Upregulation of TaHSP90A transcripts enhances heat tolerance and increases grain yield in wheat under changing climate conditions.

Plants have certain adaptation mechanisms to combat temperature extremes and fluctuations. The heat shock protein (HSP90A) plays a crucial role in plant defence mechanisms under heat stress. In silico analysis of the eight TaHSP90A transcripts showed diverse structural patterns in terms of intron/exons, domains, motifs and cis elements in the promoter region in wheat. These regions contained cis elements related to hormones, biotic and abiotic stress and development. To validate these findings, two contrasting wheat genotypes E-01 (thermo-tolerant) and SHP-52 (thermo-sensitive) were used to evaluate the expression pattern of three transcripts TraesCS2A02G033700.1, TraesCS5B02G258900.3 and TraesCS5D02G268000.2 in five different tissues at five different temperature regimes. Expression of TraesCS2A02G033700.1 was upregulated (2-fold) in flag leaf tissue after 1 and 4h of heat treatment in E-01. In contrast, SHP-52 showed downregulated expression after 1h of heat treatment. Additionally, it was shown that under heat stress, the increased expression of TaHSP90A led to an increase in grain production. As the molecular mechanism of genes involved in heat tolerance at the reproductive stage is mostly unknown, these results provide new insights into the role of TaHSP90A transcripts in developing phenotypic plasticity in wheat to develop heat-tolerant cultivars under the current changing climate scenario.

Impact analysis of moisture stress on growth and yield of cotton using DSSAT-CROPGRO-cotton model under semi-arid climate.

Adequate soil moisture around the root zone of the crops is essential for optimal plant growth and productivity throughout the crop season, whereas excessive as well as deficient moisture is usually detrimental. A field experiment was conducted on cotton (Gossipium hirsuttum) with three water regimes (viz. well-watered (control); rainfed after one post-sowing irrigation (1-POSI) and rainfed after two post-sowing irrigations (2-POSI)) in main plots and application of eight osmoprotectants in sub plots of Split plot design to quantify the loss of seed cotton yield (SCY) under high and mild moisture stress. The DSSAT-CROPGRO-cotton model was calibrated to validate the response of cotton crop to water stress. Results elucidated that in comparison of well watered (control) crop, 1-POSI and 2-POSI reduced plant height by 13.5-28.4% and lower leaf area index (LAI) by 21.6-37.6%. Pooled analysis revealed that SCY under control was higher by 1,127 kg ha-1 over 1-POSI and 597 kg ha-1 than 2-POSI. The DSSAT-CROPGRO-cotton model fairly simulated the cotton yield as evidenced by good accuracy (d-stat ≥ 0.92) along with lower root mean square error (RMSE) of ≤183.2 kg ha-1; mean absolute percent error (MAPE) ≤6.5% under different irrigation levels. Similarly, simulated and observed biomass also exhibited good agreement with ≥0.98 d-stat; ≤533.7 kg ha-1 RMSE; and ≤4.6% MAPE. The model accurately simulated the periodical LAI, biomass and soil water dynamics as affected by varying water regimes in conformity with periodical observations. Both the experimental and the simulated results confirmed the decline of SCY with any degree of water stress. Thus, a well calibrated DSSAT-CROPGRO-cotton model may be successfully used for estimating the crop performance under varying hydro-climatic conditions.

The use of Multispectral Radio-Meter (MSR5) data for wheat crop genotypes identification using machine learning models.

Satellite remote sensing is widely being used by the researchers and geospatial scientists due to its free data access for land observation and agricultural activities monitoring. The world is suffering from food shortages due to the dramatic increase in population and climate change. Various crop genotypes can survive in harsh climatic conditions and give more production with less disease infection. Remote sensing can play an essential role in crop genotype identification using computer vision. In many studies, different objects, crops, and land cover classification is done successfully, while crop genotypes classification is still a gray area. Despite the importance of genotype identification for production planning, a significant method has yet to be developed to detect the genotypes varieties of crop yield using multispectral radiometer data. In this study, three genotypes of wheat crop (Aas-'2011', 'Miraj-'08', and 'Punjnad-1) fields are prepared for the investigation of multispectral radio meter band properties. Temporal data (every 15 days from the height of 10 feet covering 5 feet in the circle in one scan) is collected using an efficient multispectral Radio Meter (MSR5 five bands). Two hundred yield samples of each wheat genotype are acquired and manually labeled accordingly for the training of supervised machine learning models. To find the strength of features (five bands), Principle Component Analysis (PCA), Linear Discriminant Analysis (LDA), and Nonlinear Discernment Analysis (NDA) are performed besides the machine learning models of the Extra Tree Classifier (ETC), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), Logistic Regression (LR), k Nearest Neighbor (KNN) and Artificial Neural Network (ANN) with detailed of configuration settings. ANN and random forest algorithm have achieved approximately maximum accuracy of 97% and 96% on the test dataset. It is recommended that digital policymakers from the agriculture department can use ANN and RF to identify the different genotypes at farmer's fields and research centers. These findings can be used for precision identification and management of the crop specific genotypes for optimized resource use efficiency.

Prioritizing strategies for wheat biofortification: Inspiration from underutilized species.

The relationship between malnutrition and climate change is still poorly understood but a comprehensive knowledge of their interactions is needed to address the global public health agenda. Limited studies have been conducted to propose robust and economic-friendly strategies to augment the food basket with underutilized species and biofortify the staples for nutritional security. Sea-buckthorn is a known "superfood" rich in vitamin C and iron content. It is found naturally in northern hemispherical temperate Eurasia and can be utilized as a model species for genetic biofortification in cash crops like wheat. This review focuses on the impacts of climate change on inorganic (iron, zinc) and organic (vitamin C) micronutrient malnutrition employing wheat as highly domesticated crop and processed food commodity. As iron and zinc are particularly stored in the outer aleurone and endosperm layers, they are prone to processing losses. Moreover, only 5% Fe and 25% Zn are bioavailable once consumed calling to enhance the bioavailability of these micronutrients. Vitamin C converts non-available iron (Fe3+) to available form (Fe2+) and helps in the synthesis of ferritin while protecting it from degradation at the same time. Similarly, reduced phytic acid content also enhances its bioavailability. This relation urges scientists to look for a common mechanism and genes underlying biosynthesis of vitamin C and uptake of Fe/Zn to biofortify these micronutrients concurrently. The study proposes to scale up the biofortification breeding strategies by focusing on all dimensions i.e., increasing micronutrient content and boosters (vitamin C) and simultaneously reducing anti-nutritional compounds (phytic acid). Mutually, this review identified that genes from the Aldo-keto reductase family are involved both in Fe/Zn uptake and vitamin C biosynthesis and can potentially be targeted for genetic biofortification in crop plants.

Corrigendum: Sub-surface drip fertigation improves seed cotton yield and monetary returns.

[This corrects the article DOI: 10.3389/fpls.2022.1038163.].

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

BACKGROUND: Climate 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. METHODS: Thirty-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. RESULTS: Biplot, 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). CONCLUSIONS: These findings can provide the basis to develop self-irrigating, drought-tolerant wheat cultivars as an adaptation to climate change.

Morpho-physiological and biochemical response of wheat to various treatments of silicon nano-particles under drought stress conditions.

Silicon nanoparticles (Si-NPs) have shown their potential for use in farming under water-deficient conditions. Thus, the experiment was accomplished to explore the impacts of seed priming of Si-NPs on wheat (Triticum aestivum L.) growth and yield under different drought levels. The plants were grown in pots under natural ecological environmental conditions and were harvested on 25th of April, 2020. The results revealed that seed priming of Si-NPs (0, 300, 600, and 900 mg/L) suggestively improved, the spike length, grains per spike, 1000 grains weight, plant height, grain yield, and biological yield by 12-42%, 14-54%, 5-49%, 5-41%, 17-62%, and 21-64%, respectively, relative to the control. The Si-NPs improved the leaf gas trade ascribes and chlorophyll a and b concentrations, though decreased the oxidative pressure in leaves which was demonstrated by the diminished electrolyte leakage and upgrade in superoxide dismutase and peroxidase activities in leaf under Si-NPs remedies over the control. The outcomes proposed that Si-NPs could improve the yield of wheat under a dry spell. In this manner, the utilization of Si-NPs by seed priming technique is a practical methodology for controlling the drought stress in wheat. These findings will provide the basis for future research and helpful to improve the food security under drought and heat related challenges.

Modelling, mapping and monitoring of forest cover changes, using support vector machine, kernel logistic regression and naive bayes tree models with optical remote sensing data.

The present study is designed to monitor the spatio-temporal changes in forest cover using Remote Sensing (RS) and Geographic Information system (GIS) techniques from 1990 to 2017. Landsat data from 1990 (Thematic mapper [TM]), 2000 and 2010 (Enhanced Thematic Mapper [ETM+]), and 2013 to 2017 (Operational Land Imager/Thermal Infrared Sensor [OLI/TIRS]) were classified into the classes termed snow, water, barren land, built-up area, forest, and vegetation. The method was built using multitemporal Landsat images and the machine learning techniques Support Vector Machine (SVM), Naive Bayes Tree (NBT) and Kernel Logistic Regression (KLR). According to the results, forest area was decreased from 19,360 km2 (26.0%) to 18,784 km2 (25.2%) from 1990 to 2010, while forest area was increased from 18,640 km2 (25.0%) to 26,765 km2 (35.9%) area from 2013 to 2017 due to "One billion tree Project". According to our findings, SVM performed better than KLR and NBT on all three accuracy metrics (recall, precision, and accuracy) and the F1 score was >0.89. The study demonstrated that concurrent reforestation in barren land areas improved methods of sustaining the forest and RS and GIS into everyday forestry organization practices in Khyber Pakhtun Khwa (KPK), Pakistan. The study results were beneficial, especially at the decision-making level for the local or provincial government of KPK and for understanding the global scenario for regional planning.

Sub-surface drip fertigation improves seed cotton yield and monetary returns.

Surface flood (SF) method is used to irrigate cotton in India, which results in huge wastage of water besides leaching of nutrients. This necessitates the adoption of efficient management strategies to save scarce water without compromising the yield. Therefore, a 2-year field investigation was conducted under two climatic regimes (Faridkot and Abohar) to study the effect of sub-surface drip fertigation (SSDF) on seed cotton yield (SCY), water productivity, nitrogen use efficiency (NUE), and economic parameters in comparison with SF and surface drip fertigation (SDF). The field experiment had a total of eight treatments arranged in a randomized complete block design. Three levels of sub-surface drip irrigation [(SSDI); i.e., 60%, 80%, and 100% of crop evapotranspiration (ETc)] and two N fertigation levels [100% recommended dose of nitrogen (RDN; i.e., 112.5 kg N ha-1) and 75% RDN] made up six treatments, while SF (Control 1) and SDF at 80% ETc (Control 2), both with 100% of RDN, served as the controls. Among irrigation regimes, the SSDI levels of 80% ETc and 100% ETc recorded 18.7% (3,240 kg ha-1) and 21.1% (3,305 kg ha-1) higher SCY compared with SF (2,728 kg ha-1). Water use efficiency under SF (57.0%) was reduced by 34.2%, 40.8%, and 38.2% compared with SSDI's 60 (76.5%), 80 (80.3%), and 100% ETc (78.8%), respectively. Among fertigation levels, NUE was higher by 19.2% under 75% (34.1 kg SCY kg-1 N) over 100% RDN (28.6 kg SCY kg-1 N), but later it also registered 11.9% higher SCY, indicating such to be optimum for better productivity. SSDF at 80% ETc along with 112.5 kg N ha-1 recorded 26.6% better SCY (3455 kg ha-1) and 18.5% higher NUE (30.7 kg SCY kg-1 N) over SF. These findings demonstrate that the application of SSDF could save irrigation water, enhance SCY, and improve the farmers' returns compared with SF. Therefore, in northwestern India, SSDF at 80% ETc along with 112.5 kg N ha-1 could be a novel water-savvy concept which would be immensely helpful in enhancing cotton productivity.

Exogenous aspartic acid alleviates salt stress-induced decline in growth by enhancing antioxidants and compatible solutes while reducing reactive oxygen species in wheat.

Salinity is the primary environmental stress that adversely affects plants' growth and productivity in many areas of the world. Published research validated the role of aspartic acid in improving plant tolerance against salinity stress. Therefore, in the present work, factorial pot trials in a completely randomized design were conducted to examine the potential role of exogenous application of aspartic acid (Asp) in increasing the tolerance of wheat (Triticum aestivum L.) plants against salt stress. Wheat plants were sown with different levels of salinity (0, 30, or 60 mM NaCl) and treated with three levels of exogenous application of foliar spray of aspartic acid (Asp) (0, 0.4, 0.6, or 0.8 mM). Results of the study indicated that salinity stress decreased growth attributes like shoot length, leaf area, and shoot biomass along with photosynthesis pigments and endogenous indole acetic acid. NaCl stress reduced the total content of carbohydrates, flavonoid, beta carotene, lycopene, and free radical scavenging activity (DPPH%). However, Asp application enhanced photosynthetic pigments and endogenous indole acetic acid, consequently improving plant leaf area, leading to higher biomass dry weight either under salt-stressed or non-stressed plants. Exogenous application of Asp, up-regulate the antioxidant system viz. antioxidant enzymes (superoxide dismutase, peroxidase, catalase, and nitrate reductase), and non-enzymatic antioxidants (ascorbate, glutathione, total phenolic content, total flavonoid content, beta carotene, lycopene) contents resulted in declined in reactive oxygen species (ROS). The decreased ROS in Asp-treated plants resulted in reduced hydrogen peroxide, lipid peroxidation (MDA), and aldehyde under salt or non-salt stress conditions. Furthermore, Asp foliar application increased compatible solute accumulation (amino acids, proline, total soluble sugar, and total carbohydrates) and increased radical scavenging activity of DPPH and enzymatic ABTS. Results revealed that the quadratic regression model explained 100% of the shoot dry weight (SDW) yield variation. With an increase in Asp application level by 1.0 mM, the SDW was projected to upsurge through 956 mg/plant. In the quadratic curve model, if Asp is applied at a level of 0.95 mM, the SDW is probably 2.13 g plant-1. This study concluded that the exogenous application of aspartic acid mitigated the adverse effect of salt stress damage on wheat plants and provided economic benefits.

Impact of climate change on agricultural production; Issues, challenges, and opportunities in Asia.

Agricultural production is under threat due to climate change in food insecure regions, especially in Asian countries. Various climate-driven extremes, i.e., drought, heat waves, erratic and intense rainfall patterns, storms, floods, and emerging insect pests have adversely affected the livelihood of the farmers. Future climatic predictions showed a significant increase in temperature, and erratic rainfall with higher intensity while variability exists in climatic patterns for climate extremes prediction. For mid-century (2040-2069), it is projected that there will be a rise of 2.8°C in maximum temperature and a 2.2°C in minimum temperature in Pakistan. To respond to the adverse effects of climate change scenarios, there is a need to optimize the climate-smart and resilient agricultural practices and technology for sustainable productivity. Therefore, a case study was carried out to quantify climate change effects on rice and wheat crops and to develop adaptation strategies for the rice-wheat cropping system during the mid-century (2040-2069) as these two crops have significant contributions to food production. For the quantification of adverse impacts of climate change in farmer fields, a multidisciplinary approach consisted of five climate models (GCMs), two crop models (DSSAT and APSIM) and an economic model [Trade-off Analysis, Minimum Data Model Approach (TOAMD)] was used in this case study. DSSAT predicted that there would be a yield reduction of 15.2% in rice and 14.1% in wheat and APSIM showed that there would be a yield reduction of 17.2% in rice and 12% in wheat. Adaptation technology, by modification in crop management like sowing time and density, nitrogen, and irrigation application have the potential to enhance the overall productivity and profitability of the rice-wheat cropping system under climate change scenarios. Moreover, this paper reviews current literature regarding adverse climate change impacts on agricultural productivity, associated main issues, challenges, and opportunities for sustainable productivity of agriculture to ensure food security in Asia. Flowing opportunities such as altering sowing time and planting density of crops, crop rotation with legumes, agroforestry, mixed livestock systems, climate resilient plants, livestock and fish breeds, farming of monogastric livestock, early warning systems and decision support systems, carbon sequestration, climate, water, energy, and soil smart technologies, and promotion of biodiversity have the potential to reduce the negative effects of climate change.

Evaluating the efficiency of coarser to finer resolution multispectral satellites in mapping paddy rice fields using GEE implementation.

Timely and accurate estimation of rice-growing areas and forecasting of production can provide crucial information for governments, planners, and decision-makers in formulating policies. While there exists studies focusing on paddy rice mapping, only few have compared multi-scale datasets performance in rice classification. Furthermore, rice mapping of large geographical areas with sufficient accuracy for planning purposes has been a challenge in Pakistan, but recent advancements in Google Earth Engine make it possible to analyze spatial and temporal variations within these areas. The study was carried out over southern Punjab (Pakistan)-a region with 380,400 hectares devoted to rice production in year 2020. Previous studies support the individual capabilities of Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) for paddy rice classification. However, to our knowledge, no study has compared the efficiencies of these three datasets in rice crop classification. Thus, this study primarily focuses on comparing these satellites' data by estimating their potential in rice crop classification using accuracy assessment methods and area estimation. The overall accuracies were found to be 96% for Sentinel-2, 91.7% for Landsat-8, and 82.6% for MODIS. The F1-Scores for derived rice class were 83.8%, 75.5%, and 65.5% for Sentinel-2, Landsat-8, and MODIS, respectively. The rice estimated area corresponded relatively well with the crop statistics report provided by the Department of Agriculture, Punjab, with a mean percentage difference of less than 20% for Sentinel-2 and MODIS and 33% for Landsat-8. The outcomes of this study highlight three points; (a) Rice mapping accuracy improves with increase in spatial resolution, (b) Sentinel-2 efficiently differentiated individual farm level paddy fields while Landsat-8 was not able to do so, and lastly (c) Increase in rice cultivated area was observed using satellite images compared to the government provided statistics.

Leaf rolling and leaf angle improve fog capturing and transport in wheat; adaptation for drought stress in an arid climate.

BACKGROUND: Plants use different mechanisms to transport the collected fog water. Leaf traits of wheat play an important role in directing fog water through leaf rolling and leaf angle into the root zone, where it can be stored for consumption. Wheat leaf traits can enhance fog capturing under drought stress. To examine this, 200 wheat genotypes were characterized for leaf rolling and leaf angle under optimal conditions in the field using a randomized complete block design. Seven different phenotypic combinations for leaf traits were observed. A core set of 44 genotypes was evaluated under drought stress. RESULTS: Results show that variability for leaf traits existed among genotypes. An association was found between leaf rolling and leaf angle, moisture capturing, physiological parameters, and yield contributing traits using correlation. Physiological parameters, especially water use efficiency, were positively correlated with grain yield and moisture capturing at both growth stages. The genotypes (G11 at tillering and G24 at booting phonological phases) with inward to twisting type rolling and erect to semi-erect leaf angle capture more water (12-20%) within the root zone. Twenty-one genotypes were selected based on moisture capturing efficiency and evaluated for leaf surface wettability. Association was found between fog capturing and wettability. This shows that it was due to the leaf repellency validated from static contact angle measurements. CONCLUSION: These results will give insights into fog capturing and the development of drought-tolerant crops in the semi-arid and arid regions.

Methyl Jasmonate Alleviated the Adverse Effects of Cadmium Stress in Pea (Pisum sativum L.): A Nexus of Photosystem II Activity and Dynamics of Redox Balance.

The accumulation of cadmium (Cd) in leaves reduces photosynthetic capacity by degrading photosynthetic pigments, reducing photosystem II activity, and producing reactive oxygen species (ROS). Though it was demonstrated that the application of Methyl Jasmonate (MeJA) induces heavy metal (HM) stress tolerance in plants, its role in adjusting redox balance and photosynthetic machinery is unclear. In this study, the role of MeJA in modulating photosystem II (PSII) activity and antioxidant defense system was investigated to reduce the toxic effects of Cd on the growth of pea (Pisum sativum L.) cultivars. One-week-old seedlings of three pea varieties were subjected to Cd stress (0, 50, 100 µm), and MeJA (0, 1, 5, 10 µm) was applied as a foliar spray for 2 weeks. Cadmium stress reduced the growth of all three pea varieties. Cadmium stress decreased photosynthetic pigments [Chl a (58.15%), Chl b (48.97%), total Chl (51.9%) and carotenoids (44.01%)] and efficiency of photosystem II [Fv/Fm (19.52%) and Y(II; 67.67%)], while it substantially increased Cd accumulation along with an increase in ROS (79.09%) and lipid peroxidation (129.28%). However, such adverse effects of Cd stress varied in different pea varieties. Exogenous application of MeJA increased the activity of a battery of antioxidant enzymes [superoxide dismutase (33.68%), peroxidase (29.75%), and catalase (38.86%)], improved photosynthetic pigments and PSII efficiency. This led to improved growth of pea varieties under Cd stress, such as increased fresh and dry weights of shoots and roots. In addition, improvement in root biomass by MeJA was more significant than that of shoot biomass. Thus, the mitigating effect of MeJA was attributed to its role in cellular redox balance and photosynthetic machinery of pea plants when exposed to Cd stress.

Using the Taguchi experimental design for assessing within-field variability of surface run-off and soil erosion risk.

Water erosion is one of the soil degradation processes driven by environmental and field factors such as rainfall intensity, slope gradient, dynamics of vegetation cover, soil characteristics, and management practices. Most of the studies assess the separate contribution of these factors under controlled conditions. However, there is a lack of adequate knowledge regarding the complex interactions between prevailing factors and soil erosion processes under heterogeneous field conditions. This study investigated 16 combinations of 5 factors at 4 levels of each factor on the soil erosion process using Taguchi's fractional factorial experiment design, identifying the factor combinations resulting in maximum sediment yield, runoff, organic carbon, and nitrogen losses. We considered the factors: Soil organic matter and silt content (SiltOM), vegetation cover (VC), slope steepness (SS), rainfall intensity (RI), and depth to a loamy layer (DLL). The interactive effects of these factors and their combinations were visualized from the analysis of signal-to-noise (S/N) responses. Results indicated that interactions between the selected factors and soil erosion processes exist and multiple linear regression models were developed to predict sediment yields, runoff, carbon, and nitrogen losses at the sub-field scale. Results revealed that 1) RI with 40.6% showed the highest contribution to sediment yield followed by SS (23.8%), VC (17.74%), SiltOM (14.77%), and DLL (3.17%), indicating a strong rainfall-erosion relationship; 2) the combination of levels of factors generating highest sediment yield was determined; 3) A simple multiple linear regression model developed for predicting local sediment yield showed the highest agreement with field observations (R2 = 82.5%). The findings suggest that Taguchi design could be used reliably for modeling soil erosion at field and sub-field scales. Using local calibration data such models have great potential for soil erosion risk assessments at the field scale, especially in areas where contributing factors and factor levels change at small spatial scales.

Effect of slow release nitrogenous fertilizers and biochar on growth, physiology, yield, and nitrogen use efficiency of sunflower under arid climate.

Sunflower plants need nitrogen consistently and in higher amount for optimum growth and development. However, nitrogen use efficiency (NUE) of sunflower crop is low due to various nitrogen (N) losses. Therefore, it is necessary to evaluate the advanced strategies to minimize N losses and also improve sunflower productivity under arid climatic conditions. A field trial was conducted with four slow release nitrogenous fertilizers [SRNF (bacterial, neem, and sulfur-coated urea and N loaded biochar)] and three N levels (100% = 148 kg N ha-1, 80% = 118 kg N ha-1, and 60% = 89 kg N ha-1) of recommended application (100%) for sunflower crop under arid climatic conditions. Results showed that neem-coated urea at 148 kg N ha-1 significantly enhanced crop growth rate (CGR) (19.16 g m-2 d-1) at 60-75 days after sowing (DAS); leaf area index (2.12, 3.62, 5.97, and 3.00) at 45, 60, 75, and 90 DAS; and total dry matter (14.27, 26.29, 122.67, 410, and 604.33 g m-2) at 30, 45, 60, 75, and 90 DAS. Furthermore, higher values of net leaf photosynthetic rate (25.2 µmol m-2 s-1), transpiration rate (3.66 mmol s-1), and leaf stomatal conductance (0.39 mol m-2 s-1) were recorded for the same treatment. Similarly, neem-coated urea produced maximum achene yield (2322 kg ha-1), biological yield (9000 kg ha-1), and harvest index (25.8%) of the sunflower crop. Among various N fertilizers, neem-coated urea showed maximum NUE (20.20 kg achene yield kg-1 N applied) in comparison to other slow release N fertilizers. Similarly, nitrogen increment N60 showed maximum NUE (22.40 kg grain yield kg-1 N applied) in comparison to N80 and N100. In conclusion, neem-coated urea with 100% and 80% of recommended N would be recommended for farmers to get better sunflower productivity with sustainable production and to reduce the environmental nitrogen losses.

Leaf rolling dynamics for atmospheric moisture harvesting in wheat plant as an adaptation to arid environments.

Plant species surviving in the arid regions have developed novel leaf features to harvest atmospheric water. Before the collected water evaporates, it is absorbed and transported for storage within the tissues and move toward the root zone through the unique chemistry of leaf structures. Deep insights into such features reveal that similarities can be found in the wheat plant. Therefore, this study aimed to evaluate the leaf rolling dynamics among wheat genotypes and their relationships with moisture harvesting and its movement on the leaf surface. For this purpose, genotypes were characterized for leaf rolling at three distinct growth stages (tillering, booting, and spike emergence). The contact angle of leaf surface dynamics (adaxial and abaxial), water budget, and morphophysiological traits of genotypes were measured. The results indicate that leaf rolling varies from inward to twisting type among genotypes and positively affected the water use efficiency and soil moisture difference at all growth stages under normal and drought conditions. Results of wetting property (hydrophilic < 90°) of the leaf surface were positively associated with the atmospheric water collection (4-7 ml). The lower values of contact angle hysteresis (12-19°) also support this mechanism. Thus, genotypes with leaf rolling dynamics (inward rolled and twisted) and surface wettability is an efficient fog harvesting system in wheat for interception and utilization of fog water in drought-prone areas. These results can be exploited to develop self-irrigated and drought-tolerant crops.

Wetting mechanism and morphological adaptation; leaf rolling enhancing atmospheric water acquisition in wheat crop-a review.

Several plant species such as grasses are dominant in many habitats including arid and semi-arid areas. These species survive in these regions by developing exclusive structures, which helps in the collection of atmospheric water. Before the collected water evaporates, these structures have unique canopy structure for water transportation that plays an equivalent share in the fog-harvesting mechanism. In this review, the atmospheric gaseous water harvesting mechanisms and their affinity of measurements were discussed. Morphological adaptations and their role in the capturing of atmospheric gaseous water of various species were also discussed. The key factor for the water collection and its conduction in the wheat plant is the information of contact angle hysteresis. In wheat, leaf rolling and its association with wetting property help the plant in water retention. Morphological adaptations, i.e., leaf erectness, grooves, and prickle hairs, also help in the collection and acquisition of water droplets by stem flows in directional guide toward the base of the plant and allow its rapid uptake. Morphological adaptation strengthens the harvesting mechanism by preventing the loss of water through shattering. Thus, wheat canopy architecture can be modified to harvest the atmospheric water and directional movement of water towards the root zone for self-irrigation. Moreover, these morphological adaptations are also linked with drought avoidance and corresponding physiological processes to resist water stress. The combination of these traits together with water use efficiency in wheat contributes to a highly efficient atmospheric water harvesting system that enables the wheat plants to reduce the cost of production. It also increases the yielding potential of the crop in arid and semi-arid environments. Further investigating the ecophysiology and molecular pathways of these morphological adaptations in wheat may have significant applications in varying climatic scenarios.

Climate change impact uncertainty assessment and adaptations for sustainable maize production using multi-crop and climate models.

Future climate scenarios are predicting considerable threats to sustainable maize production in arid and semi-arid regions. These adverse impacts can be minimized by adopting modern agricultural tools to assess and develop successful adaptation practices. A multi-model approach (climate and crop) was used to assess the impacts and uncertainties of climate change on maize crop. An extensive field study was conducted to explore the temporal thermal variations on maize hybrids grown at farmer's fields for ten sowing dates during two consecutive growing years. Data about phenology, morphology, biomass development, and yield were recorded by adopting standard procedures and protocols. The CSM-CERES, APSIM, and CSM-IXIM-Maize models were calibrated and evaluated. Five GCMs among 29 were selected based on classification into different groups and uncertainty to predict climatic changes in the future. The results predicted that there would be a rise in temperature (1.57-3.29 °C) during the maize growing season in five General Circulation Models (GCMs) by using RCP 8.5 scenarios for the mid-century (2040-2069) as compared with the baseline (1980-2015). The CERES-Maize and APSIM-Maize model showed lower root mean square error values (2.78 and 5.41), higher d-index (0.85 and 0.87) along reliable R2 (0.89 and 0.89), respectively for days to anthesis and maturity, while the CSM-IXIM-Maize model performed well for growth parameters (leaf area index, total dry matter) and yield with reasonably good statistical indices. The CSM-IXIM-Maize model performed well for all hybrids during both years whereas climate models, NorESM1-M and IPSL-CM5A-MR, showed less uncertain results for climate change impacts. Maize models along GCMs predicted a reduction in yield (8-55%) than baseline. Maize crop may face a high yield decline that could be overcome by modifying the sowing dates and fertilizer (fertigation) and heat and drought-tolerant hybrids.

Biochar and slow-releasing nitrogen fertilizers improved growth, nitrogen use, yield, and fiber quality of cotton under arid climatic conditions.

The efficiency of nitrogenous fertilizers in South Asia is on a declining trajectory due to increased losses. Biochar (BC) and slow-releasing nitrogen fertilizers (SRNF) have been found to improve nitrogen use efficiency (NUE) in certain cases. However, field-scale studies to explore the potential of BC and SRNF in south Asian arid climate are lacking. Here we conducted a field experiment in the arid environment to demonstrate the response of BC and SRNF on cotton growth and yield quality. The treatments were comprised of two factors, (A) nitrogen sources, (i) simple urea, (ii)neem-coated urea, (iii)sulfur-coated urea, (iv) bacterial coated urea, and cotton stalks biochar impregnated with simple urea, and (B) nitrogen application rates, N1=160 kg ha-1, N2 = 120 kg ha-1, and N3 = 80 kg ha-1. Different SRNF differentially affected cotton growth, morphological and physiological attributes, and seed cotton yield (SCY). The bacterial coated urea at the highest rate of N application (160 kg ha-1) resulted in a higher net leaf photosynthetic rate (32.8 µmol m-2 s-1), leaf transpiration rate (8.10 mmol s-1), and stomatal conductance (0.502 mol m-2 s-1), while leaf area index (LAI), crop growth rate (CGR), and seed cotton yield (4513 kg ha-1) were increased by bacterial coated urea at 120 kg ha-1 than simple urea. However, low rate N application (80 kg ha-1) of bacterial coated urea showed higher nitrogen use efficiency (39.6 kg SCY kg-1 N). The fiber quality (fiber length, fiber strength, ginning outturn, fiber index, and seed index) was also increased with the high N application rates than N2 and N3 application. To summarize, the bacterial coated urea with recommended N (160 kg ha-1) and 75% of recommended N application (120 kg ha-1) may be recommended for farmers in the arid climatic conditions of Punjab to enhance the seed cotton yield, thereby reducing nitrogen losses.