Critical Phenological Stages Research Articles

Assessment of Phenological and Yield Characteristics of Apple (Malus × domestica Borkh) Varieties

Phenology has gained recently a considerable emphasis as a method for assessing the impacts of changing climate on plant species. Shifting in phenological stages changes can have rippling impacts on environment. Quantifying the effects of these shifting changes on plants is therefore crucial. Knowledge of the dates and duration of particular phenological stages and their variations can be a valuable source of information for organizing, planning and implementation of some regular and special horticultural activities. The phenological study of apple is an essential component of the study as critical phenological stages of the apple are highly reliant on environment, they also have significant practical and scientific implications in the domain of selection. In particular, proper flowering and fruit ripening time are very critical to achieve optimum fruit yield and quality. Fruit yield is an important parameter that determines the acceptability of a cultivar by consumers. The present investigation was undertaken to evaluate the apple varieties for their phenology and yield under Kashmir conditions. Four varieties viz., Super Chief, Red Chief, Gala Redlum and Mitch Gala were used for study. Phenological characteristics revealed that Mitch Gala was earliest in all the phenological stages and Super Chief was late. The blooming period of the studied varieties indicated the synchronizing behaviour of the varieties, which is a prerequisite for effective pollination and adequate fruit set. As far as flowering is concerned, Mitch Gala was earliest to bloom and Gala Redlum was late. Fruit production characteristics revealed that Gala Redlum and Mitch Gala. In terms of yield characteristics, highest yield of 20.58 kg/tree was recorded in Gala Redlum followed by 19.40 kg/tree in Mitch Gala.

A deep learning approach for deriving wheat phenology from near-surface RGB image series using spatiotemporal fusion

Accurate monitoring of wheat phenological stages is essential for effective crop management and informed agricultural decision-making. Traditional methods often rely on labour-intensive field surveys, which are prone to subjective bias and limited temporal resolution. To address these challenges, this study explores the potential of near-surface cameras combined with an advanced deep-learning approach to derive wheat phenological stages from high-quality, real-time RGB image series. Three deep learning models based on three different spatiotemporal feature fusion methods, namely sequential fusion, synchronous fusion, and parallel fusion, were constructed and evaluated for deriving wheat phenological stages with these near-surface RGB image series. Moreover, the impact of different image resolutions, capture perspectives, and model training strategies on the performance of deep learning models was also investigated. The results indicate that the model using the sequential fusion method is optimal, with an overall accuracy (OA) of 0.935, a mean absolute error (MAE) of 0.069, F1-score (F1) of 0.936, and kappa coefficients (Kappa) of 0.924 in wheat phenological stages. Besides, the enhanced image resolution of 512 × 512 pixels and a suitable image capture perspective, specifically a sensor viewing angle of 40° to 60° vertically, introduce more effective features for phenological stage detection, thereby enhancing the model’s accuracy. Furthermore, concerning the model training, applying a two-step fine-tuning strategy will also enhance the model’s robustness to random variations in perspective. This research introduces an innovative approach for real-time phenological stage detection and provides a solid foundation for precision agriculture. By accurately deriving critical phenological stages, the methodology developed in this study supports the optimization of crop management practices, which may result in improved resource efficiency and sustainability across diverse agricultural settings. The implications of this work extend beyond wheat, offering a scalable solution that can be adapted to monitor other crops, thereby contributing to more efficient and sustainable agricultural systems.

Validation of Fungicide Spray Strategies and Selection for Fenhexamid Resistance in Botrytis cinerea on Greenhouse-Grown Grapevines.

In this study, in planta assays were conducted to assess the effects of fungicide spray tactics, such as the reduction of the labeled fungicide dose and mixture with a multisite fungicide, on fungicide resistance selection and disease control using Vitis vinifera 'Cabernet Sauvignon' grown in a greenhouse for 2 years. The entire clusters were inoculated with Botrytis cinerea isolates at varying frequencies of fenhexamid resistance, followed by fungicide sprays and disease and fenhexamid resistance investigations at critical phenological stages. Our findings indicate that the lower dose of the at-risk fungicide, fenhexamid, effectively managed fenhexamid resistance and disease as well as the higher, labeled dose. In addition, a mixture with the multisite fungicide captan generally resulted a net-positive effect on both resistance management and disease control.

Phenolic, Amino Acid, Mineral, and Vitamin Contents during Berry Development in ‘Italia’ and ‘Bronx Seedless’ Grape Cultivars

Understanding the variations in amino acids, phenolic compounds, elements, and vitamins between grape varieties is essential for optimizing grape production, fine-tuning dietary recommendations, and harnessing the health potential of grapes. In this regard, this comprehensive study investigated the compositional diversity of two distinct table grape cultivars, ‘Bronx Seedless’ and ‘Italia’, at various critical phenological stages (BBCH-77, -79, -81, -83, -85, and -89). The research findings demonstrated remarkable differences in the concentrations of key nutritional components. Bronx Seedless consistently exhibited higher levels of several amino acids, including glutamate, phenylalanine, and aspartate with concentrations reaching 49.6, 52.7, and 24.8 pmol μL−1, respectively, in contrast to Italia. Regarding phenolic compounds, Italia emerged as the richer source, with concentrations notably higher for compounds such as vanillic acid (18.2 µg g−1 FW) and gallic acid (37.4 µg g−1 FW). Mineral analysis revealed variable concentrations, with Italia grapes containing higher levels of Fe (91.0 mg/kg) compared to Bronx Seedless (87.1 mg/kg); however, Bronx Seedless had slightly elevated levels of K (31,089 mg/kg) compared to Italia (28,184 mg/kg). Concidering vitamins, Italia grapes showcased superior levels of Vitamin B1 (14.1 mg/100 g FW) and Vitamin A (11.0 mg/100 g FW), while Bronx Seedless had higher concentrations of Vitamin B6 (29.5 mg/kg), C (3.9 mg/100 g FW) and Vitamin B2 (36.9 mg/100 g FW). Principal component analysis (PCA) elucidated complex relationships within these components, offering insights into potential correlations and interactions. The heatmap visualization further indicated the concentration gradients across various samples, unveiling the intricate nutritional profiles of these grape cultivars. This research can aid grape growers and consumers in making informed decisions about grape cultivars and their corresponding health advantages.

RESEARCH ASSESSMENT OF THE QUALITY A LEGUMES BY ECONOMIC AND VALUE INDICATORS

The article analyzes global climate changes, which lead to a decrease in the sustainable production of agricultural products, as well as increased attention of the world community to the problems of ensuring food security, which requires innovative approaches to adapting agribusiness to new operating conditions. It has been established that the International Food and Agriculture Organization of the United Nations (FAO) is currently actively implementing a strategic action plan for the adaptation of agriculture to climate change, which is based on the reorientation of agro-food systems in the direction of innovative and mostly carbon-neutral climate-optimized agribusiness. It has been proven that the current trends in increasing prices for mineral fertilizers and energy carriers encourage the search for technologies for the cultivation of leguminous crops, which combine effective agrobiological measures to optimize their nutrition through the use of inoculation, a line of modern microfertilizers and their combinations, taking into account the critical phenological stages of plant development. which ultimately guarantees the realization of both the adaptive potential of the culture and the productive potential of its varieties. It is for these reasons that the scientific substantiation and development of measures to optimize lentil nutrition in the complex of modern agrotechnological methods of its cultivation in the conditions of the right-bank forest-steppe, aimed at ensuring a stable level of productivity with the preservation of quality indicators under the conditions of modern trends towards unstable moisture supply against the background of increasing average daily temperatures, is an urgent task. which requires scientific generalization and solution. The article provides an assessment of the quality of the grain of leguminous crops and highlights their main economic and value indicators, which are regulated by current standards. The assortment of leguminous crops, as well as their importance for the agricultural production of Ukraine, is presented. The issue of harmonization of the main regulatory documents regarding grain and leguminous crops with the requirements of the European Union for a more effective entry of Ukraine into the European and world markets has been studied. Analyzes of certification of grain and leguminous crops were carried out. The validity period of the certificate attesting the sowing quality of seeds has been determined. The concept of quality from the point of view of the consumer of legumes and their processing products is singled out, which is closely related to the aspect of their safety for the consumer's health. The scientific rationale for the development of measures to optimize modern agrotechnological methods of cultivation in the conditions of the right-bank forest-steppe, aimed at ensuring the stability of quality indicators against the background of increasing average daily temperatures, is provided.

Phenological description and thermal time requirement of tamarind (Tamarindus indica) in tropical conditions

AbstractTamarind is one of the highly valued fruit trees of the tropical world. Considering the potential of the crop, a precise standardised description of its growth stages and thermal time requirement were studied to ensure efficient agronomic practices. Phenological growth stages of tamarind are defined according to the BBCH (Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie) scale using a three‐digit numerical system. The phenology of tamarind is characterised by eight principal growth stages: bud growth, leaf growth, shoot growth, inflorescence growth, flower development, fruit growth, fruit maturation and dormancy. Principal growth stages of tamarind are defined and described through 39 secondary growth stages. For each phenological stage, duration and thermal time were also ascertained. It is evident that thermal time for fruit growth and maturation of tamarind is substantially high, whereas inflorescence and leaf require less thermal time to complete their growth. The study could be of great help to tamarind growers and scientists for implementation of efficient agronomic management protocols as well as for assessing the response of critical phenological growth stages to temperature.

Thermal imaging for assessment of maize water stress and yield prediction under drought conditions

AbstractMaize production in Thailand is increasingly suffering from drought periods along the cropping season. This creates the need for rapid and accurate methods to detect crop water stress to prevent yield loss. The study was, therefore, conducted to improve the efficacy of thermal imaging for assessing maize water stress and yield prediction. The experiment was carried out under controlled and field conditions in Phitsanulok, Thailand. Five treatments were applied, including (T1) fully irrigated treatment with 100% of crop water requirement (CWR) as control; (T2) early stress with 50% of CWR from 20 days after sowing (DAS) until anthesis and subsequent rewatering; (T3) sustained deficit at 50% of CWR from 20 DAS until harvest; (T4) late stress with 100% of CWR until anthesis and 50% of CWR after anthesis until harvest; (T5) late stress with 100% of CWR until anthesis and no irrigation after anthesis. Canopy temperature (FLIR), crop growth and soil moisture were measured at 5‐day‐intervals. Under controlled conditions, early water stress significantly reduced maize growth and yield. Water deficit after anthesis had no significant effect. A new combination of wet/dry sponge type reference surfaces was used for the determination of the Crop Water Stress Index (CWSI). There was a strong relationship between CWSI and stomatal conductance (R² = 0.90), with a CWSI of 0.35 being correlated to a 64%‐yield loss. Assessing CWSI at 55 DAS, that is, at tasseling, under greenhouse conditions corresponded best to the final maize yield. This linear regression model validated well in both maize lowland (R² = 0.94) and maize upland fields (R² = 0.97) under the prevailing variety, soil and climate conditions. The results demonstrate that, using improved standardized references and data acquisition protocols, thermal imaging CWSI monitoring according to critical phenological stages enables yield prediction under drought stress.

Coupling solar radiation and cloud cover data for enhanced temperature predictions over topographically complex mountain terrain

AbstractFine‐scale climate information is critical to understand species–climate relationships. It is usually obtained by interpolating meteorological station data or by downscaling coarse‐gridded climate data. In mountain areas, however, the low station density and macroclimate variables used in coarse‐gridded climate products cannot reproduce fine‐scale variations caused by the complex topography and thus result in biased predictions of species responses to climate change. Here, we present an innovative method to estimate daily local air temperature at 100 m spatial resolution in the mountain region of South Tyrol (Central Alps, Italy), called the cloud‐corrected model. We introduce a correction factor that couples solar radiation and cloud cover data to improve air temperature predictions. Results are compared to models that either consider elevation only (lapse‐rate model) or elevation and solar radiation but not cloud cover (clear‐sky model) using a set of independent meteorological stations for validation. Moreover, all models were tested to predict critical phenological stages of the climate‐sensitive species Vitis vinifera. Over the vegetative period, the cloud‐corrected model significantly reduced the mean absolute error of predicted temperature compared with the lapse rate and clear‐sky model by 10 and 23%, respectively, and the bias by 93 and 90%, respectively. Solar radiation and cloud cover both strongly influenced local air temperature and their inclusion in temperature estimates greatly reduced systematic biases and improved predictions of important plant phenological stages by multiple days. Our method therefore provides a new way to include easily accessible topography and climate data into fine‐scale temperature predictions and accounts for important climate forcing factors that otherwise are often neglected. It combines efficiency and accuracy, because it limits data requirements but still operates on an ecologically relevant spatial scale. Thus, our approach offers promising opportunities to improve the understanding of species–climate relationships, especially in regions sensitive to the effects of climate change.

The feasibility of using a low-cost near-infrared, sensitive, consumer-grade digital camera mounted on a commercial UAV to assess Bambara groundnut yield

ABSTRACT Accurate, timely, and non-destructive early crop yield prediction at the field scale is essential in addressing changing crop production challenges and mitigating impacts of climate variability. Unmanned aerial vehicles (UAVs) are increasingly popular in recent years for agricultural remote sensing applications such as crop yield forecasting and precision agriculture (PA). The objective of this study was to evaluate the performance of a low-cost UAV-based remote sensing technology for Bambara groundnut yield prediction. A multirotor UAV equipped with a near-infrared sensitive consumer-grade digital camera was used to collect image data during the 2018 growing season (April to August). Flight missions were carried out six times during critical phenological stages of the life-cycle of the monitored crop. Yield was recorded at harvest. Four vegetation indices (VIs) namely normalized difference vegetation index (NDVI), enhanced vegetation index 2 (EVI2), green normalized difference vegetation index (GNDVI), and simple ratio (SR) generated from the Red-Green-Near Infrared bands were calculated using the georeferenced orthomosaic UAV images. Pearson’s product-moment correlation coefficient (r) and Bland–Altman testing showed a significant agreement between remotely and proximally sensed VIs. Significant and positive correlations were found between the four VIs and yield, with the strongest relationship observed between SR and yield at podfilling stage (r = 0.81, P < 0.01). Multi-temporal accumulative VIs improved yield prediction significantly with the best index being ∑SR and the best interval being from podfilling to maturity (r = 0.88, P < 0.01). The accumulated ∑SR from podfilling to maturity resulted in higher prediction accuracy with a coefficient of determination (R 2) of 0.71, root mean square error (RMSE) of 0.20 and mean absolute percentage error (MAPE) of 14.2% than SR spectral index at a single stage (R 2 = 0.68, RMSE = 0.24, MAPE = 15.1%). Finally, a yield map was generated using the model developed, to better understand the within-field spatial variations of yield for future site-specific or variable-rate application operations.

Impact of high night temperature stress on different growth stages of wheat

Increasing temperature especially the High night temperature (HNT) is a major constriction for sustaining global food production under changing climate scenario. Recent climate change has resulted into events of abrupt night temperature rise either throughout the crop period or at specific growth stage. Historical temperature observations and model projections have predicted a more pronounced increase in night temperature [minimum temperature (Tmin.)] compared to that of day temperatures [maximum temperature (Tmax.)]. Hence, the present study was carried out to identify the most critical growth stage affected by HNT and their effects on physiological, growth and yield traits of wheat. A unique field-based movable temperature controlling system was designed to enhance the night temperature by 5 °C than ambient temperature. The three critical phenological stages of wheat i.e. at GS 15 (before tillering initiation), GS 45 (booting) and GS 75 (grain filling) of Zadok’s scale were separately subjected to HNT treatment. The results reveals that GS 75 (grain filling) was the most sensitive stage affected by HNT, which has recorded remarkable reduction in grain yield (GY), Harvest Index (HI), Thousand grain weight (TGW) and grain weight per spike (GWPS) compared to control treatment. Pearson correlation analysis showed that breeders can choose lines with higher TGW, GWPS and grain number per spike (GNPS) for obtaining better GY under HNT conditions and vegetative/booting stage HNT in field condition may not attribute for any significant yield loss.

The role of phenology in the climate change impacts and adaptation strategies for tree crops: a case study on almond orchards in Southern Europe

The assessment of the impact of climate change on Mediterranean crop systems is affected by a large number of uncertainties. To overcome this issue in an impact and adaptation assessment for tree crops, an experimental dataset containing 556 phenological observations was used to calibrate and validate a modelling framework based on the Dynamic Model and ASYMCUR approach, for assessing the flowering date of common Mediterranean almond cultivars. Data were collected over 12 years for 15 almond cultivars in 5 locations in Andalusia (Southern Spain), covering a wide range of weather conditions. The model performance was good: for late-flowering cultivars Root Mean Square Error (RMSE) was 3.6 days and Nash-Sutcliffe model efficiency (NSE) was 0.89, while for early-flowering cultivars RMSE was 3.4 days and NSE was 0.83.Weather projections from an ensemble of 12 climate model outputs including 3 Representative Concentration Pathways (RCPs) were used with the modelling framework for each almond cultivar to quantify the changes in flowering date and the associated weather conditions during this stage, under the future weather conditions of the Iberian Peninsula (IP). Thus, in future scenarios, depending on location, start of full bloom was delayed (in mild-winter areas) or advanced (in cold-winter areas), with the change in weather conditions during critical phenological stages potentially affecting the yield in different ways, depicting a high spatial variability in the projections within the IP. For this reason, a spatial analysis was applied to demarcate those areas with adverse weather conditions related to flowering stage.In light of our results it is concluded that the identification of impacts and adaptation strategies for Mediterranean agriculture requires a careful prior evaluation of the systems at local scale, due to the marked spatial heterogeneity and the remaining high uncertainties associated with the crop modelling.

Effect of Fungicide Mobility and Application Timing on the Management of Grape Powdery Mildew

Grape powdery mildew (GPM) fungicide programs consist of 5 to 15 applications, depending on region or market, in an attempt to achieve the high fruit quality standards demanded by the market. Understanding how fungicides redistribute and targeting redistributing fungicide to critical crop phenological stages could improve fungicide protection of grape clusters. This study evaluated fungicide redistribution in grapevines from major fungicide groups labeled for GPM control. Translaminar and xylem redistribution was examined by placing fungicide-impregnated filter disks on the adaxial or abaxial leaf surface of detached leaves for 10 min and then incubating for 48 h before inoculating the abaxial surface with conidia. Vapor redistribution used Teflon disks sprayed with fungicides and placed on the abaxial leaf surface of detached leaves 48 h before inoculation. Disease development was rated 10 days later. Translaminar movement through calyptra was tested using flowering potted vines. All fungicides tested redistributed through at least one mechanism. Fungicide timing at critical phenological stages (early, mid, and late bloom) was assessed in small plots of cultivar Pinot noir vines. The application of trifloxystrobin, quinoxyfen, or fluopyram at different bloom stages showed that applications initiated at end of bloom resulted in the lowest berry infection probabilities of 0.073, 0.097, and 0.020, respectively. The results of this study suggest that integrating two carefully timed applications of redistributing fungicides initiated at end of bloom into a fungicide program may be an effective strategy for wine grape growers in western Oregon to produce fruit with low GPM infection.

A renormalized modified normalized burn ratio (RMNBR) index for detecting mature rubber plantations with Landsat-8 OLI in Xishuangbanna, China

ABSTRACTA novel phenology-based algorithm, namely the Landsat-derived renormalized modified normalized burn ratio (RMNBR) index, was developed with Xishuangbanna in southern China, also the second-largest national base of natural rubber production. The RMNBR algorithm included two key steps, namely the modification of NBR (or MNBR) by normalizing near infrared (NIR) and two short-wave infrared (SWIR 1/2) bands of Landsat-8 Operational Land Imager (OLI) and, the re-normalization of MNBR between two critical phenological stages of rubber plantations, i.e. defoliation and foliation. It highlighted the temporal differences of surface reflectance of three non-visible bands (i.e. NIR, SWIR 1 and SWIR 2) in canopy density and moisture content of rubber plantations from defoliation to formation during the dry season. Then, the RMNBR was used to identify mature rubber plantations (pixels) in 2018, by combining two auxiliary masks of Landsat-based forest of the same year and topographically-suitable area of rubber trees cultivation. This makes the discrimination of mature rubber plantation straightforward as it only needs to distinguish mature rubber plantations (RMNBR < 0) from other land cover types (RMNBR > 0) within the two critical periods. The phenology-based RMNBR algorithm greatly enriches the remotely-sensed approaches for rubber plantations mapping at the regional scale.

Prevalence of Botrytis cinerea at different phenological stages of table grapes grown in the northern region of South Africa

Abstract Botrytis cinerea, is one of the major causal agents of postharvest decay in table grapes, contributing to 20% losses world-wide. The aim of this study was to determine the prevalence of B. cinerea at different phenological growth stage (full bloom, pea size and fully mature berries) and the impact of agro-climatic sites on pathogen incidence. Droplet digital polymerase chain reaction (ddPCR) was used simultaneously with culture based methods to detect and quantify B. cinerea. Botrytis cinerea was detected at different phenological stages on asymptomatic grape samples. Prevalence and level of B. cinerea differed between phenological stages and sites. Full bloom stage of site B showed the highest prevalence of B. cinerea (82.9%) compared to site A (33.3%). For the latter phenological stages (pea size and mature stage), site A had the highest prevalence (100% for both), compared to 35.2% and 44.4% observed at site B, respectively. Furthermore, the B. cinerea concentration varied between stages within the two sites. The concentration of B. cinerea at site A showed a threefold increase from pea size (2.67 copies μL−1 of DNA) to mature berry stage (9.16 copies μL−1 of DNA), compared with the decline noted for similar growth stages at site B. Botrytis cinerea inoculum build up on asymptomatic grapes could be effectively monitored as the berry develops at critical phenological stages. Preharvest monitoring of the pathogen can help growers improve well-established cultural and management practices, hence limit the risk of postharvest decay.

Flowering, a Critical Phenological Stage as a Limiting Factor for Almond Native Ecotypes Cultivation in Eastern Morocco

Flowering, a Critical Phenological Stage as a Limiting Factor for Almond Native Ecotypes Cultivation in Eastern Morocco

Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards

Wine grape quality and quantity are affected by vine growing conditions during critical phenological stages. Field observations of vine growth stages are too sparse to fully capture the spatial variability of vine conditions. In addition, traditional grape yield prediction methods are time consuming and require large amount grape samples. Remote sensing data provide detailed spatial and temporal information regarding vine development that is useful for vineyard management. In this study, Landsat surface reflectance products from 2013 and 2014 were used to map satellite-based Normalized Difference Vegetation Index (NDVI) and leaf area index (LAI) over two Vitis vinifera L. cv. Pinot Noir vineyards in California, USA. The spatial correlation between grape yield maps and the interpolated daily time series (LAI and NDVI) was quantified. NDVI and LAI were found to have similar performance as a predictor of spatial yield variability, providing peak correlations of 0.8 at specific times during the growing season, and the timing of this peak correlation differed for the two years of study. In addition, correlations with maximum and seasonal-cumulative vegetation indices were also evaluated, and showed slightly lower correlations with the observed yield maps. Finally, the within-season grape yield predictability was examined using a simple strategy in which the relationship between grape yield and vegetation indices were calibrated with limited ground measurements. This strategy has a strong potential to improve the accuracy and efficiency of yield estimation in comparison with traditional approaches used in the wine grape growing industry.

EFFECTS OF TEMPERATURE, PHOTOPERIOD AND DEFOLIATION ON FLOWERING TIME OF LOTUS TENUIS (FABACEAE) IN BUENOS AIRES, ARGENTINA

SUMMARYThe phenological development of crops from emergence to flowering time is largely controlled by temperature and photoperiod. Flowering time is a critical phenological stage for subsequent reproductive phase. Lotus tenuis management in grasslands, pastures and seed production systems is through defoliation and sowing date; however, yet little is known about their effects on flowering time. The data presented in this study were obtained from experiments conducted with L. tenuis during the years 1989 to 2016 under field conditions. Our objectives were to determine if flowering time (a) is affected by sowing date; (b) can be predicted through equations using temperature and photoperiod and (c) is affected by defoliation applied at vegetative stage. Two defoliation intensities were applied, low (LDI) crop height reduced by 54% compared to pre-defoliation crop height and high (HDI), crop height reduced by 75%. The rate of progress from seedling emergence to flowering time (inverse of time from emergence to first flowering, 1/f) was modulated by temperature, photoperiod and photothermal functions. When L. tenuis sowing was delayed from autumn to spring, time from seedling emergence to first flowering decreased from 260 to 100 days. 1/f was linearly related to average temperature (R²=0.75) and photoperiod (R²=0.85) and both variables (R²=0.92). Defoliation retarded flowering time. Flower and pod growth periods were shorter under defoliation than in control one. Defoliation did not cause abortion of flowers and pods. Flower production was fitted to quadratic function of photoperiod. Flowering peak was approximately within 15.2 h. The prediction of flowering time using thermal, photoperiod and photothermal models can provide information about crop management decisions, such as optimal environmental regimes for crop growth through sowing date.

Assessment of thermal heat requirement, radiation energy, water use efficiency, and yield of mango cv Dashehari using fertigation method

Thermal heat requirement as well as energy components depend on weather parameters. Critical phenological stages was taken into consideration for the thermal heat requirement during 2013-15 under fertigation treatment (25, 50 and 75 per cent of recommended fertilizer dose) in mango cv Dashehari at Lucknow region. Mango productivity varied from 4.54-11.84 t ha-1 across different fertigationregimes as a result of differential thermal heat accumulation. In the treatment 25 per cent RDF, productivity of 5.23, 11.13 and 6.12 t ha-1 was recorded during 2013, 2014 and 2015 while in 50 per cent RDF, 6.23, 11.84 and 7.76 t ha-1 was observed. The maximum yield reduction (37.5, 14 and 26.1%) was found in control plot. A significant variation in radiation energy received during the field experiment was revealed and a range of 8.97 to 16.27 mm day-1 extraterrestrial radiation was observed at different critical phenological stages. The heat use efficiency (HUE) varied between 2.54-6.78 g m-2°Cd-1 across seasons and treatments. Water use efficiency (WUE) of 10.8, 14.1, 13.5 and 11.7 kg fruits m-3 was recorded in different fertigated treatments. The highest reduction in WUE was noted in control (23.9%) followed by 17.2 and 4.8% in 25 and 50 per cent as compared to 75 per cent fertigation regimes.

Temporal Downscaling of Crop Coefficients for Winter Wheat in the North China Plain: A Case Study at the Gucheng Agro-Meteorological Experimental Station

The crop coefficient (Kc) is widely used for operational estimation of actual evapotranspiration (ETa) and crop water requirements. The standard method for obtaining Kc is via a lookup table from FAO-56 (Food and Agriculture Organization of the United Nations Irrigation and Drainage Paper No. 56), which broadly treats Kc as a function of four crop-growing stages. However, the distinctive physiological characteristics of overwintering crops, such as winter wheat (Triticum aestivum L.), which is extensively planted in the North China Plain (NCP), are not addressed in this method. In this study, we propose a stage-wise method that accounts for Kc variations for winter wheat at each critical phenological stage, thereby estimating Kc at finer temporal scales. Compared with the conventional FAO method, the proposed stage-wise method successfully captures the bimodal pattern in Kc time series for winter wheat, which is shown at both ten-day and phenological time scales. In addition, the accuracies of the proposed stage-wise Kc method and the FAO method were evaluated using micro-meteorological measurements of ETa collected at the Gucheng agrometeorological experimental station in the NCP. Using a leave-one-out strategy, the evaluation revealed that the stage-wise method significantly outperformed the FAO method at both daily and critical phenological time scales, with root-mean-square errors in ETa for the stage-wise method and the FAO method being 0.07 mm·day-1 and 0.16 mm·day-1, respectively, at the daily time scale, and 0.01 mm·day-1 and 0.27 mm·day-1 at the critical phenological time scale. Generally, the FAO method underestimates ETa during the initial stage and overestimates ETa during both the development and mid-season stages. It is shown that the proposed stage-wise method is important for the water-stressed NCP where precision irrigation is highly desirable, especially during the critical phenological stages. Results from this study provide insight into accurate estimation of water requirements for winter wheat at phenological time scales.

Rescheduling zinc fertilization and cultivar choice improve zinc sequestration and its bioavailability in wheat grains and flour

Zinc application may produce grains with increased Zn, but it cannot ensure a higher bioavailable Zn in the ultimate food products prepared from the grain. The aim of the present study was to optimize Zn fertilization protocols for different wheat cultivars with a view to achieve a higher enrichment of Zn in grains, and reduced Zn-Fe antagonism and loss of Zn on processing to achieve enhanced bioavailability of Zn in flour. We evaluated the effectiveness of Zn biofortification in wheat using four criteria viz., i) magnitude of Zn sequestration in flat breads of wheat, ii) Fe retention in it, iii) maintenance of a favourable phytic acid: Zn molar ratio in breads and iv)enhanced grain yield. Zinc fertilization protocols involving its application through soil, foliar spraying or both at critical phenological stages were tested with six cultivars. Both the grains and flat breads were analysed for Zn, Fe and phytic acid. Zinc fertilization yielded Zn dense but Fe- and phytic acid-depleted grains. However, 40–75% of Zn enrichment in grains was lost during processing to prepare the breads. Zinc application through soil+foliar at maximum tillering and flowering stages with cultivar UP 262 excelled in effecting not only higher grain yield but also grain Zn biofortification that ensured a net gain in Zn in breads, and its Zn bioavailability through a reduced antagonism with Fe and phytate.