Areas Of Agricultural Research Research Articles

Diagnosing Medicinal Plants and their Fungal Diseases Using Deep Learning Models

Today, with the development of technology, most manual methods have been replaced by automated computer systems for human convenience. Plant identification and disease classification are two major areas of agricultural research and are aimed at introducing computerized systems instead of manual methods. Millions of plant species are in the world and play a significant role in human life. Among all the types of plants, medicinal plants play an essential role in the traditional medical field because herbal plants can heal humans. Currently, there is a reactivation of interest in herbal medicines at the global level, and conventional medicine is now accepting the use of medicines and their products once they have been scientifically validated. To achieve this goal, we evaluated the performance of two common pre-trained deep learning models (VGG19 and ResNet50) and compared their accuracy levels Finally, the system can estimate some performance metrics such as accuracy and error rate for both algorithms and compare the algorithms based on accuracy in the form of graph. These results are promising, as they show that machine-learning techniques could be used for the early identification of medicinal plants. Our algorithm has been able to achieve a high level of accuracy in the classification of medicinal plants in training and test sets, making it a potentially valuable tool for fast and accurate diagnosis in clinical environments. Keywords—VGG19, ResNet50, Traditional medicine, Medicinal Plant Identification and Disease Classification

Early evaluation of use of fermented chicken manure products in practice of apple nutrient management

According to the Green Deal efforts, the importance and relevance of organic fertilization will increase in the near future. Therefore, the investigation of the effects of different organic fertilizers on soil productivity and nutrient supply is a priority area of agricultural research. Organic fertilizer experiment was conducted in an eight-year-old apple (Malus domestica Borkh.) orchard at Debrecen-Pallag. In the trial Pinova cultivar was used. In this study, two different fermented chicken manure products were added to the soil (in 20 cm depth) to test their effects on soil nutrient status, plant uptake and fruit quality. It was found that the applied treatments slightly increased the pH and nutrient levels in almost all cases, but significant effect was not observed in all treatments compared to the control. Leaf nutrient contents (N, P, K, Ca and Mg) were measured in the experiment. Leaf nutrient status was not affected by the fermented chicken manure treatments. However, used treatments had strong effects on the fruit characteristics and inner parameters, such as fruit diameter and Brix value. Moreover, it was established that the applied organic fertilizers increased the yield significantly.

Plant Disease Detection using Machine Learning Algorithms

One of the most exiting research areas in agriculture is the identification of plant diseases from images. Today, the term 'machine learning' is a hot topic. It is possible to apply the principles of machine learning to a wide range of fields. plant disease detection can also benefit from machine learning and deep learning. For the detection and classification of plant diseases, this paper uses several types of plant leaves as a model.

Field Application of ZnO and TiO2 Nanoparticles on Agricultural Plants

Engineered nanoparticles (ENPs) have potential application in precision farming and sustainable agriculture. Studies have shown that ENPs enhance the efficiency of the delivery of agrochemicals and thus, have the potential to positively affect the environment, thereby improving the growth and health of the crops. However, the majority of the research on the effects of ENPs on plants and in agricultural applications have been limited to controlled laboratory conditions. These conditions do not fully consider various aspects inherent to the growth of agricultural plants in fields under changing weather and climate. Some of the most investigated ENPs in the agricultural research area are ZnO nanoparticles (ZnO NPs) and TiO2 nanoparticles (TiO2 NPs). ZnO NPs have the potential to increase crop production and stress resistance, mainly by the slow release of Zn ions to crops. Unlike ZnO NPs, TiO2 NPs have less well-understood means of action, and are generally considered as plant growth promoter. This mini review presents information compiled for ZnO and TiO2 NPs, their influence on agricultural plants with emphasis on particularly effect on plant growth, nutrient distribution and pollution remediation under field conditions. It is concluded that in order to gain a broader perspective, more field studies are needed, particularly multigeneration studies, to fully understand the effects of the ENPs on agricultural plants’ growth and improvement of their health.

Enhancing Drought Tolerance of Wheat (Triticum aestivgum L.) Through Foliar Application of Proline and L-Triptophan

Limited water availability is one of the important abiotic factor affecting yield of wheat crop. Exogenous application of osmolytes is an important factor in reducing the stress due to water shortage. Keeping in view the role of proline and L-tryptophan (L-TRP) in stress alleviation, a study was carried out at the agricultural research area of the University College of Agriculture and Environmental Sciences, The Islamia University of Bahawalpur, Pakistan, during winter 2015-2016 to evaluate the impact of foliar applied proline and L-TRP on growth and photosynthetic efficiency of wheat grown under limited moisture supply. Drought stress was applied in three levels (I1 = control, I2 = drought stress at tillering stage and I3 = drought stress at grain filling stage) while, foliar application of proline and L-TRP was done in six levels [T1 = control; T2 = proline (10 mM); T3 = proline (20 mM); T4 = L-TRP (10-4 M); T5 = L-TRP (10-4 M) + proline (10 mM) and T6 = L-TRP (10-4 M) + proline (20 mM)]. The experimental results revealed that growth and photosynthetic efficiency of wheat were decreased due to reduced water supply. However, exogenously applied proline and L-TRP considerably ameliorated the effect of drought stress. Combined application of L-TRP (10-4 M) and proline (20 mM ) showed better results and induced tolerance to drought stress, in comparison with other treatments. Foliar application of proline and L-TRP also enhanced the photosynthetic rate which might be related with the improved photosynthetic pigments. Overall, exogenously applied proline and L-TRP mitigated the adverse effects of moisture deficit on growth and photosynthetic efficiency of wheat crop.
 

Evapotranspiration Estimation with Small UAVs in Precision Agriculture.

Estimating evapotranspiration (ET) has been one of the most critical research areas in agriculture because of water scarcity, the growing population, and climate change. The accurate estimation and mapping of ET are necessary for crop water management. Traditionally, researchers use water balance, soil moisture, weighing lysimeters, or an energy balance approach, such as Bowen ratio or eddy covariance towers to estimate ET. However, these ET methods are point-specific or area-weighted measurements and cannot be extended to a large scale. With the advent of satellite technology, remote sensing images became able to provide spatially distributed measurements. However, the spatial resolution of multispectral satellite images is in the range of meters, tens of meters, or hundreds of meters, which is often not enough for crops with clumped canopy structures, such as trees and vines. Unmanned aerial vehicles (UAVs) can mitigate these spatial and temporal limitations. Lightweight cameras and sensors can be mounted on the UAVs and take high-resolution images. Unlike satellite imagery, the spatial resolution of the UAV images can be at the centimeter-level. UAVs can also fly on-demand, which provides high temporal imagery. In this study, the authors examined different UAV-based approaches of ET estimation at first. Models and algorithms, such as mapping evapotranspiration at high resolution with internalized calibration (METRIC), the two-source energy balance (TSEB) model, and machine learning (ML) are analyzed and discussed herein. Second, challenges and opportunities for UAVs in ET estimation are also discussed, such as uncooled thermal camera calibration, UAV image collection, and image processing. Then, the authors share views on ET estimation with UAVs for future research and draw conclusive remarks.

Accelerating evidence-informed decision-making for the Sustainable Development Goals using machine learning

The United Nations Sustainable Development Goal 2 (SDG 2) is to achieve zero hunger by 2030. We have designed Persephone, a machine learning model, to support a diverse volunteer network of 77 researchers from 23 countries engaged in creating interdisciplinary evidence syntheses in support of SDG 2. Such evidence syntheses, whatever the specific topic, assess original studies to determine the effectiveness of interventions. By gathering and summarizing current evidence and providing objective recommendations they can be valuable aids to decision-makers. However, they are time-consuming; estimates range from 18 months to three years to produce a single review. Persephone analysed 500,000 unstructured text summaries from prominent sources of agricultural research, determining with 90% accuracy the subset of studies that would eventually be selected by expert researchers. We demonstrate that machine learning models can be invaluable in placing evidence into the hands of policymakers. Evidence syntheses produced from the scientific literature are important tools for policymakers. Producing such evidence syntheses can be highly time- and labour-consuming but machine learning models can help as already demonstrated in the health and medical sciences. This Perspective describes a machine learning-based framework specifically designed to support evidence syntheses in the area of agricultural research, for tackling the UN Sustainable Development Goal 2: zero hunger by 2030.

Examination on Advanced Machine Learning Techniques for Plant Leaf Disease Detection from Leaf Imagery

Agriculture is the backbone of countries as it feeds people across the globe. With respect to agriculture in India, the total geographical area for agriculture in India is 328 million hectares. The new area sown is 142 million hectares. 190.8 million hectares is the gross cropped area. It reveals the significance of agriculture and its related research. With technological innovations emerged, now it is the era where there is scope of increasing the research towards precision agriculture (PA). PA is the agriculture where technology-driven procedures are followed to do right things at right time. This will increase productivity and saves time, money and effort. Agricultural plat leaf disease detection is an important research area in agriculture that is related to PA. In this context, image processing and machine learning techniques assume significance to capture live leave imagery from fields and detect diseases. Automatic detection of disease based on agricultural plant leaves has its utility in real world agricultural applications. This is the motivation behind taking up this research. This paper throws light on different kinds of plant diseases, various advanced machine learning and image processing methods to detect plant diseases. The innovations in technologies lead to automatic detection of diseases even from remote places and take necessary steps to ensure that the agricultural crops are healthy and yield desired harvest. This survey also provides important research gaps that will help in further research towards realizing PA.

Crop Yield Pred iction using Regression Model

This research is done to find out the production dependability of crop with various physical circumstances. The prediction can also be done of a crop yield by using the model of regression and it is mainly discussed in this paper. Machine learning is an emerging research area in Agriculture, particularly in crop yield analysis and prediction. There are some complex data which are tough to decode or find by everyone, the strategies of machine learning can be used in this scenario and automatically the valuable underlining pattern can be accessed. Various complex decision-making activities can be performed when the feature of machine learning will enable the knowledge and patterns which are unseen about any problem. The future events can also be predicted. In the growing season as possible, a farmer is focused on conceptualizing how much yield they except. Like many other regions, the amount of agricultural data is increasing at the daily source. This paper aims to predict crop yield on the collected agricultural dataset. The regression analysis model is used to test the accuracy and effective predictions of the rice crop yield in India. Linear regression is used to establish a relationship between various environmental variables like temperature, rainfall, etc and the crop yield. It is important to measure the possible production of rate of crop and the farmers will be benefitted by the result of this prediction. As financial impact is attached of the farmers with the yield production, the research will support them to avoid any loss. The accuracy of the prediction through regression model is also observed in this research paper.

Effects of Foliar Applications of Boron at the Early Vegetative Stages on Plant Growth Parameters of Maize

Boron is an important micronutrient for growth and development of crop plants. Plant species differ in their requirement of boron for growth. This study was conducted to determine the effect of boron application on the plant characteristics of maize during early leaf stages. The experiments were conducted in Tel – Kaliş agricultural research area at the Mustafa Kemal University in 2015 and 2016 growing seasons. The field experiments were arranged in a split plot design with three replications. Four boron dosages (control, 4, 6 and 8 mg/m2) were applied at three growing stages (V2, V4 and V2V4 (at V2 and V4 stages in two equal parts) as foliar spray. The results revealed that the effects of foliar application of B were positive but statistically insignificant on plant characteristics. Further researches should be conducted for suitable boron application time at different growth stages of maize.

Gene Editing and Crop Improvement Using CRISPR-Cas9 System.

Advancements in Genome editing technologies have revolutionized the fields of functional genomics and crop improvement. CRISPR/Cas9 (clustered regularly interspaced short palindromic repeat)-Cas9 is a multipurpose technology for genetic engineering that relies on the complementarity of the guideRNA (gRNA) to a specific sequence and the Cas9 endonuclease activity. It has broadened the agricultural research area, bringing in new opportunities to develop novel plant varieties with deletion of detrimental traits or addition of significant characters. This RNA guided genome editing technology is turning out to be a groundbreaking innovation in distinct branches of plant biology. CRISPR technology is constantly advancing including options for various genetic manipulations like generating knockouts; making precise modifications, multiplex genome engineering, and activation and repression of target genes. The review highlights the progression throughout the CRISPR legacy. We have studied the rapid evolution of CRISPR/Cas9 tools with myriad functionalities, capabilities, and specialized applications. Among varied diligences, plant nutritional improvement, enhancement of plant disease resistance and production of drought tolerant plants are reviewed. The review also includes some information on traditional delivery methods of Cas9-gRNA complexes into plant cells and incorporates the advent of CRISPR ribonucleoproteins (RNPs) that came up as a solution to various limitations that prevailed with plasmid-based CRISPR system.

Levantamento de solos utilizando geoestatística em uma área de experimentação agrícola em Petrolina-PE

Due to the importance of accurate information on soils for agricultural production, quantitative techniques to estimate classes and/or soil properties have been developed to increase these maps quality. This manuscript was developed to map soils of an agronomic experimental area using geostatistical tools to assist in the allocation and management of experiments. The study area is located in an agricultural experimental area of Federal University of São Francisco Valley, Petrolina County, Pernambuco state, Brazil. Mapping the agricultural area consisted of a soil profile and a sampling grid with 51 georeferenced points. At these points the soil samples were collected at 0-0.2 m and 0.4-0.6 m soil depths that corresponded to A and B soil horizons, respectively. Soil samples were evaluated for physical (soil texture) and chemical analysis. The spatial dependence of soil attributes was described using semivariograms and maps were interpolated by ordinary kriging. The results showed that the predominant soils in the area were Ultisol with a cohesive eutrophic horizon, Ultisol with a cohesive dystrophic horizon, and Ultisol with a sandy cohesive dystrophic horizon. The geostatistical proved to be an important tool supporting the ultra-detailed pedological mapping of the agricultural research area.

Synthesis of Deep-Red La2MgGeO6:Mn4+ Phosphors for Indoor Plant Cultivation By Polymerized Complex Method

Phosphor converted white light-emitting diodes (LEDs) has been extensively practiced in lamps and displays as a next generation white light source, because it offer more benefits, including high luminescence efficiency, a long lifetime, compactness, and low environmental load. Recently, in the area of agricultural research, the LEDs have been actively studied as the light source for “indoor plant cultivation” [1]. Indoor plant cultivation is to grow plants within a controlled environment, meaning filtered air, steady temperature, and special growth media. The ideal environment permits stable supply of vegetables without the influence from the outside. The stable supply can hold the price increase, resulting from short supply, due to drought, loss caused by cold weather, and other disasters. Therefore, indoor cultivate is expected as a solution approach to the global food problem. Artificial light used in indoor plant cultivation is one of important factors. The growth of plant tissues requires three emitting colors: (1) blue light around 450 nm affecting the growth of chlorophyll, flower-bud formation, leaf morphogenesis, and phototropism, (2) red light around 660 nm helping budbreak and internodal elongation, and (3) far-red light around 730 nm promoting photosynthesis. Among them, far-red light is especially significant for preparation of higher-value vegetables, because it prevents aggravation of eating quality and the bitterness caused by the excessive growth of vegetables [2]. Convert-type white LEDs built up with blue LED emitting around of 450 nm and yellow Y3Al5O12:Ce3+ (YAG:Ce3+) have been studied as a prospective light source used in indoor plant cultivation, because of their suitable emission for the culture of plants. Currently, the combination of this the LED and the blue light excitable red LED emitting round 660 nm has been reported as a candidate for the next artificial light source redeemed red light [3]. However, the intensity of its far-red emission is insufficient. Novel blue light excitable deeper-red phosphors, therefore, are required in order to develop an artificial light source in indoor plant cultivation, achieving effective growth of plants and preparation of higher-value vegetables. Mn4+ doped phosphors are excited by blue light and exhibit red emission corresponding to the 4A2 → 4T2 and 2E → 4A2 transitions of Mn4+. La2MgGeO6:Mn4+ [4] is a good candidate for a deep-red phosphor of an artificial light source in indoor plant cultivation, because of their suitable excitation and emission spectra. In addition, it is obtained by easy synthetic condition compared to other Mn4+ doped phosphors such as CaAl12O9:Mn4+ [5], 3.5MgO・0.5MgF2・GeO2:Mn4+ [6]. La2MgGeO6:Mn4+ phosphors are usually synthesized by a conventional solid state reaction method. The method has two drawbacks: the high temperature (1350℃) needed in the heating condition and the concentration quenching, owing to Mn4+ions inhomogeneously distributed. In this study, overcome the drawbacks, we synthesized La2MgGeO6:Mn4+ phosphors by polymerized complex method. Polymerized complex method exploits polymerization between ethylene glycol and citric acid. They form the soluble metal-citric acid complexes. The complexes are immobilized in a rigid polyester network almost in the molecular level, reflecting the original solution [7]. Owing to this mechanism, this method has the advantage; the each ion is homogeneously distributed. Therefore, it is expected to be obtained the La2MgGeO6:Mn4+ samples homogeneously distributed Mn4+, resulting in high efficiency, at low temperature [8]. The luminescence properties of synthesized samples by polymerized complex method are compared with that prepared by the conventional solid state reaction method. [1] N. Yeh and J.-P. Chung, Renew. Sustain. Energy Rev., 2009, 13, 2175. [2] Y. Mori amd M. Takatsuji, Shokubutsu Kojo no tachiagekata・susumekata [How to establish/ forward the LED plant factory] (in japanese), (Nikkan kogyo shinbun, Tokyo), 2013. [3] M. Takatsuji and Y. Mori, Shokubutsu Kojo [the LED plant factory] (in japanese), (Nikkan Kogyo Shinbun, Tokyo), 2011. [4] K. Seki, K. Uematsu, T. Ishigaki, K. Toda, and M, Sato, J. Ceram. Process. Res., 2011, 12, s286. [5] T. Murata, T. Tanoue, M. Iwasaki, K. Morinaga and, T. Hase, J. Lumin., 2005, 114, 207. [6] W. Low, Phy. Rev., 1957, 105, 793. [7] M. Kakihana, J. Sol-Gel Sci. Tech., 1996, 6, 7. [8] T. Isobe, Development and Applications of Nanophosphors (in japanese), (CMC pub., Popular Ed.), 2012.

Impact of Training on Learners’ Development and Performance: A Case Study with Reference to International Agricultural Research Institute, Hyderabad

The study reports the results of impact of training on Learners' performance with reference to the training programs conducted at the International Agricultural Research Institute. Hyderabad.The sixindependent variablesApplication of Training, Job knowledge Enhancement, Analytical Tools, Resources, Motivation, New Competencies with one dependent variable Learners' improved Performance based on the knowledge gained from the training measured. The institute conducts in-house training programs on application of advanced crop improvement methods twice a year for the benefit of the researchers in India and some Sub- Saharan African countries working in the area of agricultural research in general and crop improvement in particular.The data was collected using a post-training survey of 200 trainee participants including 60 women and 140 men, from eight such training courses spread over three years to assess the training program impact on Learners' performance. The evaluation was done measuring the effect of the six above said independent variables of the training programs on the dependent variable performance. The descriptive analysis, correlationtechniques, inferential statistics, and multiple regression analysiswere applied to arrive atconclusions. To measure the reliability of thescale used for this study, and internal consistencies of the survey questionnaire, the reliabilitystatistics Cronbach's alpha and Spearman-Brown split-half reliability were used. The overall Cronbach alpha is 0.81 whereas Spearman-Brown static was 0.75and the Cronbach alpha value ranged from 0.64 to 0.71and Spearman Brown-Split Half reliability varied from 0.61 to 0.75 for all the 6independent and one dependent variable. The study concluded that the training impacts on Learner's performance positively and it also depends on the application of the learned techniques into regular use in their job assignments and regular work. Training evaluation is a vital aspect of any sort of training and its effect on the person in performing his/her job assignments more efficiently. Feedback is essential for the training organizers to know how they are progressing, also is crucial to the learner's confidence too. People's commitment to learning relies heavily on confidence and a belief that the learning is achievable, the way that training assessments are designed and delivered, and results presented back to the learners. Training evaluation is the systematic assessment of the worth or merit of some object or aim and the act of judging whether or not the activity to be evaluated is worthwhile in terms of set criteria.The rapid advancement in science, modernization of science and technological changes envisaged agricultural research sector to look for new methods of training in the area of crop improvement (1). This is essential to change the dimensions of the training needs to cope up with those changes and to keep with the pace of rapid science development. There were very limited work has been reported on addressing the impact of training in the area of agricultural research using modern techniques. This study focussed on evaluating the impact of training program using six independent variables and its impact Learner's performance using post-training survey. Training program evaluation is must for all training programs as the evaluation tools and methodologies help to determine the training program effectiveness and instructional interventions. Though the training evaluation is important, there is evidence that evaluations of training programs are often inconsistent or missing (2)-(5). The main reasons for inadequate evaluations include: insufficient budget allocated; insufficient time allocated; lack of expertise; blind trust in training solutions; or lack of methods and tools (6).The evaluation of training is complex and involve several complex factors to measure the training interventions in respect of learning, transfer, and organizational impact. These factors are associated with the dynamic and rapid changes in technology knowhow and compelled the organizations like international agricultural research institute to envisage frequent changes in the structure, instructional methods and content of the trainings they

An Empirical Analysis of the Training Program Characteristics on Training Program Effectiveness: A Case Study with Reference to International Agricultural Research Institute, Hyderabad

The study explores the relationship between the training program characteristics, the six independent variables with dependent variable training program effectiveness with special reference to International Agricultural Research Institute, Hyderabad. The institute conducts training programs on application of advanced crop improvement methodologies twice in a year for the benefit of the researchers in India and some Sub-Saharan African countries working in the area of agricultural research for crop improvement. The data collected using a post-training survey of 200 trainee participants including 60 women and 140 men, from eight such courses spread over three years to assess the training program effectiveness and to know if the objectives of the course were achieved. The evaluation was done measuring the effect of the six independent variables of the training program—lectures, practical, demos, field visit, methodology, and instructor ability on the dependant variable training program effectiveness (in brief effectiveness) on the participant. We have applied the descriptive analysis, correlation techniques, inferential statistics, and multiple regression analysis to arrive conclusions. To measure the reliability of the scale used for this study, and internal consistencies of the survey questionnaire, the reliability statics Cronbach’s alpha and Spearman-Brown split-half reliability were used. The overall Cronbach alpha is 0.88 whereas Spearman-Brown static was 0.94 and the Cronbach alpha value ranged from 0.68 to 0.82 for all the 6 independent and one dependent variables. The study observed some mean differences in some variables which are significant to effect training program effectiveness.

Research Article: Extraction of Small Spatial Plots from Geo-Registered UAS Imagery of Crop Fields

Experimental crop fields often contain large numbers of small spatial plots that require separate analysis. It can be difficult to precisely locate these plots in high-resolution imagery of the fields gathered by Unmanned Aircraft Systems (UAS). This prevents UAS imagery from being applied in High-Throughput Precision Phenotyping and other areas of agricultural research. If the imagery is accurately geo-registered, then it may be possible to extract plots from the imagery based on their map coordinates. To test this approach, a UAS was used to acquire visual imagery of 5 ha of soybean fields containing 6.0 m2 plots. Sixteen artificial targets were set up in the fields before flights to be used as Ground Control Points (GCPs) for geo-registration. Geo-registration accuracy was quantified based on the horizontal Root Mean Squared Error (RMSE) of targets used as checkpoints. Twenty test plots were extracted from the geo-registered imagery. Plot extraction accuracy was quantified based on the percentage of the desired plot area that was extracted. It was found that using four GCPs minimized the horizontal RMSE and enabled a plot extraction accuracy of at least 70%, with a mean plot extraction accuracy of 92%. Future work will focus on enhancing the plot extraction accuracy through additional image processing techniques so that it becomes sufficiently accurate for all practical purposes in agricultural research and potentially other areas of research.

Reference gene validation for quantitative RT-PCR during biotic and abiotic stresses in Vitis vinifera.

Grapevine is one of the most cultivated fruit crop worldwide with Vitis vinifera being the species with the highest economical importance. Being highly susceptible to fungal pathogens and increasingly affected by environmental factors, it has become an important agricultural research area, where gene expression analysis plays a fundamental role. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) is currently amongst the most powerful techniques to perform gene expression studies. Nevertheless, accurate gene expression quantification strongly relies on appropriate reference gene selection for sample normalization. Concerning V. vinifera, limited information still exists as for which genes are the most suitable to be used as reference under particular experimental conditions. In this work, seven candidate genes were investigated for their stability in grapevine samples referring to four distinct stresses (Erysiphe necator, wounding and UV-C irradiation in leaves and Phaeomoniella chlamydospora colonization in wood). The expression stability was evaluated using geNorm, NormFinder and BestKeeper. In all cases, full agreement was not observed for the three methods. To provide comprehensive rankings integrating the three different programs, for each treatment, a consensus ranking was created using a non-weighted unsupervised rank aggregation method. According to the last, the three most suitable reference genes to be used in grapevine leaves, regardless of the stress, are UBC, VAG and PEP. For the P. chlamydospora treatment, EF1, CYP and UBC were the best scoring genes. Acquaintance of the most suitable reference genes to be used in grapevine samples can contribute for accurate gene expression quantification in forthcoming studies.

Row Spacing and Inter Row Spacing Effects on Some Agro-Physiological Traits of Two Common Bean (Phaseolous vulgaris L.) Cultivars

An experiment was conducted to evaluate the effects of row spacing (RS) and inter row spacing (IRS) on some agro-physiological traits of 2 common bean (Phaseolous vulgaris L.) cultivars, at the agricultural research area, Faculty of Agriculture, University of Urmia, Iran during 2003. Experimental design was a factorial split-plot based on randomized complete block design with 3 replications. The 30, 45 and 60 cm of RSs constituted the main plots and IRSs of 5, 10 and 15 cm and cultivars of Derakhshan and Naz (determinate and indeterminate, respectively) were allocated to the sub plots. Obtained results showed that different RSs and IRSs affected the Grain Yield (GY). The highest GY was recorded at 30 cm RS (4.191 t ha(-1)) and 5 cm IRS (3.926 t ha(-1)). The number of pods plant(-1) (NPP), the number of grains plant(-1) (NGP) and grain weight plant(-1) (GWP) increased as a result of a decrease in the RS and IRS. In addition, it was observed that by increasing the RS and IRS the first pod trait had higher height. However, the Grain Protein Content (GPC) was not affected by the IRS and RS changes. On the other hand, Naz (indeterminate) had higher capability for the grain yield than Derakhshan (determinate) cultivar.

Experimental Design and Systematic Monitoring

Abstract David Finney has enjoyed a long and distinguished career, both as an academic statistician and in various areas of agricultural research and medical statistics. He tells Helen Joyce about his career, and shares his views on the state of statistics today.

Realizing the promise of IVF in cattle—an overview

The in vitro-produced embryo could play a central role in dairy and beef production systems because of its potential role in genetic selection strategies and crossbreeding schemes, and because it can be integrated into reproductive management strategies for improving pregnancy rates in herds with low fertility. The promise attendant upon use of in vitro-produced embryos is not being fully realized, however. Indeed, there are important technical limitations to their production that reduce the desirability of in vitro-produced embryos because of increased costs, sub-optimal embryonic and fetal survival, and offspring that are occasionally abnormal. Most technical problems associated with in vitro production of embryos can be overcome through research. Among the requirements for a successful research program will be renewed emphasis on conducting embryo transfer trials to determine effects of modifications to embryo production protocols on pregnancy rates and fetal development. Given the promise of in vitro embryo technologies, there is an urgent need for a concerted and sustained investment in research to improve these technologies. Developing a consensus that the study of embryo technology is one of the most important areas of agricultural research should be both an individual and organizational priority.