Mid-growth Stage Research Articles

Seasonal Changes in Salicylic and Jasmonic Acid Levels in Poplar with Differing Stress Responses

Poplars are essential tree species with critical roles in wood production, ecological conservation, environmental protection, and climate change mitigation. Plants of different poplar populations exhibit varying responses to biotic and abiotic stresses, largely driven by phytohormones—key regulators of plant growth and stress responses. Phytohormones control fundamental processes such as cell division, growth, tissue differentiation, organ development, and resistance to environmental challenges such as temperature extremes and drought. In this study, we conducted a quantitative analysis of phytohormone levels in the leaves of two populations of poplar species from the same geographical region over a one-year period, covering the early, mid, and late growth stages. Our results revealed that salicylic acid (SA) and jasmonic acid (JA) concentrations in the stress-resistant poplar (RP) increased significantly during the late growth stage (September), particularly in response to elevated pathogen pressure and fluctuating climatic conditions. In contrast, the susceptible poplar (SP) consistently exhibited lower levels of SA and JA across all growth stages, particularly during the mid-growth stage (characterized by high temperatures and low precipitation) and the late growth stage, when pathogen stress typically intensifies. The limited increase in phytohormone levels in the SP suggests a weaker defense response compared with the RP. These findings demonstrate that climatic factors significantly influence the phytohormonal dynamics in poplars, with the RP exhibiting more robust regulation of SA and JA to mitigate both biotic and abiotic stresses. This study provides a theoretical framework for better understanding the resistance mechanisms in Populus and offers insights for improving stress tolerance in future breeding programs.

Maize (Zea Mays L.) Response to Deficit Irrigation Levels at Different Growth Stages on its Yield and Water Use Efficiency at Koka, Central Rift Valley of Ethiopia

Water scarcity is among the major limitations for crop production. Improving water use efficiency of irrigated crops through water management options is crucial in water scarce areas. Field experiment was carried out at Wondo Genet Agricultural Research Center Koka Research site, to investigate the effect of water stress at different growth stage on yield and water use efficiency of maize. one optimum irrigation and eight growth stage based deficit levels(100% ETC at all growth stages, 75% ETC at all growth stages, 50% ETC at all growth stages, 75% ETC at development growth stage, 50% ETC at development growth stage, 75% ETC at mid growth stage, 50% ETC at mid growth stage, 75% ETC at late growth stage and 50% ETC at late growth stage) were imposed on maize (Zea mays L.) variety Melkassa II as a treatment and laid out in randomized complete block design (RCBD) with three replications. Results indicated that the different levels of growth stage based deficit levels had significant (p<0.01) effect on crop yield, harvest index and water use efficiency. Grain yield reduced with increased stress, whereas water use efficiency was increased with stress level increased. The highest grain yield of 6.4 t/ha and WUE of 1.01 kg/m3 were obtained at 100% ETC and 50% ETC at all growth stages, respectively. Also, 75% ETC at development stage and late stage treatments showed no significant variation with 100% ETC in grain yield. Water use efficiency observed at 75% ETC all growth stages treatment was statistically similar with that of 50% ETC at all growth stages treatment. Grain yield obtained from 50% ETC at mid growth stage was similar with 50% ETC at all growth stages, and water use efficiency was the least and this shows that maize was sensitive to moisture stress at mid growth stage than development and late growth stages. Therefore, maize could be irrigated at 75% ETC at all growth stages and by stressing development or late growth stages up to 50% ETC to increase water use efficiency with a small grain yield reduction for stressed scenario and for non stressed scenario with 100% ETC at all growth stages.

Transcriptome and Metabolome Reveal Accumulation of Key Metabolites with Medicinal Properties of Phylloporia pulla.

Phylloporia pulla, a macrofungal species in the Hymenochaetales, Basidiomycota, is known to enhance the nutritional and bioactive properties of rice through co-fermentation; however, its own secondary metabolites are not well understood. In this study, an integrative analysis of transcriptome and metabolome data revealed that the accumulation of steroids, steroid derivatives, and triterpenoids in P. pulla peaks during the mid-growth stage, while the genes associated with these metabolites show higher expression levels from the early to mid-growth stages. Weighted gene co-expression network analysis identified several modules containing candidate genes involved in the synthesis of steroids, steroid derivatives, and triterpenoids. Specifically, six key hub genes were identified, along with their connectivity to other related genes, as potential catalysts in converting the precursor lanosterol to celastrol. This study enhances our understanding of the secondary metabolites of P. pulla and is essential for the selective utilization of these bioactive compounds.

Algorithm for Corn Crop Row Recognition during Different Growth Stages Based on ST-YOLOv8s Network

Corn crop row recognition during different growth stages is a major difficulty faced by the current development of visual navigation technology for agricultural robots. In order to solve this problem, an algorithm for recognizing corn crop rows during different growth stages is presented based on the ST-YOLOv8s network. Firstly, a dataset of corn crop rows during different growth stages, including the seedling stage and mid-growth stage, is constructed in this paper; secondly, an improved YOLOv8s network, in which the backbone network is replaced by the swin transformer (ST), is proposed in this paper for detecting corn crop row segments; after that, an improved supergreen method is introduced in this paper, and the segmentation of crop rows and background within the detection frame is achieved utilizing the enhanced method; finally, the corn crop row lines are identified using the proposed local–global detection method, which detects the local crop rows first, and then detects the global crop rows. The corn crop row segment detection experiments show that the mean average precision (MAP) of the ST-YOLOv8s network during different growth stages increases by 7.34%, 11.92%, and 4.03% on average compared to the MAP of YOLOv5s, YOLOv7, and YOLOv8s networks, respectively, indicating that the ST-YOLOv8s network has a better crop row segment detection effect compared to the comparison networks. Corn crop row line detection experiments show that the accuracy of the local–global detection method proposed in this paper is improved by 17.38%, 10.47%, and 5.99%, respectively, compared with the accuracy of the comparison method; the average angle error is reduced by 3.78°, 1.61°, and 0.7°, respectively, compared with the average angle error of the comparison method; and the average fitting time is reduced by 5.30 ms, 18 ms, and 33.77 ms, respectively, compared with the average fitting time of the comparison method, indicating that the local–global detection method has a better crop row line detection effect compared to the comparison method. In summary, the corn crop row recognition algorithm proposed in this paper can well accomplish the task of corn crop row recognition during different growth stages and contribute to the development of crop row detection technology.

Determination of Evapotranspiration and Crop Coefficients of Irrigated Legumes on Different Soil Textures Using the FAO56 Approach

Climates and locations specific crop coefficients (Kc) plays pivotal roles in effective water management and crop yield optimization. Research on Kc for many staple crops remains limited in many regions across Africa. This study examined the crop coefficients of two varieties of cowpea (Sampea 14 and Sampea 17) and a variety of soybean (TGX 1465-1D) across different soil textures for optimized water management and crop yield. This study ascertained the Kc of Sampea14 (V1), Sampea17 (V2), and TGX 1465-1D (V3) cultivated on sandy, sandy clay loam, and sandy loam soils, within Port Harcourt, Southern Nigeria. This formed a 3 by 3 factorial experiment in three replicates. Changes in soil water content and crops’ agronomic parameters were monitored regularly. Meteorological data were obtained from the National Aeronautic and Space Administration website and reference evapotranspiration were estimated from the meteorological data using the FAO Penman Monteith’s method. Kc were estimated from the actual crop water use and reference crop evapotranspiration using the FAO56 method. During the initial-growth stage, the average Kc for V1, V2, and V3 were computed as 0.55, 0.72, and 0.81, respectively. At the mid-growth stage, these coefficients increased to 1.48, 1.30, and 1.60 and at the late season, the average Kc were 0.80, 0.69, and 0.86, for the respective varieties. The findings from this study hold the potential to significantly contribute to proper water management practices in the studied area. The understanding of Kc for these specific legume varieties across different soil textures offers valuable insights for optimizing irrigation scheduling and ultimately enhancing crop yield and water productivity.

Evaluation of land roughness and weather effects on paddy field using cameras mounted on drone: A comprehensive analysis from early to mid-growth stages

This paper proposes a potential solution to the traditional method of rice field preparation and management by utilizing unmanned aerial vehicles (UAVs) equipped with RGB and spectral cameras. Farmers typically rely on their experience to deal with bad weather and prepare rice fields in the beginning of the season, resulting in time-consuming and inefficient processes. The proposed method uses UAVs to evaluate the rice fields from the start to the middle stages of the season. The UAV flight follows a pre-determined trajectory autonomously, capturing successive images using the attached cameras. In the soil preparation stage, Harralick texture segmentation is used to analyze the roughness of paddy fields’ surfaces (low land, normal land and high land). In this approach, image is segmented first, then areas belonging the same clusters are extracted, manually verified and labelled. These labelled images are used as training data for the proposed model. The result shows that roughness of field surface can be observed clearly with high accuracy at over 90%, with random forest method is at over 97%. In addition to this, the effects of rain and flooding are also considered during the reproductive stages. The results show that the proposed method achieves high accuracy in evaluating rice fields, which can significantly increase efficiency and productivity compared to traditional methods. The study highlights the potential of UAV-based monitoring to provide a cost-effective and efficient way to manage rice fields and improve crop yields, leading to higher food security and sustainable agriculture.

Determination of Evapotranspiration and Crop Coefficients of Irrigated Legumes on Different Soil Types Using the FAO56 Approach

Climates and locations specific crop coefficients (CC) plays pivotal roles in effective water management and yield optimization. Research on crop coefficients for many staple crops remains limited in many regions across Africa. This study was designed to ascertain the crop coefficients of Sampea14 (V1), Sampea17 (V2), and TGX 1465-1D (V3) cultivated on sandy, sandy clay loam, and sandy loam soils, within Port Harcourt, Southern Nigeria. A 3x3 factorial experiment was designed in a Randomized Complete Block Design (RCBD), with three replicates. During the initial-growth stage, the average CC for V1, V2, and V3 were computed as 0.42, 0.54, and 0.24, respectively. At the mid-growth stage, these coefficients increased to 1.82, 2.04, and 2.59 and in the late season, the average CC were 0.89, 0.54, and 0.32, for the respective varieties. The outcomes of this study would enhance efficient water management practices and ultimately improve both crop and water productivity.

Crop Classification and Growth Monitoring in Coal Mining Subsidence Water Areas Based on Sentinel Satellite

In high groundwater level mining areas, subsidence resulting from mining can lead to waterlogging in farmland, causing damage to crops and affecting their growth and development, thereby affecting regional food security. Therefore, it is necessary to restore agricultural production in the coal mining subsidence water areas in the densely populated eastern plains. This study focuses on the Yongcheng coal mining subsidence water areas. It utilizes Sentinel-1 and Sentinel-2 data from May to October in the years 2019 to 2022 to monitor the growth and development of crops. The results demonstrated that (1) the accuracy of aquatic crops categorization was improved by adjusting the elevation of the study region with Mining Subsidence Prediction Software (MSPS 1.0). The order of accuracy for classifying aquatic crops using different machine learning techniques is Random Forest (RF) > Classification and Regression Trees (CART) ≥ Support Vector Machine (SVM). Using the RF method, the obtained classification results can be used for subsequent crop growth monitoring. (2) During the early stages of crop growth, when vegetation cover is low, the Radar Vegetation Index (RVI) is sensitive to the volume scattering of crops, making it suitable for tracking the early growth processes of crops. The peak RVI values for crops from May to July are ranked in the following order: rice (2.595), euryale (2.590), corn (2.535), and lotus (2.483). (3) The order of crops showing improved growth conditions during the mid-growth stage is as follows: rice (47.4%), euryale (43.4%), lotus (27.6%), and corn (4.01%). This study demonstrates that in the Yongcheng coal subsidence water areas, the agricultural reclamation results for the grain-focused model with rice as the main crop and the medicinal herb-focused model with euryale as the main crop are significant. This study can serve as a reference for agricultural management and land reclamation efforts in other coal subsidence water areas.

Oxygenation promotes vegetable growth by enhancing P nutrient availability and facilitating a stable soil bacterial community in compacted soil

Soil compaction is a widespread phenomenon in intensive agriculture and can severely hamper root growth and crop yield. Hydrogen peroxide urea (UHP) has been employed as an effective measure for oxygenation in dryland systems. However, whether oxygenation via UHP could alleviate negative effects on vegetable growth induced by soil compaction is unknown. A pot experiment involving different soil bulk densities (1.2, 1.5, and 1.7 g cm−3) with oxygenation at different stages of development (seedling stage, mid-growth stage) was conducted in amaranth (A. mangostanus), with non-oxygenation at different soil bulk densities as a control. Increased soil compaction affected vegetative growth of both the shoot and root system of amaranth and caused yield reduction. Oxygenation in topdressing significantly improved soil O2 supply until the harvest period, imparted 17.9–20.4 % additional O2 to soils. Increased O2 supply alleviated soil compaction stress, and amaranth yield increased by 5.50–16.1 % at the same soil bulk density compared with control, respectively. Oxygenation during the mid-growth stage had a superior effect on vegetable growth compared to oxygenation at the seedling stage, coincident with the higher oxygen demand during mid-growth in amaranth. Oxygenation promoted root growth in compacted soil, and increased root length by 76.3 % at a soil bulk density of 1.5 g cm−3 and 74.7 % at a bulk density of 1.7 g cm−3 compared with control. The alleviatory effect on the root system can be linked to increased root catalase activity and decreased malondialdehyde content. Oxygenation furthermore increased phosphorus (P) bioavailability by increasing both phosphatase activity and the relative abundance of phosphate-solubilizing bacteria (Firmicutes and Proteobacteria), with an increase in DGT-P by 10.5–23.6 % compared to control. The soil bacterial co-occurrence network at a soil bulk density of 1.5 g cm−3 with oxygenation was more similar to 1.2 g cm−3 soil compaction with oxygenation, which possessed most complex and the proportion of connectors, but was dissimilar from 1.7 g cm−3 soil compaction with oxygenation, which was less stable microbial network structure. Improvement of O2 content could build a healthy and stable rhizosphere bacterial community, and increased the proportion of important nodes in the network, which was conducive to the collaborative development of microorganisms to face compaction stress. And bacterial community structure with oxygenation in compacted soil could recovery to near normal microbiology community structure. The research provides a theoretical and technical framework for the development of strategies that can maximize crop yield in compacted soil.

基于随机森林算法的整株水培生菜缺素诊断

The phenotypic information of lettuce leaves can well reflect its health. In order to diagnose the nutrient deficiency types of hydroponic lettuce accurately, non-destructively and quickly in the mid-growth stage, a method for diagnosis of whole lettuce based on random forest algorithm (RF) was proposed. The images of lettuce under four different conditions, K-deficiency, Ca-deficiency, N-deficiency and Normal, were collected and segmented by Extra-green algorithm. Then, features of color, texture and shape were extracted. A RF classification model for the hydroponic lettuce nutrient deficiency diagnosis was constructed and compared with support vector machine (SVM) and back propagation neural network (BP). RF had the best classification effect among the three methods. The overall classification accuracy was 86.32%, Kappa coefficient was 0.82, and it can provide a basis for the prevention and remedies of lettuce deficiency and the scientific management of nutrient solutions.

Host feces, olfactory beacon guiding aggregation of intestinal parasites Gasterophilus pecorum (Diptera: Gasterophilidae).

The aim of this study was to identify the aggregation sites and transmission characteristics of Gasterophilus pecorum, the dominant pathogen of endangered equines in desert steppe. Therefore, we tested with a four-arm olfactometer the olfactory response of the G. pecorum adults to the odors that have a great impact on their life cycle, and also investigated the occurrence sites of the adults in the area where the Przewalski's horse (Equus przewalskii) roam frequently during the peak period of G. pecorum infection. The results of four-directional olfactory test showed that the fresh horse feces had a stronger attraction rate on both male (50.4%) and female flies (38.2%). Stipa caucasica, the only oviposition plant where G. pecorum lay eggs, had a better attraction effect on females than that on males. And the attraction rates of S. caucasica to G. pecorum females in the early growth stage (Stipa I) and mid-growth stage (Stipa II) were 32.8% and 36.8%, respectively. In addition, the two-directional olfactory test showed that the attraction rate of males to fresh horse feces (68.90%) was higher than that to Stipa II (31.10%), and females also showed similar olfactory responses. Moreover, in our field investigation, 68.29% of G. pecorum adults were collected from around the horse feces. The results of laboratory test and field investigation implied that the location mechanism of G. pecorum aggregation for mating is related to the orientation of horse feces. The horse feces and the vicinity are the key contamination areas of G. pecorum, and it is also the areas where horses are seriously infected with G. pecorum. Those fresh feces, which gather abundant information about the host, naturally had the greatest chance of contacting with the host; G. pecorum adults create the opportunity to enter directly into the host's mouth and infect the host by laying eggs on S. caucasica, which is the most favorite plant of the host in this area. These characteristics are one of the main reasons why G. pecorum has become the dominant species under the condition of sparse vegetation in desert steppe.

Evaluation of root distribution and nitrate leaching in sugarcane, Erianthus, and their intergeneric hybrid at new planting

Nitrate-nitrogen leaching from farmland has adverse effects on drinking water and environmental conservation in tropical and subtropical island regions such as the Southwest Islands of Japan. Sugarcane is widely grown in these areas, and it is necessary to increase the nitrogen use efficiency of this crop to reduce nitrogen leaching. Studies on nitrogen utilization in this species have focused on yield potential and fertilizer management; however, there have been only a few breeding attempts. The relationship between root system characteristics and nitrogen utilization is also unclear, while improvement of nitrogen utilization using Erianthus can be expected because of its unique root system characteristics. In the present study, nitrogen leaching and root system characteristics of sugarcane × Erianthus intergeneric hybrid and parental genotypes were investigated using a lysimeter to verify the possibility of improving nitrogen utilization characteristics. Nitrogen leaching was significantly lower under the parental Erianthus from the early growth stage, while it was significantly lower in the intergeneric hybrid in the mid-growth stage than that in the parental sugarcane. The nitrogen use efficiencies of Erianthus and the intergeneric hybrid were significantly greater than that of sugarcane. Erianthus and the intergeneric hybrid exhibited lower shoot/root ratio and deeper rooting than sugarcane and consumed significant amounts of soil moisture in the deeper layers, suggesting that root mass and deeper rooting may be factors in reducing nitrogen leaching. These results indicate the possibility of improving the nitrogen utilization characteristics of sugarcane by improving its root system characteristics using Erianthus.

Microclimatic conditions and seed cotton yield as affected by sowing time, row orientation and plant spacing under Bt cotton hybrid

Microclimatic modifications are the effective and suitable adaptation strategy to minimize the adverse affect of weather on cotton. In this context, multi-location field experiments were laid out at Bathinda and Faridkot, districts of Punjab during Kharif 2018 with Bt-cotton hybrid RCH 773 BGII and sown at three times i.e., April 30, May 15 and May 30 with two row orientations (North-South: N-S and East-West : E-W) and three plant spacing’s (67.5 cm × 45.0 cm, 67.5 cm × 60.0 cm and 67.5 cm × 75.0 cm) in factorial split plot design and replicated by three times. The results revealed that during the morning (0730 IST) and afternoon time (1430 IST), soil and within-canopy temperature were found higher during emergence and mid growth stages with delayed sowing which reduced the crop growing period and resulted the seed cotton yield to be declined with delayed sowing. On the other hand, morning and afternoon within-canopy relative humidity was recorded higher with delayed sowing during sensitive stages of the crop which provided favorable weather for higher infestation of cotton insect-pest and disease and caused reduction in seed cotton yield at both the study locations. Soil temperature and within-canopy temperature were recorded higher in East-West row oriented, while within-canopy RH was higher under North-South row orientation at both the study regions. During entire growth period of cotton, soil temperature and within-canopy temperature recorded during morning and afternoon hours were found to be increased with increase in plant spacing whereas within-canopy relative humidity during morning and afternoon hours was found to be decreased with increase in plant spacing at both the locations. Among microclimatic parameters, higher variation was observed between sowing dates, while lesser variation was recorded among row orientations and plant spacings.

Effects of simulated hail damage and foliar-applied recovery treatments on growth and grain yield of wheat, field pea, and dry bean crops

Hailstorms can be responsible for significant economic loss to the agricultural sector in Alberta, Canada. Foliar applications of certain fungicides and nutrient blends have been advocated to promote recovery and yield of hail-damaged crops. Proper understanding of different crop and hail-related factors is required for an accurate assessment of hail damage to crops and for evaluations of hail-recovery product claims. This study was undertaken at three locations in Alberta during three growing seasons (2016–2018) to determine the effects of two levels of simulated hail severity at three different crop developmental stages, including the early growth (BBCH 30 for wheat; BBCH 14–16 for pulses), mid-growth (BBCH 39 for wheat; BBCH 60 for pulses), and late growth (BBCH 60 for wheat; BBCH 71 for pulses) stages. Plant growth and yield parameters of wheat (Triticum aestivum L.), field pea (Pisum sativum L.), and dry bean (Phaseolus vulgaris L.) crops were measured. Simulated hail damage led to reductions in height, biomass, NDVI, grain yield, and kernel weight of all three crops. Average yield decreased by 24% and 35% for wheat, 17% and 35% for dry beans, and 37% and 45% for field peas for light and heavy hail severity, respectively. Hail timing was a critical factor influencing the extent of crop damage, with hail damage during the early growth stage leading to a lesser yield reduction compared with hail damage at the mid-growth and late growth stages. Fungicides and nutrient blends applications did not significantly improve crop recovery, grain yield, or kernel weight for any of the crops in this study.

First Report of Beet Curly Top Virus Infecting Industrial Hemp (Cannabis sativa) in Arizona.

Industrial hemp (Cannabis sativa) is an emerging crop in Arizona, with many uses, including fiber, cosmetic products, and health food. In 2020, severe curly top disease outbreaks were observed in several hemp fields in Yuma and Graham Counties, Arizona, where disease incidence and severity were considerably high, up to 100% crop loss occurring in some fields. A wide range of symptoms have been observed at different infection stages and plant growth stages at the time of infection. Early stage symptoms manifest as light green-to-yellowing of new growth, similar to sulfur or micronutrient deficiency, usually combined with older leaves with dark green "blotchy" mosaic mottling overlaying light green chlorosis. Mosaic mottling of older leaves continues into mid-growth stage, and is coupled with more severe yellowing and witch's broom (stunted leaves and shortened internode length of stem) of apical meristematic tissue. Curling and twisting of new leaves has also been observed. Symptoms often appear to be isolated to individual branches, with other branches showing no visual symptoms, often outgrowing and covering affected branches until harvest. Late stage symptoms include severe leaf curling with or without twisting, continued stunting, and necrosis of yellow leaves, resulting in significant yield reduction. Severely affected plants dwarfed by the virus experienced high mortality rates later into the season, most likely attributed to reduced ability to overcome abiotic stress conditions. These symptoms indicated the likelihood of curly top caused by Beet curly top virus (BCTV), which has been recently reported in Colorado (Giladi et al., 2020). Shoots were collected from thirty-eight symptomatic and nine asymptomatic hemp plants from July to August, 2020. Leaves were also collected as positive control from four chili pepper plants with or without curly top symptoms in Cochise County. Genomic DNA was extracted using DNeasy Plant Pro Kit (Qiagen Inc., Valencia, CA) according to the manufacturer's instructions. BCTV-specific primers BCTV1 and BCTV2 were used to detect BCTV following a method by Rondon (Rondon et al., 2016). A 500 bp DNA fragment, indicative of BCTV, was amplified from all symptomatic hemp and chili pepper samples, but not from asymptomatic samples. Sequence analysis of this 500 bp DNA fragment revealed 98.99 % identity with GenBank accession MK803280, which is Beet curly top virus isolate from hemp identified in Western Colorado (Giladi et al., 2020). The full-length genomes of BCTV isolates from hemp and chili peppers were generated with additional primers 328F/945R (620bp), 455F/ 945R (490bp), OutR/ 2213F (1,190bp), 2609R/ 1278R (1,340bp), BCTV2/ 2609R (1,890bp) (Rondon et al., 2016, Strausbaugh et al., 2008). The complete nucleotide sequence (MW182244) from hemp was 2,929 bp and had 99.35% sequence identity with GenBank accession KX867055, which was a Worland strain of Beet curly top virus isolated from an Idaho sugar beet plant (Strausbaugh et al., 2017). Our hemp BCTV genome sequences shared 96.08% identity with the hemp strain of BCTV from Colorado (MK803280) and 99.50% identity with the BCTV isolate (MW188519) from chili pepper identified in this study. BCTV was reported on outdoor hemp in Western Colorado, in 2020 (Giladi et al., 2020). This is the first report of BCTV in Arizona causing curly top of industrial hemp in the field. In Arizona, BCTV is widespread on many agronomic crops including chili peppers and spread primarily by the phloem-feeding beet leafhoppers: Circulifer tenellus (Hemiptera: Cicadellidae) (Bennett, 1967). Due to the wide distribution of beet leafhoppers and abundant range of host plants for the virus, BCTV may become one of the most yield-limiting factors affecting the emerging industrial hemp production systems in Arizona.

Effect of drought length on the performance of cabbage (Brassica oleracea var capitata) in the forest-savannah transition zone, Ghana

The drought tolerance of Cabbage, Brassica oleracea var capitata (Oxylus) was investigated by subjecting the initial, development and mid growth stages to varying drought lengths of 5, 7, 9, 11, 13 and 15 days in a 7 × 3 factorial experiment to determine the effect of each drought period at each growth stage on growth and yield. Data on number of opened leaves/plant, leaf area index (LAI), leaf chlorophyll content, head size and yield/ha were recorded due to drought effect at each growth stage. Analysis of variance at 5% probability level indicated that drought periods of 5–15 days at the initial stage significantly reduced number of leaves, LAI and head size. Significant reduction in chlorophyll content and yield were due to drought beyond 11 and 5 days respectively at the initial stage. Number of opened leaves increased significantly with increasing drought length at the development stage; critical at 11 days drought. Significant reductions in LAI at the development and mid stages were critical at 11 and 9 days respectively whilst chlorophyll content was significantly reduced at both the development and mid stages by all drought periods. Significant reduction in head size at the development and mid stages was critical at 7 days. Generally, the drought tolerance of cabbage increased from the initial to the mid-stage. To maintain economic yields of cabbage in the forest-savannah transition zone, the initial and development stages could tolerate drought stress up to 5 days whilst the mid stage could tolerate up to 7 days.

Quantifying the contribution of biophysical and environmental factors in uncertainty of modeling canopy conductance

Canopy conductance (Gc) is a crucial variable in accurately estimating latent heat flux and evapotranspiration (ET) over vegetated surfaces. Gc is highly dependent on the plant species and the surrounding environment; consequently, it is difficult to accurately simulate Gc using a simplified universal model. A number of empirical parameters and functional forms have been introduced to appropriately account for various plant species. This study examines the efficacy of a widely adopted Gc model and Penman-Monteith (PM) approach over a selection of vegetated sites in arid and semi-arid regions of China to investigate the factors that influence the accuracy of the model. Comprehensive micrometeorological and land surface monitoring datasets from eight eddy covariance (EC) stations in the Hei and Hai river basins were used for our analysis. ET calculated from a widely used surface conductance and PM method was compared with EC measurements, and the residuals of the comparison were analyzed with factors potentially influencing the residuals using their correlations and regression coefficients to identify factors accounting for the errors caused via Gc. The results showed that the vapor pressure deficit and the maximum stomatal conductance (gsm) are most significantly correlated with uncertainties in Gc for all plant species. The Gc error vs. vapor pressure deficit exhibited distinctive sensitivities among the C3 crop/grass, C4 crop and C3 tree groups. The maximum stomatal conductance showed variation with the leaf age, reaching the maximum level in the mid-growth stage for most types of plant species except for coniferous trees. The residuals increased with radiation levels for sites covered with trees, which appeared to be caused by the decreasing trend of the stomatal conductance vs. radiation at high radiation levels for trees. Finally, we propose potential methods of improving canopy conductance models based on our error analysis.

Studies on water uptake and heat status of cherry root under water-saving measures

This study investigated the effect of water-saving measures on the spatiotemporal variability of water absorption and thermal state in roots. It is crucial for understanding the process of water use of plants and its importance in the functioning of hydro-ecosystem. The spatiotemporal patterns of δ18O in the water from lower soil profile and the ratio of water absorbed by cherry roots from the soil at different depths and irrigation water, under four different treatment (CK, T1, T2, T3, i.e. 100 % of designed irrigation quota + no water-saving measures, 85 % of designed irrigation quota + no measures of water preservation, plastic film mulching and mulching by branches cut-off from the trees) were analyzed in the cherry orchard by measuring the stable oxygen isotopes (δ18O) in the soil moisture, sap in the xylem stem and in the irrigation water (local groundwater) under drip irrigation conditions during 2015−2016. The obtained outcomes show that the amount of δ18O in soil water is closely related to the changes in the irrigation, water-saving measures and soil moisture content during the growth stage of cherry trees. Irrigation significantly promotes the water absorption by cherry roots from soil water in the shallow layers. In the early growth stage of cherry trees, as the cherry grows, water absorption by cherry roots mainly comes from irrigation water. In the mid growth stage of cherry trees, if water-saving measures or adequate irrigation is employed, the contribution of soil water and irrigation water increases. In their mid-growth process, cherry trees mainly absorb water from the soil at a depth of 30−60 cm and irrigation water. Water-saving irrigation is beneficial to the utilization of irrigation water. After the harvest time, the cherries entered the late growth stage in August and September, where the cherries grow slowly and the amount of water required is low. At the same time, an increase in the temperature results in higher soil temperatures. Therefore, based on comprehensive consideration of the patterns of cherry water uptake and the characteristics of soil temperature during the cherry growth period. The best water regulation and management was 100 % of design irrigation quota and plastic film mulching at the fruit growth stage; 85 % of design irrigation quota and plastic film mulching at the postharvest stage; 85 % of design irrigation quota and no mulching should be applied at the end of growth stage.

Assessment of anomalies and effects of climate change on reference evapotranspiration and water requirement in pistachio cultivation areas in Iran

Climate change is a major challenge for the agricultural sector. In this study, the effects of climate change on reference evapotranspiration and water requirement in pistachio cultivation areas in Iran were investigated. From observational data (1980–2018) as the baseline period and simulated data (2020–2080) under RCP, scenarios were used for the future period based on MarkSimGCM database. The results showed that in the baseline period, there were an increasing trend for annual air temperature and a decreasing trend for annual precipitation in pistachio cultivation areas in Iran. Reference evapotranspiration and water requirement will deviate in the future under the RCP8.5 and RCP4.5 scenarios from the baseline period. So, under RCP8.5 and RCP4.5, reference evapotranspiration will increase by 112 and 79 mm, respectively, compared with the baseline period. The highest water requirement in pistachio trees will occur in mid-growth stage (June–September), so that, under RCP8.5 and RCP4.5, the increase will be 114 and 82 mm, respectively, compared with the baseline period. From the spatial distribution, the highest amount of reference evapotranspiration and water requirement will occur in lowland and southern half of pistachio cultivation areas, especially in Kerman province. Increasing air temperature and consequently increasing reference evapotranspiration and increasing water requirement will be a danger alarm for water resources in the dry cultivation pistachio areas in Iran. According to projections, the shortage of water will intensify in the future, and as a result of climate change, many parts of the pistachio cultivation areas in Iran will be lost.

Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery

BackgroundAutomated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm.ResultsBoth selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds.ConclusionsWith the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing.