Heat unit requirement of wheat (Triticum aestivum L.) under different thermal and moisture regimes

An experiment was conducted during 2016-17 and 2017-18 at WTC, ICAR-IARI New Delhi on wheat crop sown on three dates (15th November, 30th November and 15th December) with five irrigation treatments. The results indicated that the number of days required for attaining different phenological stages decreased with delay in sowing.For all the phenological stages, crop sown on 15th November consumed higher heat units and consequently resulted in higher yield and heat use efficiency (HUE) than that of other sowing. However, higher pheno-thermal index (PTI) values were observed for the late sown crop i.e. 15th December. Further, among the irrigation treatments, five irrigations throughout the growing period showed increase in days to physiological maturity as well as heat units and HUE for grain and biological yield as compared to other treatments. Five irrigations throughout the growing period increased the grain yield by 69 per cent and biological yield by 46 per cent that that of one irrigation at CRI stage. However, there was no significant difference observed between four and five irrigations levels. The heat units or GDD had highly significant correlation with biological yield(r=0.91) as well as with the grain yield (r=0.85).

Effects of Deficit Irrigation Scheduling on Water Use, Gas Exchange, Yield, and Fruit Quality of Date Palm

Water scarcity is very common in the arid region due to the low yearly rainfall. The cost of water for agricultural usage is extremely high in dry locations. Date palm is a high water-demanding tree throughout the year in arid regions. Therefore, the application of deficit irrigation strategies for date palm cultivation may significantly contribute to conserving irrigation water. The present study aimed to assess the effects of controlled deficit irrigation using two modern micro-irrigation systems on water use efficiency (WUE), gas exchange, fruit yield, and quality of date palm (Khalas cv.). The irrigation systems included drip irrigation (DI) and subsurface irrigation (SI) systems. The study was conducted during the 2020 and 2021 seasons at the Date Palm Research Center of Excellence, King Faisal University, Saudi Arabia. The meteorological variables of the study area were real-time monitored using cloud-based IoT (Internet of Things) to calculate the evapotranspiration reference (ETo) and control the irrigation scheduling. Three irrigation treatments (50, 75, and 100% ETc) were applied using DI and SI systems compared with the traditional surface bubbler irrigation (Control). The actual applied water at the deficit irrigation treatments of 50, 75, and 100% ETc were 27.28 ± 0.06, 44.14 ± 1.07, and 55.55 ± 0.37 m3 palm−1, respectively. At all deficit irrigation treatments, the leaf chlorophyll and gas exchange were significantly higher in the SI compared to the DI system. The yield of date palms did not differ significantly between the control and SI systems at both the level of 100 and 75% ETc. The WUE under the SI (1.09 kg m−3) was significantly higher than the DI system (0.52 kg m−3) at the 50% level. There was no significant difference regarding the fruit quality parameters between SI at 50% ETc and control at 100% ETc. Therefore, adopting deficit irrigation strategies using the SI system at 50% ETc level throughout the year could be suggested for date palm irrigation to save water, improve WUE, and maintain fruit quality.

Non-destructive Fluorescence Spectroscopy as a Tool for Discriminating Between Olive Oils According to Agronomic Practices and for Assessing Quality Parameters

A non-destructive fluorescence method combined with chemometric algorithms has been developed for discriminating between olive oils. The excitation-emission matrices (EEMs) of two olive oil varieties (Arbosana and Oliana) from two crop seasons, which had undergone two different irrigation treatments (control irrigation strategy and regulated deficit irrigation (RDI)), were recorded. EEMs were analysed using parallel factor analysis (PARAFAC), followed by linear discriminant analysis (LDA) incorporating three PARAFAC components. This analysis was able to discriminate between olive oils according to crop season (100% of predictions in the validation set were correct) and variety (100% of predictions were correct). Moreover, good discrimination (80% of correct predictions) was also achieved when examining olive oils belonging to the same variety but submitted to two different irrigation treatments. Further, the olive oil quality parameters obtained using conventional methods were compared with those obtained using unfolded partial least squares (U-PLS). Good correlation coefficients were obtained for Rancimat hours (r = 0.87), K270 (r = 0.75) and total polyphenol content (r = 0.94).

Timing of water limitations affects source to sink differences in δ13C composition in apple

Deficit irrigation is used to reduce vegetative vigor, increase fruit quality, and conserve water resources. However, physiological responses to deficit irrigation can vary depending on soil and environmental conditions. Although physiological measurements are often made at single points in time, responses are often longer lasting and a measurement that integrates responses over time would have greater value in assessing the effectiveness of deficit irrigation practices. Carbon isotope composition has long been used as a proxy measurement for water-use efficiency, stomatal conductance, and carbon dioxide exchange with the atmosphere and is heavily influenced by water status. Potentially, fruit, leaves, or other tissues could be used as samples for carbon isotope measurements. However, it is not well known how irrigation practices can influence both source and sink tissue carbon isotope composition in perennial systems. Here, we used two experiments to determine how irrigation timing affects both source and sink δ13C at the end of the season. Irrigation limitations were initiated after bloom for either the whole season or for early, middle, or late season and compared to a well-watered control. For both experiments, leaves were poor indicators of irrigation deficit treatments that were applied during the season. There were no significant differences in leaf δ13C between deficit treatments and the control for both experiments. However, all sink tissues including roots and stems in experiment one for both years and for fruits in experiment two for both years were significantly more enriched compared to the well-watered control. Environmental conditions during the season also appeared to influence the magnitude of difference inδ13Cbetween deficit irrigation treatments and the control. These results indicate that the use of sink tissues are more sensitive for measuring signals associated with in-season water deficits. Carbon isotope composition can be an effective proxy to measure efficacy of irrigation treatments at the physiological level.

Impact of irrigation during flowering and fruit growth on fruit yield and quality of the cactus Opuntia spp.

<p>Most plantations of cactus pear are not irrigated in Morocco and fruits are tiny. The impacts of irrigation during flowering (FLO) and fruit growth (FRG) on fruit yield and quality were monitored along two years on three varieties of cactus pear. In 2011, irrigation treatments were: T1 (0 mm), T2 (30 mm during FLO and 30 mm during FRG) and T3 (30 mm during FRG only). In 2012, irrigation treatments increased to 60 mm during FLO and FRG. The irrigation treatments were applied in 8 (T3) or 16 (T2) watering, once every three days. Interactions between varieties and treatments were significant for fruit yields and for yield components. In 2011, T2 and T3 irrigations had a negative effect on ‘Aissa’ and ‘Moussa’ (-2.8 kg/plant) and T2 had a positive effect on ‘Achefri’ (+2.7 kg/plant). Fruit quality was not affected by irrigation. In 2012, all the varieties responded positively to irrigation: ‘Achefri’ and ‘Aissa’ yielded very significantly more with T3 (plus 63 % and 30 % resp.) and ‘Moussa’ with T2 (+30 %). All irrigations increased fruit number and size. Irrigation had no significant effect on the fruit quality or slightly decreased the content of total sugars and titratable acidity</p>

Assessment of Ensemble Learning to Predict Wheat Grain Yield Based on UAV-Multispectral Reflectance

Grain yield is increasingly affected by climate factors such as drought and heat. To develop resilient and high-yielding cultivars, high-throughput phenotyping (HTP) techniques are essential for precise decisions in wheat breeding. The ability of unmanned aerial vehicle (UAV)-based multispectral imaging and ensemble learning methods to increase the accuracy of grain yield prediction in practical breeding work is evaluated in this study. For this, 211 winter wheat genotypes were planted under full and limited irrigation treatments, and multispectral data were collected at heading, flowering, early grain filling (EGF), and mid-grain filling (MGF) stages. Twenty multispectral vegetation indices (VIs) were estimated, and VIs with heritability greater than 0.5 were selected to evaluate the models across the growth stages under both irrigation treatments. A framework for ensemble learning was developed by combining multiple base models such as random forest (RF), support vector machine (SVM), Gaussian process (GP), and ridge regression (RR). The R2 values between VIs and grain yield between for individual base models were ranged from 0.468 to 0.580 and 0.537 to 0.598 for grain yield prediction in full and limited irrigation treatments across growth stages, respectively. The prediction results of ensemble models were ranged from 0.491 to 0.616 and 0.560 to 0.616 under full and limited irrigation treatments respectively, and were higher than that of the corresponding base learners. Moreover, the grain yield prediction results were observed high at mid grain filling stage under both full (R2 = 0.625) and limited (R2 = 0.628) irrigation treatments through ensemble learning based stacking of four base learners. Further improvements in ensemble learning models can accelerate the use of UAV-based multispectral data for accurate predictions of complex traits like grain yield in wheat.

Effect of Soil Water Availability on Physiological Parameters, Yield, and Seed Quality in Four Quinoa Genotypes (Chenopodium quinoa Willd.)

Drought represents significant environmental stress, and improving agriculture water management and yield is a priority goal. The effect of diminishing soil water content in the grain filling (GF) stage, throughout physiological maturity (GM), on the yield and grain quality, leaf water potential (LWP), and maximum quantum yield (Fv/Fm) in four long photoperiod quinoa genotypes was evaluated in the South-Central zone of Chile, during the 2014–2015 and 2015–2016 seasons. Five irrigation treatments (T) were established. Irrigation was carried out when the available water (AW) of the root zone reached values of 100%, 70%, 40%, 20%, and 0%. The lowest LWP values were obtained by T20 and T0 (−1.95 MPa). The ‘Morado’ genotype reached the lowest LWP at both seasons, while the highest average LWP was achieved by the ‘AG 2010’ (2014–2015) and ‘Cahuil’ genotypes (2015–2016). A global trend of Fv/Fm values was observed from GF to GM: 0.74 toward 0.79 (2014/2015), and 0.74 toward 0.82 (2015/2016). Only during the second season, Fv/Fm showed differences among irrigation treatments. Total average grain yields in the second season (2.97 t ha−1) were greater than those in the first season (1.43 t ha−1). In both seasons, the ‘Cahuil’ genotype and T100 reached the highest yields. A significative decrease in yield was observed when AW diminished. A direct relationship between seed yield and leaf water potential (ΔY/ΔLWP) was found in all genotypes, varying between 5.53 (‘Cahuil’) and 2.86 t ha−1 MPa−1 (‘AG 2010’). Total proteins, albumins, and globulins varied between seasons, with almost no differences among irrigation treatments. Only the ‘Morado’ genotype showed a slight trend to obtain a higher content of total protein in both seasons. It is possible to grow quinoa under irrigation deficit conditions between GF throughout GM, maintaining yield parameters and nutritional quality.