Critical Growth Stages Research Articles

Estimating Maize Maturity by Using UAV Multi-Spectral Images Combined with a CCC-Based Model

Measuring maize grain moisture content (GMC) variability at maturity provides an essential piece of information for the formulation of maize harvesting sequences and the applications of precision agriculture. Canopy chlorophyll content (CCC) is an important parameter that describes crop growth, photosynthetic rate, health, and senescence. The main goal of this study was to estimate maize GMC at maturity through CCC retrieved from multi-spectral UAV images using a PROSAIL model inversion and compare its performance with GMC estimation through simple vegetation indices (VIs) approaches. This study was conducted in two separate maize fields of 50.3 and 56 ha located in Hailun County, Heilongjiang Province, China. Each of the fields was cultivated with two maize varieties. One field was used as reference data for constructing the model, and the other field was applied to validate. The leaf chlorophyll content (LCC) and leaf area index (LAI) of maize were collected at three critical stages of crop growth, and meanwhile, the GMC of maize at maturity was also obtained. During the collection of field data, a UAV flight campaign was performed to obtain multi-spectral images from two fields at three main crop growth stages. In order to calibrate and evaluate the PROSAIL model for obtaining maize CCC, crop canopy spectral reflectance was simulated using crop-specific parameters. In addition, various VIs were computed from multi-spectral images to estimate maize GMC at maturity and compare the results with CCC estimations. When the CCC-retrieved results were compared to measured data, the R2 value was 0.704, the RMSE was 34.58 μg/cm2, and the MAE was 26.27 μg/cm2. The estimation accuracy of the maize GMC based on the normalized red edge index (NDRE) was demonstrated to be the greatest among the selected VIs in both fields, with R2 values of 0.6 and 0.619, respectively. Although the VIs of UAV inversion GMC accuracy are lower than those of CCC, their rapid acquisition, high spatial and temporal resolution, suitability for empirical models, and capture of growth differences within the field are still helpful techniques for field-scale crop monitoring. We found that maize varieties are the main reason for the maturity variation of maize under the same geographical and environmental conditions. The method described in this article enables precision agriculture based on UAV remote sensing by giving growers a spatial reference for crop maturity at the field scale.

Field evaluation of haricot bean (Phaseolus vulgaris L.) response to supplemental irrigation in Arba Minch area: Southern Ethiopia

AbstractThis study aimed to determine the impact of supplemental irrigation (SI) on the water use efficiency and yield of haricot beans during the growing seasons from 2020 to 2022. Six treatments were tested, including rain‐fed/no irrigation (T1), full irrigation throughout the season (T2), SI at vegetative, flowering and pod formation (T3), SI at emergence, vegetative and pod formation (T4), SI at emergence, flowering and grain filling (T5) and SI at emergence, pod formation and grain filling (T6), with three replications arranged in a randomized complete block design. The results showed that T2 significantly affected all growth and yield parameters except pod length and harvest index. However, T3 provided better water use efficiency and more acceptable yield reduction than T2. Thus, applying SI during the vegetative, flowering and pod formation phases proves to be more efficient for the production of haricot beans in the area under study as it leads to better economic returns and water productivity. In summary, irrigating the crop during the critical growth stages would result in water conservation and improved water productivity. Further research is recommended on SI to improve water use efficiency across different locations and seasons.

Effect of irrigation scheduling at critical growth stages and fertility levels on growth, yield and quality of summer sesame (Sesamum indicum L.)

A field experiment was conducted with sesame during summer season of 2007 and 2008 on clayey soil ofCotton Research Centre, Khapat farm, Junagadh Agricultural University, Porbandar, Gujarat to find out thecombined effect of irrigation at critical growth stages and fertility levels on growth, yield attributes, yields andquality of sesame. The treatments consisted 24 possible combinations of four levels of irrigation in main plot andsix levels of fertility in sub-plot. The results revealed that irrigating the crop at branching, flowering, capsule andseed development stages with the application of 75:50:50 kg NP2O5K2O/ha were found significant for number ofeffective branches/plant, length of capsule, number of capsules/plant, number of seed/capsule, seed yield/plant, seedyield and stover yields of sesame, harvest index, 1000-seed weight, protein and oil content in seed, oil yield andnitrogen, phosphorus and potassium uptake. Whereas water use efficiency was maximum when the crop wasirrigated at flowering and capsule development stages only.

Assessing the Effect of Irrigation Levels and Hydrogel on Growth and Yield of Wheat (Triticum aestivum L.)

One of the most essential inputs for agriculture is water. Moisture stress at critical growth stages in wheat severely effects the growth and yield. Hydrogel (water-absorbing polymer) can keep the appropriate moisture level at the root zone depth and protects the crop from adverse effect of moisture stress. The present trial was conducted during rabi season of 2020-21 to assess the performance of different hydrogels under different levels of irrigations on growth, yield, and water use efficiency of wheat. Results revealed that application of 3 irrigations recorded significantly maximum number of tillers per m2 at 90 Days After Sowing and at harvest. The application of Nano hydrogel @ 20 kg ha-1 significantly increased the number of tillers per m2 at 90 DAS and at harvest over control. Significantly maximum grain (26.1%) and straw (24.5%) yield were obtained with 3 irrigation levels over one irrigation. The Nano hydrogel increased grain (33.6%) and straw (22.9%) yield significantly over control. Water use efficiency significantly improved with one irrigation over 3 irrigation levels, application of Nano hydrogel @ 20 kg ha-1 significantly increased WUE.

Social Media Affect Adolescence’s Discrimination among Marginalized Peers

The unlimited usage of social media of teenagers is a pervasive problem across the world. Many researchers have found the explicable correlation between social networks and discrimination. However, the effect of social media on marginalized teenagers such as LGBTQ and disabled population is yet under-discussed. Adolescents are at a critical stage of physical and psychological growth and maturity, as well as sexual orientation. The LGBTQ+ community experiences a lot of stigmatization and discrimination, which affects their normal lives. Similarly, people with disabilities are also lagging behind in their development due to inadequate education and support. Hence, the present study aims to provide a holistic review of how social media affects adolescence’s discrimination among their LGBTQ and disabled peers. The conditions and plight of marginalized communities were first presented. Results implicate that social media could benefit marginalized group by offering a collaborative and united group in some extent. However, there are excessive amounts of long-lasting negative comments towards this population. The true mechanism and causation require further researches.

Herbicidal Management of Cuscuta spp. in Berseem (Trifolium alexandrinum L.) Crop

Aims: To correlate the efficiency of herbicides towards control of Dodder (Cuscuta spp.) in green forage and seed yield of berseem crop.
 Study Design: Randomized block design (RBD).
 Place and Duration of Study: Research cum Instructional Farm, Indira Gandhi Krishi Vishwavidyalaya, Raipur (C.G.) during the Rabi season of 2019-20.
 Methodology: The experiment consisted of three replications and ten treatments, in which oxyfluorfen was applied as pre-emergence herbicide and pendimethain was applied as pre-emergence and early post-emegence herbicide while imazethepyr, imazamox 35 % + imazethapyr 35 % (ready mix) and sodium acifluorfen 16.5 % + clodinafop-propargyl 8 % (ready mix) were applied as post-emergence herbicides in different proportion along with control.
 Results: No cuscuta infestation was observed during the experimentation period under any treatments (T1, T2, T3). Highest cuscuta length (41.20 cm), threads (3.00/m2), and dry weight (0.49 g/m2) were recorded just before 1st cutting with treatment (T7). Just before 2nd cutting, the highest cuscuta length (46.32 cm), threads (4.00/m2), and dry weight (0.74 g/m2) were recorded with treatment (T5) and before 3rd cutting, the highest cuscuta length (53.11 cm), threads (5.00/m2), and dry weight (1.06 g/m2) recorded under control (T10). No cuscuta infestation after herbicide application in all treatments. The significantly highest total green forage and seed yield of 60.12 ton/ha and 0.35 ton/ha were recorded under the pre-emergence application of oxyfluorfen 250 g/ha (T3) as compared to the rest of the treatments.
 Conclusion: The selective action of oxyflourfen, pendimethalin, and imazethapyr on berseem and non-selective control of weeds, including cuscuta spp., resulted in minimal weed competition during critical growth stages. This created a favourable environment for berseem, leading to improved growth, yield quality, and overall crop productivity. The effective herbicidal treatments played a vital role in enhancing berseem yield.

Assessing the Spatial–Temporal Pattern of Spring Maize Drought in Northeast China Using an Optimised Remote Sensing Index

Northeast China plays a pivotal role in producing commodity grains. The precipitation and temperature distribution during the growth season is impacted by geographical and climate factors, rendering the region vulnerable to drought. However, relying on a single index does not reflect the severity and extent of drought in different regions. This research utilised the random forest (RF) model for screening remote sensing indices. Relative soil moisture (RSM) was employed to compare seven commonly used indices: the temperature vegetation dryness index (TVDI), vegetation supply water index (VSWI), vegetation condition index (VCI), temperature condition index (TCI), vegetation health index (VHI), multi-band drought index (MBDI), and normalised difference drought index (NDDI). The effectiveness of these indices for monitoring drought during different developmental stages of spring maize was evaluated. Trend rates were employed to investigate the temporal changes in drought patterns of spring maize from 2003 to 2020, and the Sen + Mann–Kendall test was used to analyse spatial variations. The results showed the following: (1) The seven remote sensing indices could accurately track drought during critical growth stages with the TVDI demonstrating higher applicability than the other six indices. (2) The application periods of two TVDIs with different parameters differed for the drought monitoring of spring maize in different developmental periods. The consistency and accuracy of the normalised difference vegetation index (NDVI)-based TVDI (TVDIN) were 5.77% and 34.62% higher than those of the enhanced vegetation index (EVI)-based TVDI (TVDIE), respectively, in the early stage. In contrast, the TVDIE exhibited 13.46% higher consistency than the TVDIN in the middle stage, and the accuracy was the same. During the later stage, the TVDIE showed significantly higher consistency and accuracy than the TVDIN with consistency increases of 9.61% and 38.64%, respectively. (3) The drought trend in northeast China increased from 2003 to 2020, exhibiting severe spring drought and a weakening of the drought in summer. The southern, southwestern, and northwestern parts of northeast China showed an upward drought trend; the drought-affected areas accounted for 37.91% of the study area. This paper identified the most suitable remote sensing indices for monitoring drought in different developmental stages of spring maize. The results provide a comprehensive understanding of the spatial–temporal patterns of drought during the past 18 years. These findings can be used to develop a dynamic agricultural drought monitoring model to ensure food security.

Spectral library of crops and discrimination of major vegetables grown in the eastern Himalayan ecosystem: A proximal hyperspectral remote sensing approach

Identifying, characterising and mapping, unique vegetable crops grown on small mixed lands in the eastern Himalayan mountain ecosystem (EHME) using traditional methods is a challenge. The exploration of high-resolution, multispectral optical satellite data for vegetable mapping is hindered by heavy rainfall, clouds, and year-round fog/snow cover. Hyperspectral imaging is restricted in the region that borders several international countries because of security concerns. In the global literature on spectroscopy, information on vegetable crops cultivated in the Himalayan ecosystem is marginal or never covered. Thus, we used a portable Field Spec Handheld2 (ASD) spectroradiometer for spectral characterization of 28 important vegetables, from 10 different families. During the summer and winter seasons of 2020–2022, they were grown without stress and in good health in the experimental field at Umiam, Meghalaya. The ground radiometer was able to achieve a spectral resolution of <3 nm in the 325 to 1075 nm wavelength range. At critical growth stages, data were acquired on cloudless days in 1100 and 1300 nm, with a height of 1 m above the crop and a field of view (FOV) <25°. Crop-specific sensitive wavebands were identified and estimated 12 vegetation indices (VI) that relate to biophysical and biochemical properties for crop discrimination. The near infrared region (NIR: 700–1070 nm) was effective for crop discrimination. Of the 10 families, the mean NDVI values varied in the following descending order: Umbelliferae(0.878) > Malvaceae(0.863) > Cucurbitaceae(0.802) > Lamiaceae(0.732) > Solanaceae(0.721) > Leguminosae(0.651) > Cruciferae (0.607) > Amarantha ceae(0.604) > Zingiberaceae(0.595) > Liliaceae(0.531). Among vegetables, the highest NDVI was reported for Fenugreek (0.922) and the lowest for Broccoli (0.421). The position on the red edge (700-750 nm) varied greatly. Similarly, crop families had varying VI values for chlorophyll, carotenoid, xanthophyll and anthocyanin pigments. The ranges of these indices were defined as healthy or stressed crops for the region. From the multi-season spectral signature, a spectral crop library (CSL) for all vegetables was developed under clearly defined conditions (e.g. growth stage, time and year, soil, cultivar type, and climate). The CSL generated from the study provides a reference spectral source for spatial mapping and quantitative spectral modelling to assess crop conditions and prescribe measures. This will be a crucial step in promoting precision agriculture in the eastern Himalayas and other similar ecosystems.

Flowering is the critical growth stage for adverse effects of salinity on the grain yield of sunflower

Flowering is the critical growth stage for adverse effects of salinity on the grain yield of sunflower

The Effectiveness of Interpersonal Communication between Parents and Children in Preventing Adolescent Drug Use

The purpose of this study is to determine whether better communication between parents and their teens may prevent the use of narcotics and other illegal drugs because teenagers do not have a defined place; they are no longer classified as children, but they are not yet socially acceptable. To enter the adult category, you must first fill out the form. Teenagers are a transitional stage between children and adults. Adolescence is commonly referred to as the "finding identity" or "hurricane and storm" phase. Teenagers are still unable to master and perform optimally in both physical and psychological capacities. The teenage phase is a critical growth stage in terms of cognitive, emotional, and physical development. Because there is just one variable in this research, the research methodology used is a quantitative descriptive method. Everyone involved in the research process will benefit from the research design. This survey was conducted on communication studies program students from all majors in the class of 2022. Quantitative approaches use concise data (condenser data) to see the broader picture. From all dimensions of interpersonal communication between parents and children known as openness, empathy, supporting attitude, positive attitude, and equality, it is known that respondents agree that effective communication between parents and children can reduce the use of drugs or illicit drugs.fts in the industrial sector can be obtained by protecting industrial design and trademark rights through a constitutive system.

Balancing crop water requirements through supplemental irrigation under rainfed agriculture in a semi-arid environment

Rainfed farming is a dominant agricultural system in Tigray, Ethiopia. However, rainfall is characterized by short duration, intense and erratic subjected to late-onset and early cessation, suggesting a pressing need for Supplemental Irrigation (SI) to fill the crop water demand. Understanding the effects of SI during rainfall late-onset, early cessation, or both, along with their underlying causes, is a critical knowledge gap globally. Although wheat is one of the principal food crops in Tigray, it is subjected to moisture stress during critical growth stages, limiting its potential productivity. Studies specifically related to impacts due to the late-onset of rainfall on wheat are non-existent. Here, we investigated the agrometeorological characteristics of rainfall variability, onset, cessation, and length of the growing season to evaluate the use of SI for balancing the moisture stress in rainfed farming. Meanwhile, using an on-farm experiment, we also evaluated double-season (2017 and 2018) SI application during late-onset (Pre), early cessation (Post), and its combined effects (Pre + Post) on yield and water productivity (WP) of wheat. Yield and WP were significantly (P < 0.05) affected by SI application with higher grain yield (3298 kg/ha) and WP (0.538 kg/m3) obtained from applying Pre + Post. Applying Pre + Post has increased grain yield, biomass, and WP of wheat by 45.6, 27.7, and 21.5% over Rain-fed farming, respectively. Thus, balancing crop water requirements using SI during inadequate rainfall distribution is key for improving WP and wheat production in semi-arid environments. Particularly, the application of SI both during the late-onset and early cessation of rainfall is suggested for greater wheat productivity in semi-arid regions.

Drought Tolerance of Legumes: Physiology and the Role of the Microbiome.

Water scarcity and global warming make drought-tolerant plant species more in-demand than ever. The most drastic damage exerted by drought occurs during the critical growth stages of seed development and reproduction. In the course of their evolution, plants form a variety of drought-tolerance mechanisms, including recruiting beneficial microorganisms. Legumes (one of the three largest groups of higher plants) have unique features and the potential to adapt to abiotic stress. The available literature discusses the genetic (breeding) and physiological aspects of drought tolerance in legumes, neglecting the role of the microbiome. Our review aims to fill this gap: starting with the physiological mechanisms of legume drought adaptation, we describe the symbiotic relationship of the plant host with the microbial community and its role in facing drought. We consider two types of studies related to microbiomes in low-water conditions: comparisons and microbiome engineering (modulation). The first type of research includes diversity shifts and the isolation of microorganisms from the various plant niches to which they belong. The second type focuses on manipulating the plant holobiont through microbiome engineering-a promising biotech strategy to improve the yield and stress-resistance of legumes.

Effect of Seaweed Extracts on Crop Growth and Soil: A Review

Seaweed is the common name for countless species of marine plants and algae that grow in the ocean. The seaweed cultivated by bamboo raft, tube mesh and stone method and harvested generally by acid, alkali and water extraction. However, the mostly commercially available in market, which is extracted by processing of bio refinery method. These seaweeds consist of 80-90% moisture. The dry seaweed extracts contains 50% carbohydrates, protein 10-47% with high proportions of essential amino acids, 1-3% lipids and 7-38% minerals. The essential nutrients contain in seaweeds are nitrogen, potassium, phosphorous, calcium, magnesium, sulphur, iron, sodium, zinc, and copper. Seaweed extracts are available in market viz, Sagarika, Kelp, Sea Secret, Sivarika, Solumax, Biovita etc. The soil application of liquid seaweed extracts through drip @ 0.5-1 L-1 water and foliar @ 2-5 ml L‑1 of water at various critical growth stages of different crops. The application granules of seaweed were recommended @10-20 kg acre-1 during sowing or standing crops. The application of seaweed extracts either through drip or direct application to soil at lower concentrations were positively impact on germination, shoot growth, root growth, nutrient use efficiency, soil properties, soil microbes, biotic stress, abiotic stress and crop yields.

The role of development interventions in enhancing technical efficiency of sunflower producers

A key policy concern in African agriculture is low productivity even when new technologies are generated, disseminated and adopted among farmers. This study used a cross-sectional dataset from a sample of 202 sunflower farmers drawn from northern Uganda to determine technical efficiency and assess the influence of farmer management capabilities, development intervention and socio-economic factors on technical efficiency. Results reveal that technical inefficiency among sunflower farmers account for 81% of farm-level output. Further, farmer management capabilities (p<0.01), participation in development interventions (p<0.01) and reliance on certified seeds (p<0.01) significantly reduce farmer inefficiency and thus, improve technical efficiency. The study highlights that focusing on farmer management capabilities to improve farm efficiency is more cost-saving for realizing economic gains in sunflower production rather than introducing new technologies. We recommend a policy focus on using approaches such as farmer field schools that ensures farmer advisory services at all critical stages of crop growth.

Optimal Preharvest Melatonin Applications to Enhance Endogenous Melatonin Content, Harvest and Postharvest Quality of Japanese Plum

Plum is one of the most produced stone fruits worldwide. Melatonin is an environmentally eco-friendly substance that, in low concentrations, activates defence systems against biotic and abiotic stresses. This substance is considered a tool that could increase fruit quality. This study aimed to evaluate the impact of different preharvest foliar applications with different melatonin concentrations (0.1, 0.3, 0.5 mmol L−1) to enhance melatonin content and shelf life of ‘Primetime’ plum. To this purpose, two and three applications were carried out at different critical stages of fruit growth. Different quality characteristics such as size, colour, titratable acidity, total soluble solids, ripening index, respiration rate, ethylene production rate, anthocyanins and total antioxidant activity, as well as endogenous melatonin content, were tested at harvest and after 40 days of cold storage. Results showed that ‘Primetime’ plums that received 3 applications of 0.5 mmol L−1 enhanced endogenous melatonin content at harvest and showed less softening, delayed darkening, higher anthocyanin concentration and total antioxidant activity after 40 days of storage. Therefore, the concentration of 0.5 mmol L−1 melatonin in 3 applications was effective in improving the quality of ‘Primetime’ plums.

Effect of hierarchy of the production of tillers in wheat (Triticum aestivum L.) cultivar (KM5180) under the influence of planting dates

Sowing dates are an important factor in determining grain yield as they fall within the climatic changes, as the temperature plays an important role in increasing the yield. For the purpose of evaluating the planting dates during the winter season, a field experiment was conducted at Experiments Station, affiliated to the Department of Field Crops/College of Agricultural Engineering Sciences, University of Baghdad during 2020-21. The aim was to study the effect of planting dates on soft wheat cultivar (KM5180). A randomized complete block design (R. C. B. D.) was used with three replications. Sowing dates were used as main parameters viz., November 22nd (D1), December 6th (D2) and December 20th (D3). The results showed the superiority of the sowing date (D1) by giving the highest average grain yield of 5.740 t/ha as a result of the superiority of the plants of this date in the number of spikes/m2 (441.7), the biological yield (17.896 t/ha), the harvest index (32.09%) and the plant height (101.387 cm) and the weight of 1000 grains/g was 39.97 g, significantly superior to dates (D2) and (D3), which gave an average of 4.423 and 2.428 t/ha, 358.3 and 331.7 spikes/m2, 14.556 and 11.353 t/ha, 30.38 and 21.39%, 89.033 and 85.387 cm and 39.11 and 28.28 cm, respectively, The reason for the superiority was due to the appropriate environmental conditions accompanying during the growth period, including the appropriate temperature that gave the products of photosynthesis sufficient to meet the requirements to complete the stages of its growth and form the initiators of its organs in an optimal form, which was positively reflected on the components of the appointment (D1). As for the delay in the dates D2 and D3, the high temperatures coincided with the critical growth stages of plant growth, which reflected negatively on some traits. 7. Sequentially a grain/spike and (11.43, 11.54 and 11.29 cm, respectively. While date D3 was significantly superior in terms of the number of spikelets/spike with an average of (16,800) spikelets/spike over dates D2 and D1, and they gave an average of (16,300 and 16,200) spikelets/spike, respectively. In the light of the obtained results, we conclude that the shares of the total dates (D2) and (D3) reached 54% higher than the contribution of the sowing date (D1), which amounted to 46%, while the late sowing date (D3) achieved half of the share of the share in the grain yield.

Mapping of Soil Properties Using Machine Learning Techniques

We aimed to estimate Soil Nutrients and relate the spectral signatures to that of the Laboratory reference Measurements utilizing CART analysis. Sustainable agriculture aims at controlled and/or precise soil fertility interventions based on spatial soil information. The profound advancements in remote sensing and geospatial techniques provide means for determining the spatial coverage and variability of the soil properties through the survey and image data incorporated in the mapping procedures (i.e.) Digital Soil Mapping. The soil moisture content at varying levels influences crop growth and decides the yield, as the crop requires water at critical crop growth stages. Machine learning techniques provide the means of optimized model calibration when compared to conventional geostatistical or statistical approaches.

Plot level sugarcane yield estimation by machine learning on multispectral images: A case study of Bundaberg, Australia

Early crop yield prediction provides critical information for Precision Agriculture (PA) procedures, policymaking, and food security. The availability of Remote Sensing (RS) datasets and Machine Learning (ML) approaches improved the prediction of sugarcane crop yield on the local and global scales, but an additional effort on the plot scale prediction is required. Challenges for plot-level prediction include a high ratooning capacity of the sugarcane crop, the lack of high spatial resolution data during the critical growth stages, and the non-linear complexation of yield data. The principal objective of the study is to analyse the potential of a time series of high-resolution multispectral Unmanned Aerial Vehicle (UAV) imagery along with three advanced ML techniques, namely Random Forest Regression (RFR), Support Vector Regression (SVR), and Nonlinear Autoregressive Exogenous Artificial Neural Network (NARX ANN) as a solution to the plot-level sugarcane yield prediction. An experimental sugarcane field containing 48 plots was selected, and UAV imagery was collected during the three consecutive cropping seasons' early and middle crop growth stages. Each dataset per growth stage was analyzed separately to predict the sugarcane crop yield in an attempt to discover how early the prediction of pre-harvest yield can be achieved. The datasets of the first two cropping seasons were trained and tested using the three ML techniques, utilizing 10-fold cross-validation to avoid overfitting. The third cropping season dataset was then used to evaluate the reliability of the developed prediction models. The results show that the correlation of Vegetation Indices (VIs) with crop yield in the middle stage outperforms the early stage in all three ML models. Moreover, comparing these models indicates that the NARX ANN method outperformed the others in the middle stage with the highest correlation coefficient (R2) of 0.96 and the lowest Root Mean Square Error (RMSE) of 4.92 t/ha. It was followed by the SVR (R2 = 0.52, RMSE of 14.85 t/ha), which performed similarly to the RFR method (R2 = 0.48, RMSE = 11.20 t/ha). In conclusion, the best-suited model for predicting sugarcane yields during the middle growth stage is a NARX ANN model employing the Normalized Difference RedEdge (NDRE), which demonstrates the feasibility of the ML approaches to predict the plot level sugarcane yield at a specific period of growth as they are less sensitive to the inconsistency of data collection times.

Response of rice resistance based on the validation of rice blast to elevated CO2 concentration and temperature.

Rice (Oryza sativa) resistance is its ability to resist various stresses, the changes of which have important impacts on O. sativa yield security. However, the responses of O. sativa stress resistance to elevated atmospheric CO2 concentration and temperature are poorly understood. We conducted a field open top-chamber experiment with O. sativa (Nanjing 9108 and Jinxiangyu I) based on the CO2 and temperature automatic control platform. The experimental treatments included ambient CO2 concentration and temperature treatment (CK, control), elevated CO2 concentration treatment (C, CO2 concentration increase of 200 μmol·mol-1 above CK), elevated temperature treatment (T, temperature increase of 2 ℃ above CK) and elevated CO2 concentration and temperature (CT, CO2 concentration increase of 200 μmol·mol-1 and temperature increase of 2 ℃ above CK). At the critical growth stages of O. sativa, we measured superoxide dismutase activity, silica content, total flavanol content, malondialdehyde content, soluble sugar content, proline content, and soluble protein content by cutting the uppermost functional leaves. We obtained the rice stress resistance index (RSRI) by principal component analysis to analyze the differences in the composition of stress resistance indicators under different treatments. Considering the disease resistance of O. sativa, the spike neck blast disease was counted to verify the expression level of RSRI for O. sativa stress resistance at maturity stage. Results showed that at the elongation-booting stage, C and CT treatments significantly reduced the RSRI of Jinxiangyu I by 36.5% and 41.1%, respectively, compared with CK. T treatment significantly decreased the RSRI of the two varieties by 44.9% and 33.8%, respectively. The RSRI explained 71.9%-74.3% of the variation in the spike neck blast disease. Overall, the stress resistance of two O. sativa varieties were adversely affected by elevated temperature at the elongation-booting stage. There was an interactive effect of CO2 concentration and temperature on O. sativa stress resistance. Compared with Nanjing 9108, the stress resistance of Jinxiangyu I was more sensitive to elevated CO2 concentration.

Deciphering maize vertical leaf area profiles by fusing spectral imagery data and a bell-shaped function

Leaf area profiles (LAP) represent the green leaf area per unit ground area distributed with the vertical leaf layer, which is a key trait for guiding nutrition diagnosis, crop management and crop breeding. However, passive mono-angle optical sensors don't have direction information on vertical LAP, which makes spectral remote sensing can't capture the canopy-scale vertical leaf area information from top to bottom. To meet this challenge, we present a modeling framework to decipher maize vertical LAP from spectral imagery data. It first employed a hybrid method to derive LAI from the spectral imagery, and then configured a bell-shaped function to decipher the vertical LAP. We conducted a five-year field experiment in critical growth stages to test the ability of the proposed method. Results showed great disagreements between vegetative and reproductive stages. Such differences impacted the leaf area development and the largest leaf layer for LAP modeling. The proposed method considered these two phenological stage to improve the LAP estimation. The performance of this method was assessed by comparing the derived vertical LAP with measurements over different planting years and maize grain production fields. Results showed robust canopy-level modeling for vertical LAP (RMSE = 0.083–0.094 m2/m2). This study highlights that this method extends the ability of passive optical remote sensing to derive vertical information. This method is a valuable and effective remote-sensing approach for deriving vertical LAP over maize canopy scale, also has potential reference value for other vegetation with similar vertical structure.