Phytoplasma testing in sweet pepper in Moldova

Phytoplasma infects a wide variety of crops, causing considerable economic losses. About half of the vegetable crops damaged by phytoplasma belong to the Solanaceae family including tomato, eggplant and pepper which play an important role in the agriculture economics of Moldova. Our previous research confirmed the presence of ‘Candidatus Phytoplasma solani’ (16SrXII-A subgroup) in tomatoes and also identified insect vectors. In this communication, we present for the first time in Moldova the results of molecular diagnosis of association of ‘Ca. P. solani’ in 4% of the analyzed sweet pepper samples. ‘Ca. P. asteris’ group was absent in the pepper field.

Climate Change and Informal Education in the Opinion of Forest Users in Poland

Climate change affects various aspects of the economy, agriculture, economics, and politics, including forestry. There is more and more talk about the real impact of the effects of climate change. This paper presents the results of a survey on the perceptions of two groups, foresters and recreational forest users, about climate change and its impacts on forested areas; 130 foresters and 146 recreational forest users participated in the survey (total n = 276). The survey was conducted from April to November 2019 and consisted of three parts. The first part included questions about the demographic characteristics of the respondents (gender, age, education, place of residence), the second part focused on the respondents’ views on climate change and its implications for forest ecosystems, and the third part focused on informal forest education and its relationship to climate change. The results of our study indicated that progressive climate change affecting forest ecosystems is clearly felt by the professional group related to forests such as foresters, and to a lesser extent by people using forests for tourism and recreation. According to foresters, the effects of climate change on forest areas include rapid changes in weather patterns and more frequent insect infestations. On the other hand, people resting in forests mainly observe the lack of snow cover and occurrence of drought. Informal forest education insufficiently covers the topic of climate change. Thus, our study can help guide informal education towards topics related to climate change and the need for sustainable forest use.

Measuring total factor productivity in agriculture: a bibliometric review

PurposeTotal factor productivity (TFP) has become a prominent concept in agriculture economics and policy over the last three decades. The main aim of this paper is to obtain a detailed picture of the field via bibliometric analysis to identify research streams and future research agenda.Design/methodology/approachThe data sample consists of 472 papers in several bibliometric exercises. Citation and collaboration structure analyses are employed to identify most important authors and journals and track the interconnections between main authors and institutions. Next, content analysis based on bibliographic coupling is conducted to identify main research streams in TFP.FindingsThree research streams in agricultural TFP research were distinguished: TFP growth in developing countries in the context of policy reforms (1), TFP in the context of new challenges in agriculture (2) and finally, non-parametric TFP decomposition based on secondary data (3).Originality/valueThis research indicates agenda of future TFP research, in particular broadening the concept of TFP to the problems of policy, environment and technology in emerging countries. It provides description of the current state of the art in the agricultural TFP literature and can serve as a “guide” to the field.

Comparison between the Naïve Bayes and Hierarchical Clustering to Classify The Global Landslide Catalog for the Prediction of the Landslide.

Machine Learning has been used since long to identify the features of a given datasets that are important for the prediction. Landslides are complex events taking place in the various regions of the world. It is the movement of the debris, soil or rocks from an upper plane in downward direction. Identification of the features that are used for the Landslide involves consideration of various categories of parameters. Present paper studies about the performance comparison between a supervised algorithm Naïve Bayes and unsupervised algorithm Hierarchical Clustering. Naïve Bayes is a non parametric supervised algorithm that can be used for the forecasting purposes in the field of Agriculture, Economics, Aviation etc, whereas Hierarchical Clustering is used to partition the available instances of a dataset into optimal homogeneous groups on the basis of the similarities between the datapoints. The present paper draws a comparison between the accuracy of the Naïve Bayes and Hierarchical Clustering for the prediction of the Landslide dataset. The dataset used is the Global Landslide Catalog that has important parameters like date, location coordinates, country, trigger of the event, continent etc. Before the implementation of both the algorithms, reduction of the parameters is carried out using subset evaluation of the parameters and considering only the most important.

Using MLP Neural Networks to Detect Late Blight in Brazilian Tomato Crops

The food quality is a major issue in agriculture, economics, and public health. The tomato is one the most consumed vegetables in the world, having a significant production chain in Brazil. Its culture permeates many economic and social sectors. This paper presents a technological approach focused on enhancing the quality of tomatoes crops. The authors developed intelligent computational strategies to support early detection of diseases in Brazilian tomato crops. Their approach consorts real field experiments with inexpensive computer-aided experiments based on pattern recognition using neural networks techniques. The recognition tasks aimed at the identification foliage diseases named late blight, which is characterized by the incidence of brown spots on tomato leaves. The identification method achieved a hit rate of 94.12%, by using digital images in the visible spectrum of the leaves.