Trend assessment in climate variable by Mann Kendall test of Bastar district of Chhattisgarh

Climate change is a natural phenomenon but in present decades its variability of change mainly due to anthropogenic activities is alarming. Agriculture of Chhattisgarh state is mainly dependant on monsoon rain and its distribution. Considering this fact, the present study has been tried to analyze the most important climatic variables, viz., precipitation and temeperature for analyzing their trend in the area. The trends of maximum atmospheric temperature, rainfall and rainy days are analysed statistically for meteorological data of Jagdalpur station of Bastar district, over last three decades stretching between years 1980 to 2014. The long term change in temperature, rainfall and rainy days has been analysed by correlation and linear trend analysis. The annual MMAX temperature has decreased at a rate of -0.465 °C per year during this period at Jagdalpur station and decreasing trend for rainy days during monsoonal season (June to September) is also found and is confirmed by Mann-Kendall trend test. Very weak increasing trend is observed in total month rainfall (TMRF) during season June to September. There are decreasing trends of mean monthly rainfall and south west (June - September) rainfall observed in Bastar district of Chhattisgarh. The agricultural planning and utilization of water is dependent on monsoon rainfall and more than 75% of rainfall occurring during the monsoon season is uneven both in time and space. Therefore its analysis is important for crop planning.

Improving daily rainfall extremes simulation using the generalized Pareto distribution: a case study in Western Iran

Prediction of the occurrence or non-occurrence of daily rainfall plays a significant role in agricultural planning and water resource management projects. In this study, gamma distribution function (GDF), kernel, and exponential (EXP) distributions were coupled (piecewise) with a generalized Pareto distribution. Thus, the gamma-generalized Pareto (GGP), kernel-generalized Pareto (KGP), and exponential-generalized Pareto (EGP) models were used. The aim of the present study was to introduce new methods to modify the simulated generation of extreme rainfall amounts of rainy seasons based on the preserved spatial correlation. The best approach was identified using the normalized root mean square error (NRMSE) criterion. For this purpose, the 30-yr daily rainfall datasets of 21 synoptic weather stations located in different climates of West Iran were analyzed. The first, second, and third-order Markov chain (MC) models were used to describe rainfall time series frequencies. The best MC model order was detected using the Akaike information criterion and Bayesian information criterion. Based on the best identified MC model order, the best piecewise distribution models, and the Wilks approach, rainfall events were modeled with regard to the spatial correlation among the study stations. The performance of the Wilks approach was verified using the coefficient of determination. The daily rainfall simulation resulted in a good agreement between the observed and the generated rainfall data. Hence, the proposed approach is capable of helping water resource managers in different contexts of agricultural planning.

Validation of CERES-Rice v3.5 under the climate of Andhra Pradesh state, India

Crop growth simulation models, properly validated against experimental data have the potential for tactical and strategic decision making in agriculture. Such validated models can also take the information generated through site specific experiments and trials to other sites and years. For proper calibration and evaluation of crop simulation models, there is a need for collection of a comprehensive minimum set of data on soil, weather and crop management in all agronomic experiments. Keeping this in view, field experiments were conducted at Rajendranagar (17°19' N, 78°23' E; 542.3 m amsl) during 1994-97 for three popular varieties of rice viz. Sambamasuri, Rajavadlu and Tellahamsa under irrigated conditions and data collected. Genetic coefficients required for running the CERES-Rice v3.5 model were calculated and the performance of the model under the climate of the area was evaluated. The results of the study show that the model simulations of date of flowering for Sambamasuri, Rajavadlu and Tellahamsa were within an average error of 6.2, 5.7 and 6.7 days respectively. Similar errors in predictions of physiological maturity dates were 7.6, 6.7 and 7.2 days. The error in grain yield predictions by the model averaged at 7.9%, 8.3%, and 5.7% respectively for the three crop varieties. These results indicate that the CERES Rice v3.5 model is capable of prediction of grain yield and phenological development of the crop in the climatic conditions of Andhra Pradesh with reasonable accuracy and hence, the model have the potential for its use as a tool in making various strategic and tactical decisions related to agricultural planning in the state.

River Segmentation of Remote Sensing Images Based on Composite Attention Network

River segmentation of remote sensing images is of important research significance and application value for environmental monitoring, disaster warning, and agricultural planning in an area. In this study, we propose a river segmentation model in remote sensing images based on composite attention network to solve the problems of abundant river details in images and the interference of non-river information including bridges, shadows, and roads. To improve the segmentation efficiency, a composite attention mechanism is firstly introduced in the central region of the network to obtain the global feature dependence of river information. Next, in this study, we dynamically combine binary cross-entropy loss that is designed for pixel-wise segmentation and the Dice coefficient loss that measures the similarity of two segmentation objects into a weighted one to optimize the training process of the proposed segmentation network. The experimental results show that compared with other semantic segmentation networks, the evaluation indexes of the proposed method are higher than those of others, and the river segmentation effect of CoANet model is significantly improved. This method can segment rivers in remote sensing images more accurately and coherently, which can meet the needs of subsequent research.

An assessment of the urban water footprint and blue water scarcity: A case study for Van (Turkey)

Today, most of the world’s population faces water scarcity, while global warming, urbanization, industrialization and population increases continue to increase the severity of the pressure on water resources. Management of water resources plays a key role in the sustainability of agricultural production. The water footprint (WF) is different in comparison to other water statistics because it takes direct and indirect water consumption into account, and helps in the management of water resources. Within this context, the WF of Van province, which is Turkey’s most easterly located arid region, was calculated from 2004 to 2019. The study area covers lake Van, which is Turkey's largest lake, and the Van basin with an area of 23.334 km2 and a population of 1.136.757 (2019). In the calculations, crop (WFcrop), livestock (WFlivestock), and domestic and industrial water footprints (WFdomestic+industrial) were evaluated separately, and blue and green water footprints (WFblue and WFgreen) were analyzed in detail. According to the results, the average WF of Van province was found to be 8.73 billion m3 year-1. Throughout the province, 87.6% of the WF is composed of WFcrop, 4.9% is WFlivestock and 7.5% is WFdomestic+industrial. Of the WFcrop, 62.5% depends on WFblue, i.e., freshwater. Most of the WFlivestock consisted of dairy cattle (49%) and sheep (38%). The average WFdomestic+industrial for 2004 to 2019 was 0.64 billion m3 year-1. The average per capita water footprint of Van province was found to be 889.9 m3 year-1 capita-1. In addition, the province is classified as severe water scarcity (257%). This study is one of the first province-based calculations of WF in Turkey and is the first study to bring a different aspect to published literature by including residual soil moisture from the winter months. As a result of this study, the WFblue of the WFcrop is above the worldwide average and should be reduced by changing the crop pattern or synchronizing the planting and harvest dates of the crops to a period that benefits from precipitation. In addition, this study is expected to contribute to new studies for calculating the provincial scale WF and will have positive effects on agricultural planning, water allocation and the sustainability of water resources.

Analysis of issues of interest in unification economic cooperation by visualizing big data on social networks

This study aims to intuitively identify trends in public interest by performing visualization analysis on unification economic cooperation using social network user opinion big data. For the “Unification Economic Cooperation” big data, the related big data were extracted using the Textom analysis tool, and text mining was performed. The results were expressed as a visualization figure.The results showed that, first, social network users were interested in North Korea's regional agricultural planning and education by the US and the government. Additionally, North Korea's rural villages existed in connection with pastoral and missionary words. An interest in how North and South Korea cooperate in investment and development in rural areas was identified. Second, there was an issue of interest in housing exchange and cooperation in North Korean villages by the community of club members as words such as club members → housing, common → housing, village → community exchange → cooperation were connected. Third, users were interested in culture and art. It would be useful to find and implement economic cooperation, a link between culture and art connected with North Korea's economic cooperation. Fourth, words such as technology, research, development, and dissemination were drawn as issues of interest. From the perspective of an integrated process, it is of interest to the general public to identify ways to implement it so that it can be pursued with economic benefits for both South and North Korea. Fifth, social network users were interested in the content of dot-com sites. The importance of the promotion of unification economic cooperation through these dot-com sites was confirmed.

Markov chain model probability of dry, wet of rainy wet weeks during moonson period over Andhra Pradesh

For agricultural planning, it is important to know the sequence of dry, wet periods. For this purpose a week period may be taken as the optimum length of time. The success or failure of crops particularly under rainfed conditions is closely linked with the rainfall patterns. In this study the Markov chain model method has been applied to know the probability of having a dry or a wet week and consecutive dry or wet periods of 2 or 3 weeks during monsoon period over Andhra Pradesh.

Economy of the mexican agriculture sector in times of COVID-19

<p>COVID-19, a pandemic disease caused by SARS-CoV-2, changed the production schemes and supply chains in all spheres of the world’s economy. The agricultural sector in Mexico was no exception, although it has been so essential during the pandemic that its growth was higher than the other sectors of the Mexican economy and it stood out as a food supplier in the world in 2020. Farmers’ vocations and the integration of productive food chains led to a surplus of 1.2 billion dollars, with an annual increase of 39.92%. The pandemic is a challenge and an opportunity for the Mexican countryside in terms of digital and technological innovation derived from border investigation. However, it is crucial to establish public agricultural planning policies to help optimize this area of opportunity by focusing on new production and national and international trade models, responding efficiently to national visions to benefit producers-consumers and guaranteeing food security in the framework of the UN’s international policies for sustainable development, the IPCC’s reduction of climate impact and ensuring human health by the WHO.</p>

Rice phenology and growth simulation using CERES-Rice model under the agro-climate of upper Brahmaputra valley of Assam

Crop growth simulation models, properly validated against experimental data have the potential for facilitating strategic decision making in agriculture. Such validated models can also make use of the information generated for site specific experiments and trials to other sites and for different time durations. For proper calibration and evaluation of crop simulation models, there is a need for collection of a comprehensive minimum set of data on soil, weather and crop management in all agronomic experiments. Keeping this in view, data from seven field experiments conducted at Jorhat (26° 47' N, 94°12' E; 87 m amsl) during 1998-2005 for long duration rice cultivar Ranjit grown under rainfed conditions were collected. Genetic coefficients required for running the CERES-Rice v4.5 model were derived and the performance of the model under the climate of upper Brahmaputra valley was evaluated. These results indicate that the CERES Rice v4.5 model is capable of estimating growth stages and grain yield of rice cultivar Ranjit in the climatic conditions of upper Brahmaputra valley with reasonable accuracy. Hence, the model have the potential for its use as a tool in making various strategic and tactical decisions related to agricultural planning in the state.

Cascading Models of CNN and GRU with Autoencoder Loss for Precipitation Forecast in Thailand

It is a crucial task to accurately forecast precipitation, especially rainfall in Thailand, since it relates to flood prevention and agricultural planning. In our prior work, we have presented a model based on deep learning approach; however, its performance is still limited due to two main issues. First, there is an imbalance issue, where most rainfall is zero or no rain because Thailand has short rainy season. Second, predicted rainfall is still underestimated since moderate and heavy rainfall cases barely occurs. In this paper, we propose an enhanced deep learning model to forecast rainfall in Thailand. Our model is a cascading of CNN and GRU along with exogenous variables, i.e., temperature, pressure, and humidity. There are two stages in our model. First, CNN is specialized for classifying rain and non-rain events. In this stage, an imbalanced issue is alleviated by applying “focal loss”. Second, GRU is responsible for forecasting rainfall. Its predicted range is lifted using “autoencoder loss”. The experiment was conducted on hourly rainfall dataset between 2012 and 2018 obtained from a public government sector in Thailand. The results show that our enhanced model outperforms ARIMA and CNN-GRU in terms on RMSE of most regions in Thailand.