Farm-level economics of innovative tillage technologies: the case of no-till in the Altai Krai in Russian Siberia

Evaluation of energy use and carbon dioxide emissions from the consumption of fossil fuels and agricultural chemicals for paste tomato cultivation in the Bursa region of Turkey

This study was aimed to determine the fossil fuel consumption, energy use, and carbon dioxide (CO₂) emissions in per unit production area (ha) considering the petroleum products (PP) directly used and the chemical fertilizers and pesticides for the cultivation of paste tomatoes in open-field conditions in Bursa region of Turkey. The primary data of the study consisted of data collected by making face-to-face surveys with the producers of paste tomatoes in the Bursa region. The direct energy inputs and CO₂ emissions related to diesel fuel and lubricant oil consumptions of engines of agricultural tractors for cultivation operations and the indirect energy inputs and CO₂ emissions related to the manufacturing of chemical fertilizers and plant growth regulators used for plant nutrition and pesticides used for plant protection were determined for paste tomato cultivation. A total of 288.6 L diesel fuel and 0.067 L lubrication oil are consumed per hectare when using tools and machinery in paste tomato production. A total of 408 kg of chemical fertilizers and 15.5 kg of pesticides are used per hectare in paste tomato production in the Bursa region of Turkey. A total of 2343.45 MJ/ha and 2700.5 MJ/ha indirect energy is used in the application of chemical fertilizers and pesticides, respectively. A total of 792.43 kg and 0.189 kg CO₂ is released as a result of diesel fuel and lubricant oil consumptions. For the production of one kilogram of paste tomato, 2.68 grams (g) diesel fuel and lubricating oil, 175.02 kilojoules (kJ) of energy is consumed, and 15.88 g CO₂ is released in the Bursa region of Turkey.

Do ARMA Models Provide Better Gap Filling in Time Series of Soil Temperature and Soil Moisture? The Case of Arable Land in the Kulunda Steppe, Russia

The adoption of climate-smart agriculture requires the comprehensive development of environmental monitoring tools, including online observation of climate and soil settings. They are often designed to measure soil properties automatically at different depths at hour or minute intervals. It is essential to have a complete dataset to use statistical models for the prediction of soil properties and to make short-term decisions regarding soil tillage operations and irrigation during a vegetation period. This is also important in applied hydrological studies. Nevertheless, the time series of soil hydrological measurements often have data gaps for different reasons. The study focused on solving a problem of gap-filling in hourly time series of soil temperature and moisture, measured at the 30 cm depth using a weighted gravitation lysimeter station while meteorological data were recorded simultaneously by a weather station. The equipment was installed in the Kulunda Steppe in the Altai Krai, Russia. Considering that climate conditions affect soil temperature and moisture content directly, we did a comparative analysis of the gap-filling performance using the three imputation methods—linear interpolation, multiple linear regression, and extended ARMA (p,q) models with exogenous climatic variables. The results showed that, according to the minimum of the mean absolute error, ARMA (p,q) models with optimally selected order parameters, and an adaptive window, had some advantages compared to other single-imputation methods. The ARMA (p,q) model produced a good quality of gap-filling in time series with the mean absolute error of 0.19 °C and 0.08 Vol. % for soil temperature and moisture content, respectively. The findings supplemented the methodology of hydrological data processing and the development of digital tools for the online monitoring of climate and soil properties in agriculture.

Who cares what parents think or do? Observational learning and experience-based learning through communication in rice farmers' willingness to adopt sustainable agricultural technologies in Hubei Province, China

Sustainable agricultural technologies are of great significance in fully utilizing agricultural resources and promoting agricultural production. However, the adoption rates of these technologies are often characterized as low in rural areas in China. To figure out the potential salient determinants of rice farmers' willingness to adopt sustainable agricultural technologies, this paper, by employing the multivariate probit model and ordered probit model, particularly and firstly explores the roles of observational learning and experience-based learning through communication from parents within the household on rice farmers' willingness to adopt these technologies. Results show that there are strong complementarities and substitutabilities between sustainable agricultural technologies that rice farmers are willing to adopt, and that observational learning and experience-based learning through communication within the household do have pronounced effects on rice farmers' willingness to adopt some sustainable agricultural technologies and on their intensive use intentions. Therefore, while formulating policies to improve the adoption rates and adoption intensity of these technologies, relevant government agencies should take the complementarities and substitutabilities between sustainable agricultural technologies as well as observational learning and experience-based learning through communication from parents into consideration.

Duration analysis on the adoption behavior of green control techniques

Based on field survey data of 366 traditional households (THs) and 364 family farms (FFs) from Huang-Huai-Hai Plain, a discrete-time cloglog model for parameter estimation was constructed to reveal factors that affect the two types of farms' duration from the awareness to the adoption of green control techniques (GCTs). Differences in the influencing factors affecting the duration of the two types of farmers were also discussed. The research results are as follows. First, the duration from awareness to adoption of GCTs is significantly shorter in FFs than that in THs. Second, a higher degree of education, risk preference, family financial status, perceived ease of use and usefulness of the technique, and extension of media and supervision of agricultural technique extension departments of local governments significantly reduce the duration from awareness to adoption of GCTs by THs and FFs, whereas a male head of household prolongs the duration. Third, the age, farm size, and number of laborers exert different impacts on the duration from awareness to adoption of GCTs by THs and FFs.