Phenological Stages Research Articles

Sunpheno: A Deep Neural Network for Phenological Classification of Sunflower Images.

Leaf senescence is a complex trait which becomes crucial for grain filling because photoassimilates are translocated to the seeds. Therefore, a correct sync between leaf senescence and phenological stages is necessary to obtain increasing yields. In this study, we evaluated the performance of five deep machine-learning methods for the evaluation of the phenological stages of sunflowers using images taken with cell phones in the field. From the analysis, we found that the method based on the pre-trained network resnet50 outperformed the other methods, both in terms of accuracy and velocity. Finally, the model generated, Sunpheno, was used to evaluate the phenological stages of two contrasting lines, B481_6 and R453, during senescence. We observed clear differences in phenological stages, confirming the results obtained in previous studies. A database with 5000 images was generated and was classified by an expert. This is important to end the subjectivity involved in decision making regarding the progression of this trait in the field and could be correlated with performance and senescence parameters that are highly associated with yield increase.

Classifying Stand Compositions in Clover Grass Based on High-Resolution Multispectral UAV Images

In organic farming, clover is an important basis for green manure in crop rotation systems due to its nitrogen-fixing effect. However, clover is often sown in mixtures with grass to achieve a yield-increasing effect. In order to determine the quantity and distribution of clover and its influence on the subsequent crops, clover plants must be identified at the individual plant level and spatially differentiated from grass plants. In practice, this is usually done by visual estimation or extensive field sampling. High-resolution unmanned aerial vehicles (UAVs) offer a more efficient alternative. In the present study, clover and grass plants were classified based on spectral information from high-resolution UAV multispectral images and texture features using a random forest classifier. Three different timestamps were observed in order to depict the phenological development of clover and grass distributions. To reduce data redundancy and processing time, relevant texture features were selected based on a wrapper analysis and combined with the original bands. Including these texture features, a significant improvement in classification accuracy of up to 8% was achieved compared to a classification based on the original bands only. Depending on the phenological stage observed, this resulted in overall accuracies between 86% and 91%. Subsequently, high-resolution UAV imagery data allow for precise management recommendations for precision agriculture with site-specific fertilization measures.

Arbuscular mycorrhizal fungi affect early phenological stages of three secondary vegetation species in a temperate forest

The relationship between arbuscular mycorrhizal fungi (AMF) and secondary vegetation (SV) species at early phenological stages is critical for the successful establishment of these plants on disturbance sites in temperate forests. The main objective of this research is to evaluate the effect of AMF colonization on the early phenological stages (germination and early growth) of three shrub species present in the SV of a temperate forest in central Mexico. We collected soil from different sites in the Abies religiosa forest in central Mexico. We collected seeds of Acaena elongata, Ageratina glabrata, and Solanum pubigerum. We used a controlled experimental design with pasteurized soil (-AMF treatments) and unpasteurized soil (+ AMF treatments). We monitored germination percentage, growth (shoot and root weight and total biomass), AMF root colonization, and the mycorrhizal response index (MRI) for each plant species. All three species tested benefited by AMF, showing higher germination rates. Shoot and root weight and total biomass were significantly higher in the + AMF treatment. Solanum pubigerum showed greater stem length and Ageratina glabrata showed greater root development due to AMF. Ageratina glabrata and Acaena elongata were the most responsive to AMF as indicated by MRI. This research underscores the critical role of AMF in the early phenological stages of SV and highlights the potential ecological benefits of AMF in supporting plant germination and plant growth. These results suggest that AMF enhance germination and early growth of secondary vegetation species, which can be considered in management plans for forest ecosystems.

Differential Gene Expression in Contrasting Common Bean Cultivars for Drought Tolerance during an Extended Dry Period.

Common beans (Phaseolus vulgaris L.), besides being an important source of nutrients such as iron, magnesium, and protein, are crucial for food security, especially in developing countries. Common bean cultivation areas commonly face production challenges due to drought occurrences, mainly during the reproductive period. Dry spells last approximately 20 days, enough time to compromise production. Hence, it is crucial to understand the genetic and molecular mechanisms that confer drought tolerance to improve common bean cultivars' adaptation to drought. Sixty six RNASeq libraries, generated from tolerant and sensitive cultivars in drought time sourced from the R5 phenological stage at 0 to 20 days of water deficit were sequenced, generated over 1.5 billion reads, that aligned to 62,524 transcripts originating from a reference transcriptome, as well as 6673 transcripts obtained via de novo assembly. Differentially expressed transcripts were functionally annotated, revealing a variety of genes associated with molecular functions such as oxidoreductase and transferase activity, as well as biological processes related to stress response and signaling. The presence of regulatory genes involved in signaling cascades and transcriptional control was also highlighted, for example, LEA proteins and dehydrins associated with dehydration protection, and transcription factors such as WRKY, MYB, and NAC, which modulate plant response to water deficit. Additionally, genes related to membrane and protein protection, as well as water and ion uptake and transport, were identified, including aquaporins, RING-type E3 ubiquitin transferases, antioxidant enzymes such as GSTs and CYPs, and thioredoxins. This study highlights the complexity of plant response to water scarcity, focusing on the functional diversity of the genes involved and their participation in the biological processes essential for plant adaptation to water stress. The identification of regulatory and cell protection genes offers promising prospects for genetic improvement aiming at the production of common bean varieties more resistant to drought. These findings have the potential to drive sustainable agriculture, providing valuable insights to ensure food security in a context of climate change.

Can Multi-Temporal Vegetation Indices and Machine Learning Algorithms Be Used for Estimation of Groundnut Canopy State Variables?

The objective of this research was to assess the feasibility of remote sensing (RS) technology, specifically an unmanned aerial system (UAS), to estimate Bambara groundnut canopy state variables including leaf area index (LAI), canopy chlorophyll content (CCC), aboveground biomass (AGB), and fractional vegetation cover (FVC). RS and ground data were acquired during Malaysia’s 2018/2019 Bambara groundnut growing season at six phenological stages; vegetative, flowering, podding, podfilling, maturity, and senescence. Five vegetation indices (VIs) were determined from the RS data, resulting in single-stage VIs and cumulative VIs (∑VIs). Pearson’s correlation was used to investigate the relationship between canopy state variables and single stage VIs and ∑VIs over several stages. Linear parametric and non-linear non-parametric machine learning (ML) regressions including CatBoost Regressor (CBR), Random Forest Regressor (RFR), AdaBoost Regressor (ABR), Huber Regressor (HR), Multiple Linear Regressor (MLR), Theil-Sen Regressor (TSR), Partial Least Squares Regressor (PLSR), and Ridge Regressor (RR) were used to estimate canopy state variables using VIs/∑VIs as input. The best single-stage correlations between canopy state variables and VIs were observed at flowering (r > 0.50). Moreover, ∑VIs acquired from vegetative to senescence stage had the strongest correlation with all measured canopy state variables (r > 0.70). In estimating AGB, MLR achieved the best testing performance (R2 = 0.77, RMSE = 0.30). For CCC, RFR excelled with R2 of 0.85 and RMSE of 2.88. Most models performed well in FVC estimation with testing R2 of 0.98–0.99 and low RMSE. For LAI, MLR stood out in testing with R2 of 0.74, and RMSE of 0.63. Results demonstrate the UAS-based RS technology potential for estimating Bambara groundnut canopy variables.

Study of essential oils and hydroalcoholic extracts obtained from Clinopodium vimineum (Lamiaceae), according to its different phenological stages

ABSTRACT The Caribbean shrub, Clinopodium vimineum, had its essential oil (EO) and hydroalcoholic extracts analyzed across three growth stages: vegetative, flowering, and post-flowering. The chemical composition of the EOs was analyzed by GC/MS/FID and that of the extracts was studied by UHPLC/ESI-Q-Orbitrap-MS. Twenty six compounds (>0.1%) were detected in the EOs of C. vimineum. p-Menth-3-en-8-ol (35–40%) and pulegone (27–30%) were the major components. Among the hydroalcoholic extracts, hydroxycinnamic acids (18–46 mg/g) and glycosylated flavones (17–19 mg/g) were found. The amounts of rosmarinic acid (20–40 mg/g) in the extracts isolated from the hydrodistillation residual biomass were twice as high as those in the extracts from the fresh plant material. The antioxidant activity of the EOs and extracts was evaluated by the ABTS+• and ORAC assays. No significant differences (p > 0.05) were found in the chemical composition or antioxidant activity of EOs and hydroalcoholic extracts, obtained from plants collected at different phenological stages.

Effect of NPK fertilization and seed rate on barley (Hordeum vulgare) yield, yield component and nitrogen dynamics in semi-arid conditions

Objective: This study aimed to investigate the impact of varying of NPK fertilization doses and seed rates on barley (Hordeum vulgare) yield, yield components, and inorganic nitrogen (NH4-N and NO3-N) dynamics in semi-arid conditions without artificial irrigation. Material and Methods: The field experiment was conducted at the Gobustan Experimental Station from October 2018 to June 2019, utilizing the "Celilabad-19" barley variety. A randomized complete block design with four replications was employed, incorporating various seed rates and NPK fertilizer treatments. Plant and soil samples were collected at different phenological stages, and analyses included grain and straw yield, yield parameters, total N content of plant, and soil NH4-N and NO3-N levels. Results and Discussion: The results the treatment with a seed rate of 140 kg/ha and N60P45K45 fertilizer application consistently demonstrated the highest aboveground biomass, grain, and straw yields. This treatment exhibited optimal N content during the Full Maturity stage of plant. Conclusion: In conclusion, this study has provided insights into optimizing barley cultivation practices in semi-arid climates, such as seed rate and NPK fertilizer dose. With a seed rate of 140 kg/ha and N60P45K45 fertilizer application, the highest yield and performance indicators were achieved in the “Celilabad-19” barley variety.

Quantitative Analysis on Expression of Insecticidal Crystal Proteins in Different Plant Parts of BG-II Cotton Hybrids at Various Phenological Stages

Quantitative Analysis on Expression of Insecticidal Crystal Proteins in Different Plant Parts of BG-II Cotton Hybrids at Various Phenological Stages

A Method for Estimating Soybean Sowing, Beginning Seed, and Harvesting Dates in Brazil Using NDVI-MODIS Data

Brazil, as a global player in soybean production, contributes about 35% to the world’s supply and over half of its agricultural exports. Therefore, reliable information about its development becomes imperative to those who follow the market. Thus, this study estimates three phenological stages of soybean crops (sowing, beginning seed, and harvesting dates), identifying spatial–temporal patterns of soybean phenology using phenological metric extraction techniques from Normalized Difference Vegetation Index (NDVI) time-series data from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Focused on the state of Paraná, this study validates the methodology using reference data from the Department of Rural Economics (DERAL). Subsequently, the model was applied to the major Brazilian soybean area cultivation. The results demonstrate strong agreement between the phenological estimates and reference data, showcasing the reliability of phenological metrics in capturing the stages of the soybean cycle. This study represents the first attempt, to the best of our knowledge, to correlate the vegetative peak of soybeans with the beginning seed stage at a large scale within Brazilian territory. Amidst the urgent need for the accurate estimation of agricultural crop phenological stages, particularly considering extreme weather events threatening global food security, this research emphasizes the continual importance of advancing techniques for soybean monitoring.

Influence of Diverse Growing Environments and Plant Densities on Phenological Development and Agrometeorological Indices of Groundnut (Arachis hypogaea L.) in the Hyper Arid Zone of Rajasthan, India

Groundnut (Arachis hypogaea L.) stands as a significant oilseed crop globally. The growth, development, and productivity of these plants are notably affected by the adverse impacts of global climate change. Therefore, the current study sought to examine how diverse growing environments and planting densities influence the phenological development of groundnut in the hyper-arid zone of Rajasthan, India. A field experiment spanning the kharif seasons of 2017, 2018, and 2019 was conducted at Krishi Vigyan Kendra, Swami Keshwanand Rajasthan Agricultural University, Bikaner, India. The experiment laid out in split-plot design with four replications. The treatments included three main plots for growing environments (sowing on May 15, May 30, and June 15) and three sub-plots for planting densities (1.67 lakh ha-1, 2.50 lakh ha-1 and 3.33 lakh ha-1).. The outcomes of the field experiment indicates that sowing groundnut on May 30 was statistically on par with sowing on June 15 and resulted in higher values of GDD, helio thermal units (HTU), PTI, heat use efficiency (HUE), photothermal use efficiency (PUE), and heliothermal use efficiency, as well as hygrothermal use efficiency (Hg TUE-I and II) at the initiation of flowers and peg formation stages. However, at later growth stages significantly higher values of GDD, HTU, HUE, PUE, HgTUE-I and II were observed with the May 15 sowing. These values gradually decreased with delayed sowing up to May 30 and June 15. Further, increasing the planting density from 1.67 lakh ha-1, 2.50 lakh ha-1 and 3.33 lakh ha-1 significantly enhanced the HTU, HUE, PUE, HgTUE-I and II at various phenological stages of groundnut. Therefore, these findings underscore the significance of precise timing and density control in maximizing groundnut yields under challenging environmental circumstances. By understanding and modifying these variables, farmers can mitigate the adverse effects of climate change and enhance groundnut productivity, especially in extremely arid areas like Rajasthan.

Impact of Split Nitrogen and Potassium Applications on Nutrient Content and Uptake in Wheat (Triticum aestivum L.)

An experiment was conducted during the winter (rabi) seasons of 2020-21 and 2021-22 at Krishi Vigyan Kendra, Budgam- Sher-e-Kashmir University of Agricultural Sciences and Technology, Kashmir, to evaluate the Nutrient Content (Nitrogen and Potassium) and their uptake by Wheat (Triticum aestivum L.) as influenced by split application of Potassium and Nitrogen. The experiment was set up in a split-plot design replicated thrice with Potassium splits as main plot factor and nitrogen splits as sub plot factor which resulted in 15 treatment combinations, viz. of potassium @ 30 kg/ha [K1: 100% as basal dose-(RFP); K2: 50% as basal dose + 50% at active tillering; K3: 25 % as basal dose + 75% at active tillering] and 5 treatments of nitrogen @ 120 kg/ha in split ratios of [N1 : 50% as basal + 25% at jointing + 25% at booting stage (RFP); N2:25% as basal dose + 75% at active tillering; N3 : 25% as basal dose +50% at active tillering + 25% at booting; N4 : 50% as basal + 50% at active tillering; N5 : 0% as basal + 75% at active tillering + 25% at booting]. The results of pooled data (2 yrs.) revealed that nutrient content and uptake (nitrogen and potassium) of wheat crop at various phenological stages were significantly more with the application of Potassium in two equal splits in the ratio of 50:50 as compared to the treatment where potassium was applied in one split as 100% basal dose. With the application of potassium in two equal splits (50:50), increase in Nitrogen, Potassium content(%) their uptake and dry matter accumulation by wheat crop was significantly higher as compared to recommended fertilizer application (K1). Application of nitrogen in three splits with reduced basal dose in the ratio of 25:50:25 resulted in an increase of Nitrogen, Potassium content (%), their uptake and dry matter accumulation at all phenological stages by wheat crop and at physiological maturity there was an increase by 2.40%, 7.4%, 10.17 %, 8.83% in Nitrogen, Potassium content (%), their uptake and dry matter accumulation respectively, as compared to recommended practice.

A unified approach to publish semantic annotations of agricultural documents as knowledge graphs

The research results presented in this paper were obtained as part of the D2KAB project (Data to Knowledge in Agriculture and Biodiversity) which aims to develop semantic web-based tools to describe and make agronomical data actionable and accessible following the FAIR principles. We focus on constructing domain-specific Knowledge Graphs (KGs) from textual data sources, using Natural Language Processing (NLP) techniques to extract and structure relevant entities. Our approach is based on the formalization of a semantic data model using common linked open vocabularies such as the Web Annotation Ontology (OA) and the Provenance Ontology (PROV). The model was developed by formulating motivating scenarios and competency questions from domain experts. This model has been used to construct three different KGs from three distinct corpora: PubMed scientific publications on wheat and rice genetics and phenotyping, and French agricultural alert bulletins. The named entities to be recognized include genes, phenotypes, traits, genetic markers, taxa and phenological stages normalized using semantic resources such as the Wheat Trait and Phenotype Ontology (WTO), the French Crop Usage (FCU) thesaurus and the Plant Phenological Description Ontology (PPDO). Named entities were extracted using different NLP approaches and tools. The relevance of the semantic model was validated by implementing experts questions as SPARQL queries to be answered on the constructed RDF knowledge graphs. Our work demonstrates how domain-specific vocabularies and systematic querying of KGs can reveal hidden interactions and support agronomists in navigating vast amounts of data. The resources and transformation pipelines developed are publicly available in Git repositories.

Regional differences in the impact paths of climate on aboveground biomass in alpine grasslands across the Qinghai-Tibet Plateau

Alpine grasslands on the Qinghai-Tibet Plateau (QTP) play an essential role in water conservation, biodiversity protection and climate feedback, with aboveground biomass (AGB) serving as a crucial indicator of grassland health and functionality. While previous studies have independently explored the phenological differences, cumulative effects, and spatial variability of climatic impacts on biomass/productivity in alpine grasslands, the cascading effects regarding climate and phenology on AGB still present knowledge gaps. Here, using peak AGB measurements, remote sensing and gridded climate data in the QTP alpine grasslands during 2002–2018, we systematically analyzed the impact paths of climatic variables (i.e., cumulative precipitation, CP; growing degree-days, GDD) and phenology-mediated paths (start and peak date of the growing season, SOS and POS) on AGB and their regional differences. During the preseason (pre60) or the growing season (sos-pos), climate primarily directly impacted variations in AGB across different climatic regions, although a phenology-mediated path by which climate indirectly affected AGB existed (i.e., GDDsos-pos → POS → AGB). Three general patterns were revealed: In the plateau temperate arid regions, an increase in CPpre60 significantly promoted AGB (path coefficients w = 0.61–0.71), whereas an increase in GDDpre60 inhibited AGB (w = −0.42 ~ −0.49); In the plateau sub-cold regions, increases in both CPsos-pos and GDDsos-pos significantly promoted AGB, respectively (w = 0.46–0.81 and w = 0.37–0.70); Similarly, in the plateau temperate arid or semi-arid regions, increases in CPsos-pos also significantly promoted the AGB (w = 0.56–0.73). This study highlights that the water and heat accumulation mainly exert direct impacts on alpine grassland AGB across various climatic regions and phenological stages, providing insights into the mechanism driving AGB by climate and phenology during spring and summer.

Projected rising temperatures and vapour pressure deficit threaten Arabica coffee production Tanzania's burgeoning coffee region: Empirical insight from Mbinga district, Tanzania

Tanzania is emerging as a key coffee producer, but climate change poses a serious threat to coffee production in the country. While extensive literature addresses climatic variables’ trends and variability in coffee-growing regions globally, elevation, a crucial factor in coffee cultivation, is often overlooked. This paper aims to fill this knowledge gap by examining the future trends and variability of climatic variables identified in previous studies being crucial for Arabica coffee (rainfall, temperature, and vapor pressure Deficit (VPD)) during different phenological stages of Arabica coffee growth across three elevation zones (lowland, midland, and highland). We study the Mbinga district, now a major Arabica-producing area and the forefront of hopes for expanding production in Tanzania. We used a Multi-Model Ensemble (MME) of two Global Circulation Models (GCMs) to estimate the future trends and variability of rainfall, temperature, and VPD under SSP2-4.5 and SSP5-8.5 scenarios from 2023 to 2064. We analyzed these data using non-parametric trend analysis (Mann-Kendal and Sen's slope test estimation) and standardized anomalies. Our results suggest that both temperatures (minimum and maximum) and VPD during critical flowering and growing seasons are projected to increase significantly over the next 42 years across all elevation zones under both emission scenarios. The mean annual temperatures across elevation zones in Mbinga are projected to surpass the optimal temperature envelope (23 °C) for producing quality Arabica, challenging the notion that the highlands might serve as a refuge for future production. The mean annual VPD in the lowlands are projected to surpass the optimal envelope (0.82 kPa) for Arabica productivity from 2023 to 2064 under both emission scenarios, while midlands are projected to surpass the optimal VPD envelope from mid-2040s onwards. In constrast, the highlands are projected to stay within the optimal VPD envelope for Arabica productivity from 2023 to 2064 under both emission scenarios. Nevertheless, as highlands are projected to get more warmer in the future because of rising temperatures, this hope might not be feasible in the post-2064. To secure Mbinga's future in global coffee value chains, the Tanzanian government and stakeholders must support adaptation measures, enhancing smallholders’ resilience against climate-induced disruptions.

Genetic adaptation of phenological stages in Chinese and European elite soybeans (Glycine max [L.] Merr.) across latitudes in Central Europe

Genetic adaptation of phenological stages in Chinese and European elite soybeans (<scp><i>Glycine max</i></scp> [L.] Merr.) across latitudes in Central Europe

Seasonal Dynamic of NO3− and K+ in a Citrus Crop Irrigated by Different Water Qualities

This study evaluated the effect of (i) irrigation water source: transfer (TW) and reclaimed water (RW), and (ii) crop phenological stage: winter rest (WR), flowering-sprouting (FS), and fruit growth (FG), on NO3− and K+ dynamics in soil and leaf of a citrus crop. The experiment was carried out during the 2018 and 2019 growing seasons on adult ‘Star Ruby’ grapefruit trees (Citrus paradisi Macf.). The concentration of both nutrients was periodically measured in soil and leaf samples and continuously monitored in the soil soluble fraction with nutrient sensors. Moreover, soil NO3− leaching was indirectly estimated by the periodic measurement of the leaf enrichment in 15N isotope (15Nleaf). The two water sources showed a different nutrient loading. Thus, NO3− and K+, were approximately 5 and 7 times higher, respectively, in the RW. Furthermore, the average contents of NO3− and K+ in the soil samples from the RW treatment were 10.1 and 19.7%, respectively, higher than in TW, with the highest soil NO3− leaching observed in RW treatment. In line with this, the mean contents of NO3− and K+ in the leaves from the RW treatment were 106.9 and 30.4% higher than the TW ones. As for the different phenological stages, in the FG stage, the lowest concentrations of NO3− in the soil samples and the highest in the leaf tissue were observed after a high soil leaching event. In this study the nutrient sensors measurements varied according to the dynamic of NO3− and K+ in the soil samples. The use of RW promoted an accumulation of NO3− and K+ in the soil and leaves of grapefruit trees, but also enhanced soil NO3− leaching, indicating that the proper management of this water source is necessary to avoid soil contamination. The mobilization of NO3− and K+ from soil to leaf was the highest in the FG stage, to ensure fruit development and vegetative growth.

The niche through time: Considering phenology and demographic stages in plant distribution models

Abstract Species distribution models (SDMs) are widely used to infer species–environment relationships, predict spatial distributions and characterise species' environmental niches. While the importance of space and spatial scales is widely acknowledged in SDM applications, temporal components of the niche are rarely addressed. We discuss how phenology and demographic stages affect model inference in plant SDMs. Ignoring conspicuousness and timing of phenological stages may bias niche estimates through increased observer bias, while ignoring stand age may bias niche estimates through temporal mismatches with environmental variables, especially during times of rapid global warming. We present different methods to consider phenology and demographic stages in plant SDMs, including the selection of causal, spatiotemporally explicit predictors and the calibration of stage‐specific SDMs. Based on a case study with citizen science data, we illustrate how spatiotemporal SDMs provide deeper insights on the coincidence of range and phenological shifts under climate change. The proliferation of digitally available biodiversity and citizen science data increasingly allows considering time explicitly in SDMs. This offers a more mechanistic understanding of plant distributions, and more robust predictions under global change, especially if the reporting of phenological stages and age is facilitated and promoted by relevant data portals.

Maize production at phenological stages affected by water irrigation stress in dryland conditions

Abstract The growth and yield of maize are significantly influenced by the adequacy of soil moisture during the growth period. Given the uncertain rainfall due to climate change, the availability of water for maize production is becoming increasingly limited, while the demand is rising due to population growth. The extent to which maize yield is affected by soil moisture stress at each phenological stage in dryland areas has not been thoroughly explored. This study aimed to determine the production and biomass of maize at various growth stages due to water stress. Watering using drip irrigation equipped with soil moisture sensors was stopped after the maize plants reached 20, 40, 60 (flowering), 80 days after sowing (DAS), and at harvest. Parameters of biomass, maize yield, and other parameters were determined when the soil’s permanent wilting point reached, and soil samples were taken to determine the moisture content. The results showed that irrigation up to 80 DAS was not significantly different in yield, although it was 30% lower than that irrigation up to harvest. However, irrigation up to 60 DAS reduced the yield by 3.9 times and 4.9 times compared to the yield at irrigation up to 80 DAS (85.897 grams/plant) and at harvest (108.57 grams/plant), respectively. Other parameters, such as dry biomass, cob weight, cob length, and cob diameter followed the yield trend, although their values varied. The dry biomass of maize at irrigation until 20 and 40 DAS were 13.68 grams/plant and 40.05 grams/plant, respectively. The experiment implies that irrigation water should be provided until 80 DAS to minimize significant yield losses of maize in dryland conditions.

Parcel level temporal variance of remotely sensed spectral reflectance predicts plant diversity

Over the last two decades, considerable research has built on remote sensing of spectral diversity to assess plant diversity. The spectral variation hypothesis (SVH) proposes that spatial variation in reflectance data of an area is positively associated with plant diversity. While the SVH has exhibited validity in dense forests, it performs poorly in highly fragmented and temporally dynamic agricultural landscapes covered mainly by grasslands. Such underperformance can be attributed to the mosaic-like spatial structure of human-dominated landscapes with fields in varying phenological and management stages. Therefore, we argued for re-evaluating SVH’s flawed window-based spatial analysis and underutilized temporal component. In particular, we captured the spatial and temporal variation in reflectance and assessed the relationships between spatial and temporal components of spectral diversity and plant diversity at the parcel level as a unit that relates to management patterns. Our investigation spanned three grasslands on two continents covering a wide spectrum of agricultural usage intensities. To calculate different components of spectral diversity, we used multi-temporal spaceborne Sentinel-2 data. We showed that plant diversity was negatively associated with the temporal component of spectral diversity across all sites. In contrast, the spatial component of spectral diversity was related to plant diversity in sites with larger parcels. Our findings highlighted that in agricultural landscapes, the temporal component of spectral diversity drives the spectral diversity-plant diversity associations. Consequently, our results offer a novel perspective for remote sensing of plant diversity globally.

AGRONOMIC EFFICIENCY OF HUMIC AND FULVIC ACIDS, FUNGI, BACTERIA, USED IN THE CULTURE OF THE SECOND SAFRA

The implementation of the research took place in the period corresponding to the second harvest of the 2022/2023 agricultural year, by the company Pesquisa e Consultoria Agronômica, in geographic coordinates located at 28°18'18'' south latitude, 38°15' 21'' longitude west. The research aimed to evaluate, through the application of biological products, whether it would be possible to obtain more vigorous and productive plants, in order to quantify their agronomic efficiency, thus validating the use of new technological alternatives in nutritional management in corn cultivation. The P3707 hybrid was used and the composition of the treatments is T1: Control treatment with Azospirillum brasilense bacteria (AbV5) (0.100 L ha-1) (planting juice); T2: Azospirillum brasilense (AbV5) (0.100 L ha-1) + Trichoderma asperellum BV10 (0.100 L ha-1) + Bacillus amyloliquefaciens (0.200 L ha-1) (planting juice); T3: Azospirillum baldaniorum, Bacillus amyloliquefaciens; Bacillus proteolyticus and Pseudomonas aeruginosa (0.200 kg ha-1) (planting juice); T4: Azospirillum brasilense (AbV5) (0.100 L ha-1) + Humic and fulvic acids, Azospirillum baldaniorum, Bacillus amyloliquefaciens; Bacillus proteolyticus and Pseudomonas aeruginosa (0.200 kg ha-1) (TS); T5: Humic and fulvic acids, Azospirillum brasilense (AbV5) (0.400 L ha-1) + Azospirillum baldaniorum, Bacillus amyloliquefaciens; Bacillus proteolyticus and Pseudomonas aeruginosa (0.400 kg ha-1) (planting juice); T6: Humic and fulvic acids, Azospirillum brasilense (AbV5) (0.100 L ha-1) + Azospirillum baldaniorum, Bacillus amyloliquefaciens; Bacillus proteolyticus and Pseudomonas aeruginosa (0.400 kg ha-1) (TS). The technological variables evaluated include plant population at the phenological stage (R5), plant height (V3 and R1), root diameter (V3 and R1), insertion height of the first ear, number of ears per plant, root volume (V3 and R1 ), fresh mass of roots (V3 and R1), dry mass of roots (V3 and R1), thousand grain weight, kilogram productivity per hectare. The data obtained were subjected to analysis of variance and the means of the treatments compared using the Tukey method of separation of means, using R Core Team (2019) with a significance of 5%. The application of humic acid, fulvic acid, fungi, bacteria in seed treatment provided gains in fresh aerial part mass, grain weight and greater productivity of the corn crop.