Plants In Plots Research Articles

Research Progress on Mechanization Technology and Equipment for Cassava Field Production

Cassava is one of the world’s staple crops and an important economic and food crop for one billion people. It holds significant value in food, feed, and industrial raw materials. However, cassava production is faced with the following problems: agricultural machinery and agronomy are not integrated, work efficiency is not high, it is not suitable for large-scale planting plots, and there is a lack of large-scale equipment, automation, and intelligent cassava production equipment. This study reviews research on cassava planting agronomy and mechanized production technologies and equipment, including planting, stem–tuber separation, and harvesting, both globally and in China. The shortcomings are analyzed, and recommendations are provided for current mechanized cassava production to accelerate the development of intelligent equipment and strengthen the shortcomings. Meanwhile, we used typical and extensive Chinese planting and harvesting machines as case studies. The future trends in cassava production are forecasted, underscoring the importance of advancing global mechanized technology and equipment for cassava production to enhance its efficiency, competitiveness, and modernization.

Modelling Differently Defined Dominant Stand Diameters of Monospecific Forest Plantations

Quadratic mean diameter is a widely used stand parameter present in the stand inventory summaries, while the top stand diameter is rarely reported in the literature, mainly in relation to dominant stand height. Since the dominant stand height is usually determined from the tree height-diameter curve of the stand, it is important how the top tree assemblage, used to estimate dominant diameter, is defined. The main objective of our study was to assess the bias between differently defined dominant diameter estimates for monospecific plantations of various species, to model the dominant diameter as a function of quadratic mean diameter and other relevant stand variables, and to estimate its goodness-of-fit in predicting dominant diameter and dominant height.We used data records gathered in sample plots in monospecific plantations of four tree species: Scots pine, Black pine, black locust and hybrid black poplar. We calculated the quadratic and arithmetic mean diameters of the 20% thickest trees in the plots, and the quadratic and arithmetic mean diameters of the trees, whose number corresponded to the 100 thickest trees per hectare. For each dataset, we analyzed the range and the distribution of the relative deviations calculated for each pair of dominant diameter estimates. For the Black pine plantations, regression models were developed for the two dominant diameter definitions, whose values differed most. Their goodness-of-fit was assessed from model efficiency and error statistics. The same model derivation procedure, applied to the Scots pine data, was followed by substitution of the predicted dominant diameter into a height-diameter model to assess the goodness-of-fit of the dominant height predictions.The differences between the arithmetic and quadratic means, estimated from the same subsample of trees, did not exceed 2% in all cases. However, dominant stand diameters calculated as averages of differently defined largest tree collectives differed by as much as 35%. Regardless of its definition, the dominant stand diameter was adequately predicted by a function of the quadratic mean diameter alone or considering stand basal area as a second predictor. The models showed very good accuracy of model efficiency above 0.92, average absolute error below 8%, with 90% of the relative errors less than 15%. The predicted dominant diameter value can be used in a height-diameter model to estimate with confidence the dominant stand height of a monospecific forest plantation, allowing the forecast of the stand attributes based on dominant trees when only average stand variables are known.

Extraction of Winter Wheat Planting Plots with Complex Structures from Multispectral Remote Sensing Images Based on the Modified Segformer Model

As one of the major global food crops, the monitoring and management of the winter wheat planting area is of great significance for agricultural production and food security worldwide. Today, the development of high-resolution remote sensing imaging technology has provided rich sources of data for extracting the visual planting information of winter wheat. However, the existing research mostly focuses on extracting the planting plots that have a simple terrain structure. In the face of diverse terrain features combining mountainous areas, plains, and saline alkali land, as well as small-scale but complex planting structures, the extraction of planting plots through remote sensing imaging is subjected to great challenges in terms of recognition accuracy and model complexity. In this paper, we propose a modified Segformer model for extracting winter wheat planting plots with complex structures in rural areas based on the 0.8 m high-resolution multispectral data obtained from the Gaofen-2 satellite, which significantly improves the extraction accuracy and efficiency under complex conditions. In the encoder and decoder of this method, new modules were developed for the purpose of optimizing the feature extraction and fusion process. Specifically, the improvement measures of the proposed method include: (1) The MixFFN module in the original Segformer model is replaced with the Multi-Scale Feature Fusion Fully-connected Network (MSF-FFN) module, which enhances the model’s representation ability in handling complex terrain features through multi-scale feature extraction and position embedding convolution; furthermore, the DropPath mechanism is introduced to reduce the possibility of overfitting while improving the model’s generalization ability. (2) In the decoder part, after fusing features at four different scales, a CoordAttention module is added, which can precisely locate important regions with enhanced features in the images by utilizing the coordinate attention mechanism, therefore further improving the model’s extraction accuracy. (3) The model’s input data are strengthened by incorporating multispectral indices, which are also conducive to the improvement of the overall extraction accuracy. The experimental results show that the accuracy rate of the modified Segformer model in extracting winter wheat planting plots is significantly increased compared to traditional segmentation models, with the mean Intersection over Union (mIOU) and mean Pixel Accuracy (mPA) reaching 89.88% and 94.67%, respectively (an increase of 1.93 and 1.23 percentage points, respectively, compared to the baseline model). Meanwhile, the parameter count and computational complexity are significantly reduced compared to other similar models. Furthermore, when multispectral indices are input into the model, the mIOU and mPA reach 90.97% and 95.16%, respectively (an increase of 3.02 and 1.72 percentage points, respectively, compared to the baseline model).

Mobile Web Application for Durian Orchard Management and Geospatial Data Visualization Using Deep Learning

Durian, a globally popular fruit, is primarily exported by Thailand, making it the foremost contributor to the world market. Nevertheless, there remains a notable absence of a comprehensive platform or application catering to both consumer tourists and businesses seeking domestic purchases. Prior research has highlighted several shortcomings, notably the inability of existing applications to provide location-based search functionality or automatically identify durian plantation plots from digital photographs. Consequently, this study proposes the development of a mobile web application aimed at managing, processing, and visualizing geospatial data pertaining to orchards and durian plantations. Through the integration of mobile technology, geospatial technology, and machine learning, the research endeavours to address these deficiencies. The findings indicate promising results, particularly in the accurate classification of durian plantations using four machine learning algorithms: convolutional neural network (CNN), support vector machine (SVM), random forest, and k-nearest neighbor (KNN). Among these algorithms, CNN exhibited the highest accuracy, achieving a value of 95%, with precision, recall, and f-measure values of 95.55%, 94.44%, and 94.97%, respectively.

Pengaruh Dosis Phospor Dan Pupuk Cair Pada Tanaman Bunga Kol (Brassica Oleracea Var. Botrytis L)

Cauliflower is a vegetable that has commercial value and excellent prospects for public consumption. According to the Central Statistics Agency of the Republic of Indonesia, cauliflower production has decreased from 2019 to 2021, therefore it is necessary to improve maintenance, one of which is by providing Phosphorus fertilizer to increase productivity. This will increase the rate of photosynthesis to produce high productivity and the correct dose of liquid organic fertilizer (POC) to maximize growth in the vegetative and generative phases thereby increasing the yield of cauliflower plants. This research aims to determine the effect of giving various doses of Phosphorus and POC fertilizers on the growth and yield of cauliflower plants. The research was carried out in the village, Girilayu, Matesih, Karanganyar from February to May 2024. Using the Complete Randomized Block Design (RAKL) method consisting of 2 treatment factors and 3 replications. The first factor is the dosage of Phosphorus (P) fertilizer P1: 100 kg/ha, P2: 150 kg/ha, P3: 200 kg/ha. The second factor is the dose of POC (C), C1: 4 cc/L, C2: 8 cc/L, C3: 12 cc/L. The results research showed (1) Giving various doses of Phospor fertilizer had a very real effect on fresh stover, dry stover, plant diameter, plant weight per plant, and crop weight per plot. However, it does not have a real effect on plant height and number of leaves. (2) Giving various doses of POC had a very real influence on fresh stover, dry stover, plant diameter, and plant weight per plant had a real influence on crop weight per plot and had no real influence on plant height and number of leaves. (3) The interaction between treatments had a very real influence on fresh stover, dry stover, and plant weight per plant, and had no real influence on plant height, number of leaves, plant diameter, and weight per plant plot. (4) The highest results were obtained in the branch pruning treatment 3 weeks after planting with an NPK fertilizer dose of 200 kg/ha, namely a plant weight of 11,997.4 grams per plot (26,644 kg/ha).

Identification and characterization of the causal agent of powdery mildew on native salmonberry and thimbleberry plants in British Columbia

Salmonberry (Rubus spectabilis) and thimbleberry (Rubus nutkanus) are important native berry plants for Indigenous communities and restoration sites in the coastal region of British Columbia (BC). Compared to cultivated berry plants, information on the diseases impacting native berry plant species is limited. In late August 2022, powdery mildew symptoms and signs were observed on field plots of salmonberry and thimbleberry plants at the Agassiz Research and Development Centre, BC. The disease symptoms progressed until the end of September. The morphology of conidiophores and conidia when examined under a compound and scanning electron microscope showed they matched with those of Podosphaera aphanis. DNA from diseased foliage was subjected to PCR amplification with primers representing the internal transcribed spacer (ITS) region of rDNA followed by sequencing. The corresponding sequences from both hosts showed >99% similarity to P. aphanis in GenBank and grouped with several accessions of P. aphanis following phylogenetic analysis. In artificially inoculated trials conducted in the greenhouse, powdery mildew symptoms developed within 3−4 weeks post-inoculation and P. aphanis isolates from salmonberry and thimbleberry were cross-pathogenic to each host. To our knowledge, this is the first study characterizing the powdery mildew pathogen infecting salmonberry and thimbleberry and demonstrating cross-pathogenicity to both hosts. The outcomes from this study provide insight into the biology of the powdery mildew pathogen infecting native Rubus spp.

Extraction of Arbors from Terrestrial Laser Scanning Data Based on Trunk Axis Fitting

Accurate arbor extraction is an important element of forest surveys. However, the presence of shrubs can interfere with the extraction of arbors. Addressing the issues of low accuracy and weak generalizability in existing Terrestrial Laser Scanning (TLS) arbor point clouds extraction methods, this study proposes a trunk axis fitting (TAF) method for arbor extraction. After separating the point cloud data by upper and lower, slicing, clustering, fitting circles, obtaining the main central axis, filtering by distance, etc. The canopy point clouds are merged with the extracted trunk point clouds to precisely separate arbors and shrubs. The advantage of the TAF method proposed in this study is that it is not affected by point cloud density or the degree of trunk curvature. This study focuses on a natural forest plot in Shangri-La City, Yunnan Province, and a plantation plot in Kunming City, using manually extracted data from a standardized dataset of samples to test the accuracy of the TAF method and validate the feasibility of the proposed method. The results showed that the TAF method proposed in this study has high extraction accuracy. It can effectively avoid the problem of trunk point cloud loss caused by tree growth curvature. The experimental accuracy for both plots reached over 99%. This study can provide certain technical support for arbor parameter extraction and scientific guidance for forest resource investigation and forest management decision-making.

Fires in the face of climate change: Indicators of fire disturbance in heath areas – Inference from military training lands

Global warming significantly affects the frequency and intensity of wildfires in many fire-prone areas of the world and fire disturbance regimes are changing rapidly. Alongside this, controlled burning is often considered or implemented as an alternative method for nature protection. Here, we studied the post-fire secondary succession in dry heath habitat located in military training grounds to recognise the impact of fire on the effectiveness and rate of ecosystem recovery. We focus particularly on identifying indicator species for a given successional phase of Central European dry heath habitats and assessing their value for dating the last fire disturbance. The research involved 60 vegetation patches (plots of 25 m2), which were assigned to four post-fire age classes, namely: 1–5 years (Class A), 6–10 years (Class B), 11–15 years (Class C), and >15 years (Class D). In each study plot, species diversity and coverage of lichens, bryophytes and vascular plants were examined in addition to the physicochemical properties of the soil substrate. Cryptogams and vascular plants clearly differ in terms of the secondary succession pattern; specific sets of cryptogams correspond well to particular post-fire classes and are therefore good determinants of the post-fire succession stage. Spontaneous succession of plant vegetation eventually leads to complete recovery of the heath in a relatively short time. Nevertheless, great vegetation dynamics in the first years after a fire disturbance may result in seemingly different directions of succession. Post-fire classes did not differ noticeably in terms of soil properties, it follows that the effects of fire on soil conditions are negligible; though, a significant upward trend was observed for exchangeable form of K throughout the succession process. Our results indicate that sporadic fires reduce the undesirable overgrowth of heathlands or psamophilous grasslands and generally have a little negative side-effect on the ecosystem. The revealed succession patterns and defined sets of species characteristic for subsequent post-fire age classes are applicable to dating fire disturbances, regardless of whether the fire was planned or spontaneous.

Initial Diagnostic of Fungal Pathogens in Coconut Plantation Plot Area in Bukit Kor, Terengganu, Malaysia

Initial Diagnostic of Fungal Pathogens in Coconut Plantation Plot Area in Bukit Kor, Terengganu, Malaysia

Evaluation of Radio Frequency Identification Power and Unmanned Aerial Vehicle Altitude in Plant Inventory Applications

In the business of growing and selling ornamental plants, it is important to keep track of plants from nursery to distribution. Radio Frequency Identification (RFID) technology provides an easier tracking method for inventories of plants by attaching tags with unique identifiers. Due to the vast area of most nurseries, there is a need to have an efficient method of scanning RFID tags. This paper investigates the use of drones and RFID, specifically, the effects of RFID reader power and flight altitude on tag counts. The experimental setup evaluated three RFID reader power levels (15 dBm, 20 dBm, and 27 dBm), three flight altitudes (3 m, 5 m, and 7 m), the number of passes (one or two), and two plant types (‘Green Giant’ arborvitae and ‘Sky Pencil’ holly). For RFID tags, four types were used (L5, L6, L8, and L9), with two antenna types (dog-bone and square-wave) and two attachment types (loop-lock and stake). For each power level, the UAV was flown to three different altitudes of 3 m, 5 m, and 7 m above the ground. At each altitude, two scan passes were performed at a constant speed of approximately 1.5 m/s. Each plot of plants (two in total) was randomly tagged with a total of 40 RFID tags per plot. Field data were collected from September to December 2023 (on a total of eight dates). The data showed that a power level of 15 dBm and an altitude of 3 m yielded a tag count of 53%, while counts of 34% and 16% were achieved at 5 m and 7 m, respectively. At 20 dBm and an altitude of 3 m, the count accuracy across all tag types and both plants was 90%. When the altitude was increased to 5 m and 7 m, tag-count accuracy dropped to 75% and 33%, respectively. The highest count accuracy was observed at 27 dBm and an altitude of 3 m, with a reading accuracy of 98%. Tag types L6 and L9 performed better at any power level and altitude, while L5 and L8 performed well at a higher power level and lower altitude. In this experiment, canopy properties (size and shape) had no effect on the number of tags read. This study aimed to evaluate the RFID power and UAV altitude achieving the highest accuracy in scanning the RFID tags. Furthermore, it also assessed the effects of plant growth on the scanning efficiency and accuracy of the system.

Evaluating and modeling the efficacy of Stipagrostis plumosa for the phytoremediation of petroleum compounds in crude oil-contaminated soil

ABSTRACT This study focused on using Stipagrostis plumosa for phytoremediation to eliminate total petroleum hydrocarbons (TPHs) and heavy metals (HMs) like cadmium (Cd), chromium (Cr), lead (Pb), and nickel (Ni) from oil-contaminated soil. Conducted over six months at a field-scale without artificial pollutants, soil samples were analyzed using gas chromatography‒mass spectrometry (GC‒MS) for TPHs and inductively coupled plasma-optical emission spectroscopy (ICP‒OES) for HMs. Results after six months revealed that plots with plants had significantly higher average removal percentages for TPHs (61.45%), Cd (39.4%), Cr (46.1%), Pb (41.5%), and Ni (44.2%) compared to the control group (p <0.05). Increased microbial respiration and bacteria populations in planted plots indicated enhanced soil microbial growth. Kinetic rate models aligned well with the first-order kinetic rate model for all pollutants (R2 >0.9). Overall, the study demonstrates that S. plumosa can effectively reduce TPHs and HMs in oil-contaminated soil, making it a promising option for pollutant absorption.

Characteristics and risk assessment of arsenic contamination in a lead and zinc plant area in Yunnan Province

Taking a lead and zinc plant plot in Yunnan as the research object, the land use status and planting types of the region were investigated, the original enterprise distribution area and crop growing area of the plot were defined, and 67 surface soil samples and 14 columnar soil samples were systematically arranged in the plot for monitoring. Combined with the monitoring results and analysis, it is concluded that there is no definite basis that the high arsenic content in soil in the region is caused by the enterprise production, and the high arsenic content in the region may be caused by the background value. Based on the monitoring and investigation results, the risk assessment of the area was carried out. The results showed that the non-carcinogenic risk and carcinogenic hazard index of arsenic exceeded the prescribed acceptable level when the area was developed as the second type of construction land, and soil remediation should be carried out. At the same time, the collaborative distribution monitoring of regional crops showed that the arsenic content of crops was low, which met the requirements of food standards, possibly because the cultivated crops did not have strong adsorption and enrichment effects on the arsenic occurrence form in the regional soil.

Contrasting change patterns of lignin and microbial necromass carbon and the determinants in a chronosequence of subtropical Pinus massoniana plantations

The continuous input and degradation of organic matter is crucial for soil organic carbon (SOC) sequestration during stand development. However, it remains unclear how the relative contribution of plant residues and microbial necromass to SOC changes with stand age and what are the determinants of the change pattern. The space-for-time substitution (chronosequence) approach was used to establish plots of Pinus massoniana plantations at five stand ages (6, 13, 29, 38, and 57 years). Soil samples were collected in each plot to determine the relative contribution of plant residues and microbial necromass to SOC storage by using SOC-normalized content of lignin and microbial necromass carbon (MNC). Lignin increased initially from young to mature but then decreased in over-mature plantation (R2 = 0.54, P < 0.01) and was dominated by vanillyl phenols (79 %–89 %). MNC decreased at first from young to mature but then increased in over-mature plantation (R2 = 0.50, P < 0.05) and it was dominated by fungal necromass carbon (54–63 %). Partial least squares path modeling revealed that stand age directly positively affected lignin and MNC, and indirectly maintained their change patterns through tree growth and microbial attributes. Stand age had a total positive effect (0.57) on lignin and a total negative effect (−0.54) on MNC. Regression analysis showed that the ratio of SOC to total nitrogen (SOC/TN) was positively correlated with lignin (R2 = 0.76, P < 0.01) yet negatively correlated with MNC (R2 = 0.58, P < 0.01). Additionally, the diameter at breast height, pH, and the ratio of fungal to bacterial biomass together with SOC/TN regulated the patterns of lignin and MNC with stand age. Overall, this study highlights the opposite pattern of lignin and MNC and emphasizes the importance of considering both tree growth and microbial attributes when developing SOC sequestration strategies with stand age.

Agronomic Suitability for Oil Palm Growing in Uganda

The ever-increasing demand for vegetable oils with its products in Uganda, calls for new areas to be put under oil palm cultivation. A study was conducted to investigate the agronomic suitability of oil palm cultivation in six areas located within a 30 km radius off-shore, on the island and more than 30 km radius off-shore on the mainland. The experiment was superimposed on 45 &amp;times; 45 m plots in oil palm adaptive trial plantations established in 2008 in Bugiri, Mayuge, Buvuma, Mukono, Kibaale and Masaka districts. The experiment was laid out in a randomized complete block design with three replications. Data on number of oil palm bunches, bunch weight and yield was collected while climatic data (rainfall, temperature, relative humidity and radiation) was acquired from meteorological stations near the experimental locations. Rainfall data analyzed and dry season duration suggested that Mayuge, Masaka, Mukono, Buvuma and Kibaale were moderately favorable for oil palm growing, while temperature and radiation data indicated that all study locations were suitable for oil palm growing. Furthermore, based on relative humidity, Buvuma, Mayuge and Bugiri were suitable for oil palm cultivation but Kibaale, Masaka and Mukono were moderately suitable. Oil palm yield varied significantly (P &amp;lt; 0.05) across locations. The yield was much greater in Mukono (17.7 t/ha/yr) followed by Buvuma (13.8 t/ha/yr) and Kibaale (12.9 t/ha/yr) then Mayuge (10.7 t/ha/yr), Bugiri (10.2 t/ha/yr), and Masaka (10.3 t/ha/yr). The significantly high yield of oil palm observed in Mukono was attributed to the high rainfall received in this location. Based on research findings, Mukono, Masaka, Bugiri, Buvuma, Kibaale and areas within the 30 km radius of Lake Victoria are agronomically suitable for oil palm cultivation.

The Organically Cultivation Techniques of Curly Chili in Handayani Farming Group, Sidokaton Village, Gisting District

Abstract: This community service aims to guide chili farmer groups, especially the Handayani farmer group, to start cultivating chili plants organically on their farming land.There were three stages of the implementation method used, namely increasing knowledge, increasing skills and evaluation. Increasing knowledge was given through lectures to provide knowledge about the cultivation of organic chili plants, botanical pesticides and production of compost made from Azolla pinnata; followed by skills improvement by making botanical pesticides, propagating Azolla pinnata and making compost, demonstration plots of chili plants and then evaluation. Guidance for demonstrations on how to cultivate organic chilies started with planting seeds on the ridge, applying azolla compost to the planting holes, then planting chilies and applying botanical pesticides. Farmers planted chilies in beds that had been made as many as 192 plants which were divided into 6 beds. Afterwards, there were carried out the maintenance activities, controlling pests and diseases with the application of botanical pesticides, and harvesting along with post-harvest handling. This activity would be monitored. Monitoring was carried out once a month, especially at the end of each group of practiced cultivation activities. As an indicator of success, the results of the activity evaluation showed an increase in knowledge of organic farming concepts of 55% and an increase in skills of 85%. Keywords: Sidokaton village, Pesticide Organic, Azolla pinnata

Effect of soil conditions on the state of tree plantations in the green zone of Astana

Creation and maintenance of a green zone around the capital of the Republic of Kazakhstan, Astana, is complicated not only by sharply continental climate, but also by soil salinity. In this regard, plants characterized by resistance to drought, frost and soil salinity are chosen for landscaping of these areas. Avoiding or minimizing the impact of negative consequences of afforestation of saline areas can be achieved by understanding the mechanisms by which forest trees and shrubs are able to survive in such conditions. The purpose of the study was to investigate the salt content in the soil under forest crops in the green zone of the Astana city and their influence on the condition of woody plants. The objects of the study were artificial plantations of the dwarf elm and maple ash planted in 2015. The main research method used was the establishment of sample plots in healthy, weakened and dead plantations. Maple ash was found to be more tolerant to higher salt content in soil than the dwarf elm. It was found that high content of toxic ions and low concentration of cations in the soil are related to the condition of crops, with more toxic type of salinization prevailing in the areas with weakened and dead plantations. According to the research results, it can be seen that the soil of healthy plots is characterized by a higher content of Ca2+, Mg2+ and Na+ cations than the soil of the weakened plantations. HCO3–, Сl– and SO4 2– anions are present in significant amounts in the soil under weakened crops, and the content of НСО3– ions is much higher in the areas with dead trees, which may be one of the reasons for severe soil salinization and vegetation death. It was also determined that sulfate-chloride-hydrocarbonate salinization and magnesium-calcium chemistry with toxicity greater than 0.17 mg-eq НСО3– negatively affected plant health. Sulfate-chloride type of salinization prevails on the healthy plots, in the weakened and dead plantations – the more toxic sulfate-chloride-hydrocarbonate type is found.

Preliminary Evaluation of Drip Irrigation System Performance in Watermelon Cultivation

In watermelon production, irrigation is the most significant factor in achieving good yields. Properly selecting an irrigation system may provide successful irrigation for excellent crop quality and productivity. Drip irrigation is a popular approach for this purpose. Drip irrigation can successfully deliver water to plants based on the amount required by the crop. However, this irrigation system's performance should be investigated to guarantee that it can run in the best possible conditions. This study aimed to assess the preliminary version of drip irrigation for watermelon crops. The field experiments were conducted on a watermelon planting plot at MARDI Kundang Selangor. The drip irrigation system performance was evaluated based on hydraulic parameters such as coefficient uniformity (CU), emission uniformity (EU), coefficient of variation (CV), and emitter flow variation (EFV) according to ASAE standards. The results indicate that CU is in excellent classification, with a CU efficiency greater than 91 per cent. The EU value was 86%, showing a reasonable variety. However, the CV value was 0.1, indicating a marginal classification. Meanwhile, the emitter flow variation (EFV) is 19% which is considered acceptable. The results of this preliminary study showed that the performance of this drip irrigation system is satisfactory, as the hydraulic parameters evaluated met the ASAE standard's minimum classification requirements. The distribution of discharge data was similarly shown to have no significant difference using a one-sample t-test, with a p-value of 0.096.

Method for extracting corn planting plots in the Loess Plateau region based on the improved HRNet and NDVI time series

Method for extracting corn planting plots in the Loess Plateau region based on the improved HRNet and NDVI time series

Effect of elevated atmospheric CO2 on spectral reflectance of coffee leaves of plants cultivated at face facility

Climate change impacts are stressing many economic sectors worldwide, including agriculture, increasingly hindering efforts to meet human needs. Dioxide carbon is one of the main greenhouse gases and it affects directly the crop production. The objective of this study was to evaluate if established remote sensing indices could detect the effects of elevated atmospheric CO2 on the leaves of coffee (Coffea arabica L.) plantation growing under field conditions. Plots of coffee plants were exposed to ambient air (~390 µmol CO2 mol-1) and elevated CO2 (~550 µmol CO2 mol-1) at the free air CO2 enrichment (FACE) experiment. The statistical design was the completely randomized blocks with six replicates per treatment (ambient CO2 and elevated CO2), with 10m-diameter plots. Coffee leaves were spectrally characterized by reflectance spectra on their adaxial surfaces and seven vegetation indices were calculated from reflectance data: chlorophyll normalized difference index (Chl NDI), normalized difference nitrogen index (NDNI), normalized difference vegetation index (NDVI), photochemical reflectance index (PRI), pigment specific simple ration indices for chlorophyll a (PSSRa) and chlorophyll b (PSSRb), and structural independent pigment index (SIPI). NDNI was a sensitive indicator of the atmospheric CO2 effects on coffee leaves. NDVI, PSSRa and PSSRb were sensitive to estimate the effect of elevated CO2 only under drought conditions. These indices identify the effect of CO2 when a long period with high precipitation deficit stressing the leaves occurred. Chl NDI, PRI and SIPI were not sensitive to atmospheric CO2.

Foliar Lesions Induced by Pestalotiopsis arengae in Oil Palm (O × G) in the Colombian Southwest Palm Zone.

In Colombia, plantings with the oil palm hybrid between Elaeis oleifera × Elaeis guineensis, known as O × G hybrid, have increased due to its tolerance to bud rot. Despite this, different degrees of foliar necrosis, chlorosis, and leaf blight have been reported in some cultivars; therefore, this work aimed to diagnose this problem. We visited plantation plots with palms exhibiting the mentioned symptoms and collected 21 samples of affected tissues in different disease states. The affected tissues were examined and seeded in a culture medium. Pathogenicity tests were performed and the isolates were characterized by culture and morphological and molecular features. Curvularia, Colletotrichum, Phoma, and 25 Pestalotiopsis-like fungi were isolated from the foliar lesions. In the pathogenicity tests, the symptoms observed in the field were reproduced with MFTU01-1, MFTU12, and MFTU21 isolates, which were identified at the species level through a sequence analysis of three genes (ITS, TUB2, and TEF1-α) as Pestalotiopsis arengae with an identical level of 99% based on the results of BLAST and phylogenetic tree analyses. The remaining 22 Pestalotiopsis-like non-pathogenic isolates were identified as species of Neopestalotiopsis and Pseudopestalotiopsis. The direct association of P. arengae with the disease was confirmed via molecular detection in affected tissues in 15 of 21 samples collected for this evaluation. This is the first report of P. arengae as the causal agent of foliar lesions in O × G hybrid oil palm in Colombia.