquisitions at least four-fold greater than N and at least seven times greater than C. This pattern was greatly impacted at harvest for all treatments except NINORG and control, with a reduced investment rate for P- acquisition compared to that of C-, N-, albeit the C: N:P ratio was still far short of the global mean 1:1:1 ratio. The strong negative correlation between available P and vector angle (p<0.001) indicated that the addition of bioavailable P through organic amendments helped reduce the gap towards the ideal ratio. In summary, on soils with imbalanced microbial nutrient and energy demands, organic amendments intensified microbial growth, resulting in heightened nutrient acquisition. However, the declining trend in P acquisition rates compared to C and N over the cropping period suggests possible adjustments with the help of organic fertilizations. Organic fertilizers, by addressing energy-nutrient imbalances, can contribute to a healthier soil ecosystem under conventional farming systems.

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

Cover crops with reduced tillage technology (CC-RT) can foster soil health and functioning, a crucial agroecological principle in any transition strategy to more sustainable agricultural systems. However, CC-RT commonly strongly relies on herbicides and synthetic fertilizers, and vegetable crop yields are variable and often low. We assessed the effects of two tillage systems (RT and conventional tillage) and the application of native effective microorganisms (NEM) on onion crop growth and development, yield, N-status, weed pressure, and soil physico-chemical and biological quality after a summer CC, without using herbicides or synthetic fertilizers. Using a participatory research strategy, we conducted a two-year experiment at an experimental station and a one-year trial on two commercial farms. Onion yields were generally low (between 10 and 16 Mg ha−1) and lower in 2019 than in 2020, and lower in RT than in CT in 2020. The relatively low yields in 2019 and RT were associated with poor crop growth and development and leaf-N concentrations below the critical threshold in the early stages of crop development. Soil bulk density was not limiting crop growth in any treatment. Soil mineral N was lower in 2019 than in 2020 and did not significantly differ between treatments. Soil biological activity was higher in RT than in CT. Although the CC residue soil cover in the early stages of the onion crop in RT was more than 50%, RT had a higher weed pressure than CT, which was reversed later in the growing season. The NEM application did not significantly affect most crop, weed, and soil variables. In conclusion, a reduced tillage system for onions without herbicides and synthetic fertilizers is within reach. However, further research is needed to manage weeds and soil N supply dynamics to make CC-RT feasible for no or low agrochemical input systems.

Mulching and drip irrigation to mitigate soil salinity on tomato (Lycoperscon esculentum L.) production in coastal area

Bangladesh’s coastal regions are rich in saline water resources. The majority of these resources are still not being used to their full potential. In the southern Bangladeshi region of Patuakhali, research was conducted to investigate the effects of mulching and drip irrigation on tomato yield, quality, and blossom-end rot (BER) at different soil salinity thresholds. There were four distinct treatments applied: T1= drip irrigation with polythene mulch, T2 = drip irrigation with straw mulch, T3 = drip irrigation without mulch, and T4 = standard procedure. While soil salinity was much greater in treatment T3 (1.19–8.42 dS/m) fallowed by T4 (1.23–8.63 dS/m), T1 treatments had the lowest level of salinity and the highest moisture retention during every development stage of the crops, ranging from 1.28–4.29 dS/m. Treatment T3 exhibited the highest soil salinity levels (ranging from 1.19 to 8.42 dS/m), followed by T4 with a range of 1.23 to 8.63 dS/m. In contrast, T1 treatments consistently maintained the lowest salinity levels (ranging from 1.28 to 4.29 dS/m) and the highest moisture retention throughout all stages of crop development. In terms of yield, drip irrigation with no mulch treatment (T3) provided the lowest output (13.37 t/ha), whereas polyethylene mulching treatment (T1) produced the maximum yield (46.04 t/ha). According to the study, conserving moisture in tomato fields and reducing soil salinity may both be achieved with drip irrigation combined with polythene mulch. The research suggests that employing drip irrigation in conjunction with polythene mulch could effectively preserve moisture in tomato fields and concurrently decrease soil salinity.

Evaluation of daily crop reference evapotranspiration and sensitivity analysis of FAO Penman-Monteith equation using ERA5-Land reanalysis database in Sicily, Italy

Crop evapotranspiration (ET) is one of the most important components in many hydrological processes. The crop reference evapotranspiration (ETo) represents the atmospheric water demand in each crop type, development stage, and management practices. The Penman-Monteith equation in the version suggested by the Food and Agriculture Organization (FAO56-PM), is one of the most used methods to estimate ETo. In several regions of the world, meteorological observations are not always available. The most recent reanalysis database ERA5-Land, released in 2019, can be useful to overcome this limit. The database provides, with a spatial grid of 0.1° latitude and 0.1° longitude, several hourly climate data such as air temperature, dew point temperature, solar radiation, and wind speed components all at 2.0 m above the soil surface, except wind speed components at 10 m, useful to apply the FAO56-PM equation. The objective of this research is to assess the quality of ERA5-Land climate variables data to estimate daily ETo in Sicily, Italy. The effect of the weather station’s elevation associated with the statistical indicators was also evaluated to verify how the morphology affects the measurements. Finally, the sensitivity analysis of the FAO56-PM equation was carried out to identify which climate variables have the most influence on the ETo estimation. For the period 2006–2015, the comparison between air temperature, global solar radiation, wind speed, and relative air humidity, measured from 39 ground weather stations in Sicily, and ERA5-Land was carried out and then, through FAO56-PM equation daily ETo values were estimated using both databases. The statistical indicators Root Mean Square Error (RMSE) and Mean Bias Error (MBE) confirm the possibility of considering the ERA5-Land a suitable solution to estimate ETo. The sensitivity analysis showed that good ETo estimation depends mainly on the accuracy of the relative air humidity and air temperature data.

The Roles of Plant-Growth-Promoting Rhizobacteria (PGPR)-Based Biostimulants for Agricultural Production Systems.

The application of biostimulants has been proven to be an advantageous tool and an appropriate form of management towards the effective use of natural resources, food security, and the beneficial effects on plant growth and yield. Plant-growth-promoting rhizobacteria (PGPR) are microbes connected with plant roots that can increase plant growth by different methods such as producing plant hormones and molecules to improve plant growth or providing increased mineral nutrition. They can colonize all ecological niches of roots to all stages of crop development, and they can affect plant growth and development directly by modulating plant hormone levels and enhancing nutrient acquisition such as of potassium, phosphorus, nitrogen, and essential minerals, or indirectly via reducing the inhibitory impacts of different pathogens in the forms of biocontrol parameters. Many plant-associated species such as Pseudomonas, Acinetobacter, Streptomyces, Serratia, Arthrobacter, and Rhodococcus can increase plant growth by improving plant disease resistance, synthesizing growth-stimulating plant hormones, and suppressing pathogenic microorganisms. The application of biostimulants is both an environmentally friendly practice and a promising method that can enhance the sustainability of horticultural and agricultural production systems as well as promote the quantity and quality of foods. They can also reduce the global dependence on hazardous agricultural chemicals. Science Direct, Google Scholar, Springer Link, CAB Direct, Scopus, Springer Link, Taylor and Francis, Web of Science, and Wiley Online Library were checked, and the search was conducted on all manuscript sections in accordance with the terms Acinetobacter, Arthrobacter, Enterobacter, Ochrobactrum, Pseudomonas, Rhodococcus, Serratia, Streptomyces, Biostimulants, Plant growth promoting rhizobactera, and Stenotrophomonas. The aim of this manuscript is to survey the effects of plant-growth-promoting rhizobacteria by presenting case studies and successful paradigms in various agricultural and horticultural crops.

Identification of Single Nucleotide Polymorphic Loci and Candidate Genes for Seed Germination Percentage in Okra under Salt and No-Salt Stresses by Genome-Wide Association Study.

Excessive soil salinity is a major stressor inhibiting crops' growth, development, and yield. Seed germination is a critical stage of crop growth and development, as well as one of the most salt-sensitive stages. Salt stress has a significant inhibitory effect on seed germination. Okra is a nutritious vegetable, but its seed germination percentage (GP) is low, whether under salt stress conditions or suitable conditions. In this study, we used 180 okra accessions and conducted a genome-wide association study (GWAS) on the germination percentage using 20,133,859 single nucleotide polymorphic (SNP) markers under 0 (CK, diluted water), 70 (treatment 1, T1), and 140 mmol/L (treatment 2, T2) NaCl conditions. Using the mixed linear model (MLM) in Efficient Mixed-model Association eXpedated (EMMAX) and Genome-wide Efficient Mixed Model Association (GEMMA) software, 511 SNP loci were significantly associated during germination, of which 167 SNP loci were detected simultaneously by both programs. Among the 167 SNPs, SNP2619493 on chromosome 59 and SNP2692266 on chromosome 44 were detected simultaneously under the CK, T1, and T2 conditions, and were key SNP loci regulating the GP of okra seeds. Linkage disequilibrium block analysis revealed that nsSNP2626294 (C/T) in Ae59G004900 was near SNP2619493, and the amino acid changes caused by nsSNP2626294 led to an increase in the phenotypic values in some okra accessions. There was an nsSNP2688406 (A/G) in Ae44G005470 near SNP2692266, and the amino acid change caused by nsSNP2688406 led to a decrease in phenotypic values in some okra accessions. These results indicate that Ae59G004900 and Ae44G005470 regulate the GP of okra seeds under salt and no-salt stresses. The gene expression analysis further demonstrated these results. The SNP markers and genes that were identified in this study will provide reference for further research on the GP of okra, as well as new genetic markers and candidate genes for cultivating new okra varieties with high GPs under salt and no-salt stress conditions.

Prediction of leaf nitrogen in sugarcane (Saccharum spp.) by Vis-NIR-SWIR spectroradiometry

Nitrogen is one of the essential nutrients for the production of agricultural crops, participating in a complex interaction among soil, plant and the atmosphere. Therefore, its monitoring is important both economically and environmentally. The aim of this work was to estimate the leaf nitrogen contents in sugarcane from hyperspectral reflectance data during different vegetative stages of the plant. The assessments were performed from an experiment designed in completely randomized blocks, with increasing nitrogen doses (0, 60, 120 and 180 kg ha−1). The acquisition of the spectral data occurred at different stages of crop development (67, 99, 144, 164, 200, 228, 255 and 313 days after cutting; DAC). In the laboratory, the hyperspectral responses of the leaves and the Leaf Nitrogen Contents (LNC) were obtained. The hyperspectral data and the LNC values were used to generate spectral models employing the technique of Partial Least Squares Regression (PLSR) Analysis, also with the calculation of the spectral bands of greatest relevance, by the Variable Importance in Projection (VIP). In general, the increase in LNC promoted a smaller reflectance in all wavelengths in the visible (400–680 nm). Acceptable models were obtained (R2 > 0.70 and RMSE <1.41 g kg−1), the most robust of which were those generated from spectra in the visible (400–680 nm) and red-edge (680–750 nm), with values of R2 > 0.81 and RMSE <1.24 g kg−1. An independent validation, leave-one-date-out cross validation (LOOCV), was performed using data from other collections, which confirmed the robustness and the possibility of LNC prediction in new data sets, derived, for instance, from samplings subsequent to the period of study.