Critical Growth Stages Research Articles

An Increase in Solar Radiation in the Late Growth Period of Maize Alleviates the Adverse Effects of Climate Warming on the Growth and Development of Maize

Against the background of long-term climate change, quantifying the response of maize growth and development to climate change during critical growth stages will contribute to future decision-making in maize production. However, there have been few reports on the impact of climate change on maize dry matter accumulation and yield formation using long-term field trial data. In this study, field trial data from 13 agricultural meteorological stations in the Beijing–Tianjin–Hebei region from 1981 to 2017 were analyzed using partial correlation analysis and multiple regression models to investigate the effects of climate change on maize growth and yield. The results showed that the average temperature (Tavg) and accumulated effective precipitation (EP) during the maize growing season increased while the accumulated solar radiation (SRD) decreased from 1981 to 2017. During the seedling stage (GS1, VE-V8) and ear development stage (GS2, V8-R1), Tavg increased by 0.34 °C and 0.36 °C/decade, respectively, and EP increased by 1.83 mm and 3.35 mm/decade, respectively. The significant increase in Tavg during GS1 was the main reason for the inhibitory effect of climate change on maize growth, development, and biomass accumulation. However, the increase in SRD during the grain formation stage (GS3, R1–R3) and grain filling stage (GS4, R3–R6) was favorable for yield formation, increasing the grain number per ear (GN) and grain weight (GW) by 5.00% and 2.84%, respectively. SRD significantly increased after the silk stage, partially offsetting the adverse effects of temperature on maize yield formation, but the final result was a 0.18% and 0.94% reduction in maize plant dry weight (TDW) and grain yield (GY), respectively, due to the combined effects of the three climate factors. Spatially, climate change mainly had a negative impact on maize in the eastern and western parts of the central region of Beijing–Tianjin–Hebei, with a maximum GY reduction of up to 34.06%. The results of this study can provide a scientific basis for future decision-making in maize production against the background of climate change.

Evaluating the Effects of Flooding Stress during Multiple Growth Stages in Soybean

Flooding is becoming an increasing concern for soybean (Glycine max [L.] Merr.) production worldwide due to the sensitivity of most cultivars grown today to flood stress. Flooding can stunt plant growth and limit yield, causing significant economic loss. One sustainable approach to improve performance under flood stress is to develop flood-tolerant soybean cultivars. This study was conducted to evaluate soybean genotypes for the response to flood stress at three critical growth stages of production—germination, early vegetative growth (V1 and V4), and early reproductive growth (R1). The results demonstrated that stress imposed by flooding significantly affected soybean yield for each growth stage studied. The average germination rate over the various treatments ranged from 95% to 46%. Despite the poor germination rates after the extended flood treatments, the flood-tolerant genotypes maintained a germination rate of >80% after 8 h of flooding. The germination rate of the susceptible genotypes was significantly lower, ranging 58–63%. Imposing flood stress at the V1 and V4 growth stage also resulted in significant differences between the tolerant and susceptible genotypes. Genotypes with the highest level of flood tolerance continually outperformed the susceptible genotypes with an average 30% decrease in foliar damage based on visual scoring and a 10% increase in biomass. The yield of the tolerant genotypes was also on average 25% higher compared to the susceptible genotypes. These results suggest that breeding for flood tolerance in soybean can increase resiliency during crucial growth stages and increase yield under flood conditions. In addition, the genotypes developed from this research can be used as breeding stock to further make improvements to flood tolerance in soybean.

Source-Sink Alterations in Rice Fallow Adaptive Blackgram Variety ADT3 for Enhancement of Yield and Quality of Seed

Seed Production of rice fallow blackgram variety, ADT3 has limitations due to inherent issues of soil fertility, climate change and thermo sensitivity results in poor flowering, reduced pod setting and poor seed filling. Macro and micro nutrients application during critical stages of growth were tried to improve the seed yield and quality in rice fallow blackgram variety ADT3. The combined foliar spraying of 0.5% NPK (macro) and 0.2% chelated micro nutrients mixture (Fe 2.5%, Mn 1.0%, Zn 3.0%, Cu 1.0%, Mo 0.1% and B 0.5%), were sprayed on 15th (Vegetative phase), 30th (Reproductive phase), 45th ( Flowering and pod formation phase) days after germination improved the growth and seed parameters viz., plant height, number of branches, number of pods per plant, number of seeds per pod, filled seed % which resulted in increased seed yield in rice fallow blackgram variety, ADT3.
 Response of macro and micro nutrients through foliar mode revealed the lack of soil fertility resulting in poor uptake of macro and micro nutrients. Foliar application induced uniform flowering and synchronous maturity of pods with least shattering which would facilitate mechanical harvesting. Foliar application of macro and micro nutrients phenotypically altered the source-sink which greatly influenced the yield of seed, germination and seedling quality parameters.

Identification of Single-Nucleotide Polymorphisms (SNPs) Associated with Heat Tolerance at the Reproductive Stage in Synthetic Hexaploid Wheats Using GWAS.

The projected rise in global ambient temperature by 3-5 °C by the end of this century, along with unpredicted heat waves during critical crop growth stages, can drastically reduce grain yield and will pose a great food security challenge. It is therefore important to identify wheat genetic resources able to withstand high temperatures, discover genes underpinning resilience to higher temperatures, and deploy such genetic resources in wheat breeding to develop heat-tolerant cultivars. In this study, 180 accessions of synthetic hexaploid wheats (SHWs) were evaluated under normal and late wheat growing seasons (to expose them to higher temperatures) at three locations (Islamabad, Bahawalpur, and Tando Jam), and data were collected on 11 morphological and yield-related traits. The diversity panel was genotyped with a 50 K SNP array to conduct genome-wide association studies (GWASs) for heat tolerance in SHW. A known heat-tolerance locus, TaHST1, was profiled to identify different haplotypes of this locus in SHWs and their association with grain yield and related traits in SHWs. There was a 36% decrease in grain yield (GY), a 23% decrease in thousand-grain weight (TKW), and an 18% decrease in grains per spike (GpS) across three locations in the population due to the heat stress conditions. GWASs identified 143 quantitative trait nucleotides (QTNs) distributed over all 21 chromosomes in the SHWs. Out of these, 52 QTNs were associated with morphological and yield-related traits under heat stress, while 15 of them were pleiotropically associated with multiple traits. The heat shock protein (HSP) framework of the wheat genome was then aligned with the QTNs identified in this study. Seventeen QTNs were in proximity to HSPs on chr2B, chr3D, chr5A, chr5B, chr6D, and chr7D. It is likely that QTNs on the D genome and those in proximity to HSPs may carry novel alleles for heat-tolerance genes. The analysis of TaHST1 indicated that 15 haplotypes were present in the SHWs for this locus, while hap1 showed the highest frequency of 25% (33 SHWs). These haplotypes were significantly associated with yield-related traits in the SHWs. New alleles associated with yield-related traits in SHWs could be an excellent reservoir for breeding deployment.

Analysis of metabolic differences in maize in different growth stages under nitrogen stress based on UPLC-QTOF-MS.

Maize has a high demand for nitrogen during the growth period. The study of metabolic changes in maize can provide a theoretical basis for rational nitrogen nutrition regulation. In order to investigate the changes of different metabolites and their metabolic pathways in maize leaves under nitrogen stress, we used ultra-performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) for metabolomic analysis of maize leaves under different nitrogen treatments at three critical growth stages (V4, V12 and R1) in a pot experiment under natural conditions. The results showed that nitrogen stress significantly affected sugar metabolism and nitrogen metabolism, and affected carbon and nitrogen balance, and the effects of stress on maize leaves metabolism increased with the growth process. Metabolic pathways such as the TCA cycle and starch and sucrose metabolism were mainly affected at the seeding stage (V4). The stress response to nitrogen deficiency also showed significant upregulation of flavonoids such as luteolin and astragalin during the booting stage (V12) and anthesis-silking stage (R1). During R1 stage, the synthesis of tryptophan and phenylalanine and the degradation of lysine were significantly affected. Compared with nitrogen stress, the metabolic synthesis of key amino acids and jasmonic acid were intensified and the TCA cycle was promoted under nitrogen sufficiency conditions. This study initially revealed that the response mechanism of maize to nitrogen stress at the metabolic level.

PHYSIOLOGICAL AND PHENOTYPIC CHARACTERIZATION OF COTTON IN HIGH PLANT DENSITIES UNDER WATER STRESS

The study was conducted to examine the effect of high density planting system (HDPS) and moisture regimes on certain varieties of cotton in tropical monsoon based Southern Telangana Agro-climatic Zone. This study was conducted with an objective to identify the suitable spacings and genotypes for cultivating under HDPS. In the experiment, growth attributes such as plant height, crop growth rate (CGR) and relative growth rate (RGR) were estimated at critical stages of growth and it was found that the plants under wider spacing of 75 x10 cm2 spacing led to better plant height (48.1 cm) and CGR (0.58 g m-2 day-1) at 60-90 DAS. Among the genotypes of cotton studied, WGCV-48 exhibited maximum plant height (52.8 cm), CGR (0.65 g m-2 day-1) and RGR(0.034 g g-1 day-1).

Functional cleft palate surgery.

Cleft lip and palate (CLP) as a dislocation malformation confronts parents with a malformation of their child that could not be more central and visible: the face. In addition to the stigmatizing appearance, however, in cases of a CLP, food intake, physiological breathing, speech and hearing are also affected. In this paper, the principles of morphofunctional surgical reconstruction of the cleft palate are presented. With the closure of the palate, and restoration of the anatomy, a situation is achieved enabling nasal respiration, normal or near normal speech without nasality, improved ventilation of the middle ear, normal oral functions with coordinated interaction of the tongue with the hard and soft palate important for the oral and pharyngeal phases of feeding. With the establishment of physiological function, in the early phases of the infant and toddler, these activities initiate essential growth stimulation, leading to normalisation of facial and cranial growth. If these functional considerations are disregarded during primary closure, lifelong impairment of one or more of the abovementioned processes often follows. In many cases, despite secondary surgery and revision, it might not be possible to correct and achieve the best possible outcomes, especially if critical stages of development and growth have been missed or there has been significant tissue loss due to resection of existing tissue while primary surgery. This paper describes functional surgical methods and reviews long term, over many decades, results of children with cleft palate.

Climate Change Effects on Irrigated Corn Growth in the Lower Mississippi Delta

Abstract In this study, the possible climate change impacts on irrigated corn production in the lower Mississippi delta (LMD) region were analyzed. The observed daily maximum and minimum air temperature, wind speed, relative humidity (RH), and precipitation from 1960 to 2018 were used in the analysis. The length of the growing season, evapotranspiration (ET), and crop yield estimates from the precalibrated Root Zone Water Quality Model (RZWQM) were also used. Trend analyses were performed on growing-season averages for temperature, RH, and wind speed; growing-season totals for precipitation and ET; daily values of minimum and maximum temperature; and averages of RH and wind speed at critical corn growth stages. The last day of spring freezing (LDSF) and days with an average daily air temperature Ta of more than 35°C during corn silking were also included in the analysis. The trend analysis was performed using the modified Mann–Kendall test, Pettitt test, and Sen’s slope at a significance level of p ≤ 0.05. The results from our study pointed out increases in minimum Ta, increases in the number of days with Ta exceeding 35°C during the corn silking stage, increases in RH and decreases in ET, advancement of the LDSF by 2 weeks, and 8% reductions in corn yield that could be attributed to changes in climate. If the observed trends in climate (climate variability and change) and yield reductions continue in the region, it could be challenging to grow the corn crop in the LMD profitably. Significance Statement In recent years, growing corn and soybeans instead of cotton has been gaining popularity in the lower Mississippi delta (LMD) region. Our study is aimed to understand how climate change in the recent past (1960–2018) has affected irrigated corn production in LMD. This will help us to better understand the expected consequences of the future climate. We used observations of daily weather data from 1960 to 2018 and corn yield, water balance, and crop yield results from a computer model designed to simulate corn production. Tools were used to analyze trends and patterns in the data and results. Our analysis pointed out significant changes in weather, water balance, and yield decline for irrigated corn in the LMD.

Leaf wettability and leaf angle affect air-moisture deposition in wheat for self-irrigation

BackgroundClimate change and depleting water sources demand scarce natural water supplies like air moisture to be used as an irrigation water source. Wheat production is threatened by the climate variability and extremes climate events especially heat waves and drought. The present study focused to develop the wheat plant for self-irrigation through optimizing leaf architecture and surface properties for precise irrigation.MethodsThirty-four genotypes were selected from 1796 genotypes with all combinations of leaf angle and leaf rolling. These genotypes were characterized for morpho-physiological traits and soil moisture content at stem-elongation and booting stages. Further, a core set of ten genotypes was evaluated for stem flow efficiency and leaf wettability.ResultsBiplot, heat map, and correlation analysis indicated wide diversity and traits association. The environmental parameters indicated substantial amount of air moisture (> 60% relative humidity) at the critical wheat growth stages. Leaf angle showed negative association with leaf rolling, physiological and yield traits, adaxial and abaxial contact angle while leaf angle showed positive association with the stem flow water. The wettability and air moisture harvesting indicated that the genotypes (coded as 1, 7, and 18) having semi-erect to erect leaf angle, spiral rolling, and hydrophilic leaf surface (<90o) with contact angle hysteresis less than 10o had higher soil moisture content (6-8%) and moisture harvesting efficiency (3.5 ml).ConclusionsThese findings can provide the basis to develop self-irrigating, drought-tolerant wheat cultivars as an adaptation to climate change.

Analysing the potential of polarimetric decomposition parameters of Sentinel–1 dual-pol SAR data for estimation of rice crop biophysical parameters

The potential of dual-polarization Sentinel–1 polarimetric decomposition parameters i.e., entropy, anisotropy and alpha angle, to monitor the biophysical parameters of rice crop namely, fresh biomass, dry biomass, vegetation water content (VWC) and plant height is investigated in this study. Multi-temporal Sentinel–1A dataset during critical growth stages of rice was considered for regression analysis between the polarimetric decomposition parameters and the biophysical parameters using linear and logarithmic models. The best correlations are obtained at early vegetation stages of the crop i.e. between tillering to booting stages. The maximum R2 value obtained is 0.6 for linear regression of entropy and VWC followed by other biophysical parameters. The correlation of polarimetric decomposition parameters with biophysical parameters is not as high as that of VH backscatter coefficient. Multiple regression using various Sentinel–1 parameters yields a better correlation than regression using individual parameters. The maximum R2 value of 0.765 is obtained for the multiple linear regression for VWC. Multiple regression using the backscatter coefficients and polarimetric decomposition parameters together gives a better correlation than that using only the backscatter coefficients. This indicate that the polarimetric decomposition parameters are sensitive to biophysical parameters and can be used as additional parameters along with the backscatter parameters for rice crop monitoring.

Mapping Diverse Paddy Rice Cropping Patterns in South China Using Harmonized Landsat and Sentinel-2 Data

Paddy rice cropping patterns (PRCPs) play important roles in both agroecosystem modeling and food security. Although paddy rice maps have been generated over several regions using satellite observations, few studies have focused on mapping diverse smallholder PRCPs, which include crop rotation and are dominant cropping structures in South China. Here, an approach called the feature selection and hierarchical classification (FSHC) method was proposed to effectively identify paddy rice and its rotation types. Considering the cloudy and rainy weather in South China, a harmonized Landsat and Sentinel-2 (HLS) surface reflectance product was employed to increase high-quality observations. The FSHC method consists of three processes: cropping intensity mapping, feature selection, and decision tree (DT) model development. The FSHC performance was carefully evaluated using crop field samples obtained in 2018 and 2019. Results suggested that the derived cropping intensity map based on the Savitzky–Golay (S-G) filtered normalized difference vegetation index (NDVI) time series was reliable, with an overall accuracy greater than 93%. Additionally, the optimal spectral (i.e., normalized difference water index (NDWI) and land surface water index (LSWI)) and temporal (start-of-season (SOS) date) features for distinguishing different PRCPs were successfully identified, and these features are highly related to the critical growth stage of paddy rice. The developed DT model with three hierarchical levels based on optimal features performed satisfactorily, and the identification accuracy of each PRCP can be achieved approximately 85%. Furthermore, the FSHC method exhibited similar performances when mapping PRCPs in adjacent years. These results demonstrate that the proposed FSHC approach with HLS data can accurately extract diverse PRCPs over fragmented croplands; thus, this approach represents a promising opportunity for generating refined crop type maps.

Evaluation of the Effect of Conventional and Stabilized Nitrogen Fertilizers on the Nutritional Status of Several Maize Cultivars (Zea mays L.) in Critical Growth Stages Using Plant Analysis

The study presents the results of a three year field trial aimed at assessing the nutritional status of maize in critical growth stages by means of a plant analysis in the cultivation of three maize cultivars differing in their agronomic and genetic profile. The main research problem was to demonstrate whether the availability of nitrogen from stabilized fertilizers for “stay-green” maize varieties is consistent with the dynamics of the demand for this component. This is very important from both the economic and agronomic aspect of maize cultivation. The research showed a significant response of the maize cultivars to different nitrogen fertilizer formulations, which was observed in the period from the five-leaf stage to the full flowering stage. The advantage of the fertilizer, UltraGran stabilo, over other nitrogen fertilizers in the BBCH 15 stage was demonstrated only for the cultivar, ES Metronom, which produced a greater aerial mass while maintaining the nitrogen concentration at the level of the other two maize cultivars. The nitrogen and potassium content shaped the kernel weight in the ear in the flowering stage, confirming the importance of the interaction of these two elements in forming this feature of maize as the main predictor of the grain yield. This trait (expressed by the R2 coefficient) manifested each year of the study, but especially in the years with optimal weather patterns (i.e., the first year). The response of the maize cultivars to nitrogen fertilizers, especially the cultivar, ES Metronom, was manifested by an increase in the content of nutrients and chlorophyll in the ear leaf, that is considered a predictive organ for grain yield. The fertilizers, Super N-46 and UltraGran stabilo, had a positive effect on the chlorophyll content (CCI parameter) and increased its efficiency of excitation energy transfer (the F0 parameter).

EVALUATION OF WEED CONTROL PRACTICES ON YIELD RESPONSE OF MAIZE CULTIVARS IN THE RAINFOREST ZONE OF ANAMBRA STATE

Weeds are one of the major problems in crop production system around the world with devastating consequences. Thus, effective weed control method is very important in the management of the competition between maize and weeds at critical growth stages of the crop. An experiment was conducted at Igbariam to determine the response of three selected cultivars of maize (‘Oka Nteje’, ‘Oka Umunya’, ‘Oka awkuzu’) landrace to four different weed control methods during the 2021 cropping season. It was laid out in 3×4 factorial experiment in a Randomized Complete Block Design (RCBD) with three replications. The selected maize cultivars were the main factor while the four different weed control methods [weedy check, hoe weeding (twice), Pre-emergence herbicide (Atrazine 3ml/L of water) and Pre-emergence herbicide + hoe weeding once 4weeks after herbicide application] were the sub factor. Data collected were subjected to analysis of variance (ANOVA) and significant mean were separated using Fisher’s Least Significant Difference (LSD). The highest dehusked weight after drying (0.185 kg/ha) and cob fresh weight (0.381kg/ha) were obtained in ‘Oka Awkuzu’ which showed more prospects in terms of yield parameters while pre-emergence herbicide + hoe weeding once had the best weed control effect followed by hoe weeding twice.

Changes in micronutrient concentrations under minimum tillage and cotton-based crop rotations in irrigated Vertisols

Australian irrigated cotton (Gossypium hirsutum L.) yields are among the highest in the world but may deplete soil nutrient reserves faster than in dryland systems. Little is known about changes in long-term micronutrient (Cu, Fe, Mn, Zn) concentrations in these systems. This study investigated changes in soil micronutrient concentrations over time in two long-term tillage and crop rotation experiments under furrow-irrigated cotton systems and a no-till dryland cropping enterprise. The tillage practices that were investigated were maximum (disc to 0.2 m, chisel ploughing to 0.3 m followed by the construction of beds in 1 m spacings) and minimum (mulching cotton residues, followed by root cutting, incorporation of cotton stalks and bed renovation with a disc-hiller) tillage. Soil samples were analysed for diethylenetriamine penta-acetic acid (DTPA) extractable micronutrients, x-ray fluorescence (XRF) total micronutrients, pH, and soil organic carbon (SOC). Both maximum and minimum tillage influenced topsoil distribution of DTPA-extractable Cu, Zn and Mn, with the greatest changes occurring in Mn concentration. Concentrations of Mn in the topsoil (0–0.15 m) during 2015 were higher than those in the subsoil (0.15–0.6 m) by 74 % with maximum tillage and 159 % with minimum tillage, suggesting greater stratification with the latter (28 mg kg−1 in topsoil vs 11 mg kg−1 in subsoil). Including wheat (Triticum aestivum L.) and maize (Zea mays L.) in cotton rotations increased DTPA-extractable Mn concentration. DTPA-extractable Mn was positively correlated with SOC in two experiments (P < 0.01) and DTPA Fe and pH were negatively correlated (P < 0.01). DTPA Zn concentrations under minimum-till cotton systems were stable over 18 years. DTPA Zn concentrations measured at 0.02 m increments suggested that soil below the fertiliser band depth (< 0.04 m) was potentially responsive to Zn application. Sampling in smaller depth increments in the topsoil (0.02 m increments in 0–0.1 m depth) more accurately identified micronutrient stratification and may improve management decisions when sowing. Our results indicated that managing soil pH and SOC in alkaline Vertisols under irrigated cotton systems was a more practical approach to address micronutrient availability than soil application of micronutrients. Future research should consider the implications of current agronomic practices in cotton production, such as the method and timing of nitrogen application as it influences changes in soil pH, which may impact micronutrient availability at critical crop growth stages.

Role of cluster frontline demonstrations in enhancing production and productivity of chickpea

Cluster front line demonstrations (CFLDs) on chickpea variety ‘Phule Vikram’ were conducted by Krishi Vigyan Kendra, Khamgaon during Rabi 2020 to 2023 in Beed district of Maharashtra. The results revealed that average seed yield of 1803 kg/ha under demonstrated plots as compared to farmer’s practice (1360 kg/ ha). The average yield of chickpea is increased by 33.12% with high yielding varietal intervention. Adoption of scientific package of practices like seed treatment, integrated nutrient management, irrigation at critical growth stage, seed treatment with bio-fertilizers and need based suitable plant protection practices resulted in higher yields. The improved technologies gave higher net returns of ` 44,150, ` 60,765 and ` 65,898 per hectare as against to farmer’s practices i.e., ` 25,200, ` 38,605 and ` 47,625 per hectare during the study period 2020-21, 2021-22 and 2022-23, respectively. This shows the efficiency and effectiveness of the improved technologies as a result of successful technical interventions to enhance the production and productivity of chickpea.

Optimizing rate and time of nitrogen application for reducing nitrogen requirement and increasing productivity of wheat

As per 4Rs nutrient stewardship, the right timing and right rate of nitrogen (N) are two important attentions for profitable and sustainable crop production, particularly in the N deficient soil. The effectiveness of N utilization depends on the appropriate timing and amount of N application as per the crop demands at the critical stages of crop growth. The experiment was carried out at the experimental field of Agrotechnology Discipline, Khulna University, Bangladesh during the winter season of 2019-20 to investigate into the effect of schedule and rates of N application on wheat growth, yield and profitability. The experimental treatments comprised three application schedules (66% at sowing and 34% at the 3-leaf stage; 50% at 3-leaf, 30% at jointing and 20% at the booting stage; 50% at 6-leaf stage, 30% at jointing and 20% at the booting stage) and three nitrogen rates (120, 92 and 80 kg ha-1) with three replications. The yield attributes and yield of wheat were substantially affected by the schedule and rates of N application yet growth parameters were not significant. The highest grain yield, total dry matter, grains spike-1 and 1000-seed weight were achieved from the application of N @ 50% at 3-leaf, 30% at jointing and 20% at booting stages with 120 kg ha-1. Statistically similar results were also attained by the same application schedule with a reduced rat2.e of N (92 kg ha-1) due to proper distribution based on the crop requirement on different growth stages rather than the usual practice with a higher rate of N. The maximum net return (BDT 50687) and benefit-cost ratio (1.62) were found in the application of 92 kg ha-1 N in three splits @ 50% at 3-leaf, 30% at jointing and 20% at booting stages. The findings of this study revealed that the right application schedule reduces 25% N requirement and increases the grain yield of wheat.

On-farm evaluation of UAV-based aerial imagery for season-long weed monitoring under contrasting management and pedoclimatic conditions in wheat

Timely availability of weed infestation maps is a key prerequisite for the implementation of site-specific weed management practices. Low-altitude aerial imagery obtained from unmanned aerial vehicles (UAVs) has shown significant potential for weed detection in crops. However, most studies focused on wide-spaced row crops such as maize and sunflower and evaluated proposed methods on single or few well-characterized experimental sites at specific points in time representing a limited range of application scenarios. This study evaluated the feasibility of weed detection in on-farm wheat fields characterized by a narrow row spacing, throughout the early and late developmental stages using UAV imagery and ground-based high-resolution imagery. Image data was obtained for nine sites, representing a wide range of management and pedoclimatic conditions. These sites can be seen as a representative sample of scenarios that would be encountered in practice. A high within- and across-site as well as temporal variation was observed for weed infestation levels and weed population species composition, highlighting the need for spatially and temporally resolved weed mapping. Image-based classification of vegetation objects as crop or weed plants was achieved with an accuracy of 0.88 and 0.72 in ground-based high-resolution images and UAV-based aerial images captured from an altitude of 10 m, respectively. The accuracy of pixel-wise, vegetation-index-based weed infestation estimation during the late vegetative stages varied strongly across sites. Our results highlight the critical importance of a high ground resolution for weed detection using object-based image analysis during the critical growth stages of wheat and of robust methods that are applicable across a range of scenarios. This suggested that future research aiming at a rapid implementation of site-specific weed management in wheat should focus on the development of ground-based systems. Yet, aerial monitoring of wheat stands during late developmental stages using currently available equipment offers significant potential for reducing weed pressure with site-specific weed control measures in the context of crop rotations.

Estimation of yellow rust severity in wheat using visible and thermal imaging coupled with machine learning models

Reliable and quick estimation of wheat yellow rust (WYR) severity in field is essential to manage the disease and minimize the losses. Field experiments were conducted during 2017–18 and 2018–19 to obtain visible and thermal images of 24 wheat cultivars having different levels of WYR resistance at critical growth stages. Machine learning (ML) models were constructed using the combinations of image indices (IN) and partial least square regression (PLS) scores of image indices with disease severity (DS) and Yeo-Johnson (YJ) transformed values of disease severity. The results revealed that 26 visible and 2 thermal indices considered in this study have significant correlations with WYR. The models performances were evaluated using four possible dataset combinations of (1) disease severity + indices, (2) disease severity + PLS scores of indices, (3) YJ transformed disease severity + indices, and (4) YJ transformed disease severity + PLS scores. Disease severity with image derived indices was found to be the best dataset for the prediction of WYR severity using machine learning models with an R2 and d-index above 0.95 during calibration, while up to 0.67 and 0.87, respectively during validation. Cubist model with disease severity + indices dataset was the best to predict WYR severity, while the Gaussian process regression with YJ transformed disease severity + PLS scores dataset was the poorest predictor. The results obtained in the present study showed the potential of ML models for non-destructive prediction of WYR in field using visible and thermal imaging.

Predicting Wheat Leaf Nitrogen Content by Combining Deep Multitask Learning and a Mechanistic Model Using UAV Hyperspectral Images

Predicting leaf nitrogen content (LNC) using unmanned aerial vehicle (UAV) images is of great significance. Traditional LNC prediction methods based on empirical and mechanistic models have limitations. This study aimed to propose a new LNC prediction method based on combining deep learning methods and mechanistic models. Wheat field experiments were conducted to make plants with different LNC values. The LNC and UAV hyperspectral images were collected during the critical growth stages of wheat. Based on these data, a method combining the deep multitask learning method and the N-based PROSAIL model was proposed and compared with traditional LNC prediction methods, including spectral index (SI), partial least squares regression (PLSR) and artificial neural network (ANN) methods. The results show that the new proposed method obtained the best LNC prediction results, with R2, RMSE and RMSE% values of 0.79, 20.86 μg/cm2 and 18.63%, respectively, during calibration and 0.82, 18.40 μg/cm2 and 16.92%, respectively, during validation. The other methods obtained R2, RMSE and RMSE% values between 0.29 and 0.68, 25.71 and 38.52 μg/cm2 and 22.95 and 34.39%, respectively, during calibration and between 0.43 and 0.74, 22.79 and 33.55 μg/cm2 and 20.96 and 30.86%, respectively, during validation. Thus, this study provides an accurate LNC prediction tool for precise nitrogen (N) management in the field.

Abiotic Stress Management in Fruit Crops: A Review

The current changes in fruit production scenario challenged the quality and quantity of fruits required for the vast population. The abiotic and biotic stresses, encountered at critical growth stages and adversely affect productivity of plants. The abiotic stresses like drought, extremes temperature floods and salinity have emerged as major challenges for production of crops. The different abiotic stressescause morphological, anatomical, physiological and biochemical changes and ultimately impacting the productivity and quality of plants. Timely intervention with appropriate adaptation strategies would help in realizing sustainable yields and further enable to overcome adverse effects of abiotic stresses. Successful cultivation of crops and attaining reasonable yields under abiotic stress situations mainly depends on the available adaptation options. Practices like modification in cultural practices, adopting novel irrigation, choice of tolerant rootstocks, choice of tolerant cultivars/crops and biotechnological approaches are to be implemented for alleviating adverse effects. Though, the productivity of fruit crops remains low in areas experiencing abiotic stresses. Therefore, focus is required for developing integrated location-specific and crop- specific adaptation strategies for various abiotic stresses. The integration of all available adaptation options would be the most effective approach in sustaining the production and productivity of fruit crops under abiotic stresses.