Dynamic effects of fiscal and monetary policy instruments on environmental pollution in ASEAN

This study aims to re-examine the impacts of monetary and fiscal policy on environmental quality in ASEAN countries from 1990 to 2019. We utilized the panel and time series NARDL approach to explore the long-run and short-run estimates at a regional level and country level. ASEAN regional-wise analysis shows that contractionary monetary policy reduces the CO2 emissions, while expansionary monetary policy enhances CO2 emissions in the long run. The long-run coefficient further confirms that expansionary fiscal policy mitigates CO2 emissions in ASEAN. The impact of expansionary monetary and fiscal policy on CO2 emissions is positive and significant, while contractionary monetary and fiscal policy have an insignificant impact on CO2 emissions in the short run. ASEAN country-wise analysis also reported the country-specific estimates for the short and long run. Some policies can redesign in light of these novel findings in ASEAN economies

Multicriteria Prediction and Simulation of Winter Wheat Yield Using Extended Qualitative and Quantitative Data Based on Artificial Neural Networks

Wheat is one of the main grain species as well as one of the most important crops, being the basic food ingredient of people and livestock. Due to the importance of wheat production scale, it is advisable to predict its yield before harvesting. However, the current models are built solely on the basis of quantitative data. Therefore, the aim of the work was to create three multicriteria models for the prediction and simulation of winter wheat yield, which were made on the basis of extended quantitative and qualitative variables from field research in the year period 2008–2015. Neural networks with MLP (multi-layer perceptron) topology were used to build the following models, which can predict and simulate the yield on three dates: 15 April, 31 May, and 30 June. For this reason, they were designated as follows: QQWW15_4, QQWW31_5, and QQWW30_6. Each model is based on a different number of independent features, which ranges from 19 to 25. As a result of the conducted analyses, a MAPE (mean absolute percentage error) forecast error from 6.63% to 6.92% was achieved. This is equivalent of an error ranging from 0.521 to 0.547 t·ha−1, with an average yield of 6.57 ton per hectare of cultivated area. In addition, the most important quantitative and qualitative factors influencing the yield were also indicated. In the first predictive range (15 April), it is the average air temperature from 1 September to 31 December of the previous year (T9-12_PY). In the second predictive range (31 May) it is the sum of precipitation from 1 May to 31 May, and in the third (30 June) is the average air temperature from 1 January to 15 April of the year (T1-4_CY). In addition, one of the qualitative factors had a significant impact on the yield in the first phase-the type of forecrop in the previous year (TF_PY). The presented neural modeling method is a specific extension of the previously used predicting methods. An element of innovation of the presented concept of yield modeling is the possibility of performing a simulation before harvest, in the current agrotechnical season. The presented models can be used in large-area agriculture, especially in precision agriculture as an important element of decision-making support systems

Prediction of Protein Content in Pea (<i>Pisum sativum</i> L.) Seeds Using Artificial Neural Networks

Pea (Pisum sativum L.) is a legume valued mainly for its high seed protein content. The protein content of pea is characterized by a high lysine content and low allergenicity. This has made consumers appreciate peas increasingly in recent years, not only for their taste, but also for their nutritional value. An important element of pea cultivation is the ability to predict protein content, even before harvest. The aim of this research was to develop a linear and a non-linear model for predicting the percentage of protein content in pea seeds and to perform a comparative analysis of the effectiveness of these models. The analysis also focused on identifying the variables with the greatest impact on protein content. The research included the method of machine learning (artificial neural networks) and multiple linear regression (MLR). The input parameters of the models were weather, agronomic and phytophenological data from 2016–2020. The predictive properties of the models were verified using six ex-post forecast measures. The neural model (N1) outperformed the multiple regression (RS) model. The N1 model had an RMS error magnitude of 0.838, while the RS model obtained an average error value of 2.696. The MAPE error for the N1 and RS models was 2.721 and 8.852, respectively. The sensitivity analysis performed for the best neural network showed that the independent variables most influencing the protein content of pea seeds were the soil abundance of magnesium, potassium and phosphorus. The results presented in this work can be useful for the study of pea crop management. In addition, they can help preserve the country’s protein security

Interactive Effects of Nitrogen and Potassium Fertilizers on Quantitative-Qualitative Traits and Drought Tolerance Indices of Rainfed Wheat Cultivar

Increasing global food requirements and global warming are two challenges of future food security. Water availability and nutrient management are two important factors that affect high-yield and high-quality wheat production. The main and interactive effects of nitrogen and potassium fertilizers on quantitative-qualitative properties and drought tolerance of an Iranian rainfed cultivar of wheat, Azar-2, were evaluated. Four rates of nitrogen (N0, N30, N60, and N90 kg/ha), along with four concentrations of potassium (K0, K30, K60, and K90 kg/ha), were applied in rainfed (drought stress) and non-stress conditions. The interactive effect of N × K was significant on nitrogen and protein contents of grains at 5% and 1% probability levels, respectively. Different trends of SSI, STI, K1STI, and K2STI indexes were observed with the interactive levels of nitrogen and potassium. The lowest SSI index (0.67) was observed in N30K30, whereas the highest STI (1.07), K1STI (1.46), and K2STI (1.51) indexes were obtained by N90K60 and N90K90. The obtained results could be useful to increase yield and quality of winter rainfed wheat cultivars under drought stress with cool-rainfed areas. N60K30 and N90K60 can be recommended to increase the grain yield and protein content of rainfed wheat under drought stress and non-stress conditions, respectively

Predictions and Estimations in Agricultural Production under a Changing Climate

In the 21st century, agriculture is facing numerous challenges [...

Interactive Effects of Nitrogen and Potassium Fertilizers on Quantitative-Qualitative Traits and Drought Tolerance Indices of Rainfed Wheat Cultivar

Increasing global food requirements and global warming are two challenges of future food security. Water availability and nutrient management are two important factors that affect high-yield and high-quality wheat production. The main and interactive effects of nitrogen and potassium fertilizers on quantitative-qualitative properties and drought tolerance of an Iranian rainfed cultivar of wheat, Azar-2, were evaluated. Four rates of nitrogen (N0, N30, N60, and N90 kg/ha), along with four concentrations of potassium (K0, K30, K60, and K90 kg/ha), were applied in rainfed (drought stress) and non-stress conditions. The interactive effect of N &times; K was significant on nitrogen and protein contents of grains at 5% and 1% probability levels, respectively. Different trends of SSI, STI, K1STI, and K2STI indexes were observed with the interactive levels of nitrogen and potassium. The lowest SSI index (0.67) was observed in N30K30, whereas the highest STI (1.07), K1STI (1.46), and K2STI (1.51) indexes were obtained by N90K60 and N90K90. The obtained results could be useful to increase yield and quality of winter rainfed wheat cultivars under drought stress with cool-rainfed areas. N60K30 and N90K60 can be recommended to increase the grain yield and protein content of rainfed wheat under drought stress and non-stress conditions, respectively

Neural Modeling of the Distribution of Protein, Water and Gluten in Wheat Grains during Storage

An important requirement in the grain industry is to obtain fast information on the quality of purchased and stored grain. Therefore, it is of great importance to search for innovative solutions aimed at the monitoring and fast assessment of quality parameters of stored wheat The results of the evaluation of total protein, water and gluten content by means of near infrared spectrometry are presented in the paper. Multiple linear regression analysis (MLR) and neural modeling were used to analyze the obtained results. The results obtained show no significant changes in total protein (13.13 &plusmn; 0.15), water (10.63 &plusmn; 0.16) or gluten (30.56 &plusmn; 0.54) content during storage. On the basis of the collected data, a model artificial neural network (ANN) MLP 52-6-3 was created, which, with the use of four independent features, allows us to determine changes in the content of water, protein and gluten in stored wheat. The chosen network returned good error values: learning, below 0.001; testing, 0.015; and validation, 0.008. The obtained results and their interpretation are an important element in the warehouse industry. The information obtained in this way about the state of the quality of stored grain will allow for a fast reaction in case of the threat of lowering the quality parameters of the stored grain