Middle School Students From China’s Rice Area Show More Adaptive Creativity but Less Innovative and Boundary-Breaking Creativity

The present study aimed to conduct a cross-cultural comparison of creative thinking among Chinese middle school students from the rice- and wheat-growing areas in China through the lens of the rice theory, which postulates that there are major psychological differences among the individuals in these agricultural regions. Differences in cultural mindsets and creativity between the rice group (n = 336) and the wheat group (n = 347) were identified using the Chinese version of (1) the Auckland Individualism and Collectivism Scale (AICS) and (2) the Test for Creative Thinking–Drawing Production (TCT–DP), respectively. Interesting findings were obtained. The results of latent mean analyses indicate that the rice group showed significantly more collectivism and adaptive creativity than the wheat group but less individualism and innovative and boundary-breaking creativity. However, the two groups showed no significant differences in their overall creative performance, as reflected in the TCT–DP composite score. Moreover, results of hierarchical multiple regression analyses revealed that collectivism was positively related to adaptive creativity but negatively related to innovative and boundary-breaking creativity; however, a reverse pattern was found for individualism. These findings enrich the discourse regarding the rice theory and shed important light on the effect of culture on creativity.

Policy Framework for Myanmar Rice Production and In-Depth Study on Greenhouse Gas Emissions

This study assesses the interactive nature of rice and climate change in the context of Myanmar, one of the largest rice-producing countries. In the first section, special emphasis is given to the current situation of Myanmar’s rice production as affected by climate change alongside with possible adaptation strategies. Since only a small share (23.6 %) of the rice area is irrigated, low precipitation climate extremes directly translate into either drought problems due to limited access of water in case of drought or flood problems due to limited drainage. Moreover, more than half of the national rice production derives from the Ayaryewady delta, so that Myanmar’s food security is very susceptible to impacts triggered tropical cyclones such as “Nargis” in 2008. The scope of adaptation to climate change is elaborated at different levels ranging technical options for increasing resilience of the rice crop to policies that alleviate risks for farmers.

Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm

The timely and accurate mapping of paddy rice is important to ensure food security and to protect the environment for sustainable development. Existing paddy rice mapping methods are often remote sensing technologies based on optical images. However, the availability of high-quality remotely sensed paddy rice growing area data is limited due to frequent cloud cover and rain over the southwest China. In order to overcome these limitations, we propose a paddy rice field mapping method by combining a spatiotemporal fusion algorithm and a phenology-based algorithm. First, a modified neighborhood similar pixel interpolator (MNSPI) time series approach was used to remove clouds on Sentinel-2 and Landsat 8 OLI images in 2020. A flexible spatiotemporal data fusion (FSDAF) model was used to fuse Sentinel-2 data and MODIS data to obtain multi-temporal Sentinel-2 images. Then, the fused remote sensing data were used to construct fusion time series data to produce time series vegetation indices (NDVI\LSWI) having a high spatiotemporal resolution (10 m and ≤16 days). On this basis, the unique physical characteristics of paddy rice during the transplanting period and other auxiliary data were combined to map paddy rice in Yongchuan District, Chongqing, China. Our results were validated by field survey data and showed a high accuracy of the proposed method indicated by an overall accuracy of 93% and the Kappa coefficient of 0.85. The paddy rice planting area map was also consistent with the official data of the third national land survey; at the town level, the correlation between official survey data and paddy rice area was 92.5%. The results show that this method can effectively map paddy rice fields in a cloudy and rainy area.

Dynamics of rice production among the food crops of Tanzania

Rice is an important crop in Tanzania which contributes significantly to the farmers, consumers, and the government. Recognising this importance, the government has made initiatives to attain rice self-sufficiency. These initiatives are crucial in contributing to regional self-sufficiency, enabling rice market leadership, and injecting productivity through significant improvements in the quality, quantity, and value of rice produced in Tanzania. We investigated the dynamics of rice area, production, and productivity and identified shifts in the land-use patterns in Tanzania. To analyse secondary data collected over a 33-year period from 1986/1987 to 2018/2019, we used compound annual growth rates, Cuddy-Della Valle Index and a first-order Markov chain approach. We found that the growth in the areas under rice cultivation, production and productivity were inconsistent as evidenced by the presence of instabilities. Rice remains the third most stable crop in the country in terms of area under production retention; however, this might decline in the next 2 years. Policies in future must enable strategies to increase productivity as well as promote high-yielding varieties, efficient input usage, and irrigation infrastructure development.

Rice Cultivation - A Way of Life for the People of North Eastern Hill Region of India

Being an amazing picturesque of land, the North Eastern Hill Region of India, consisting of more than 200 ethnic groups, has only about 2.27% of the total rice area and shares only 1.96% of the total rice production in the country. Whether profitable or not, the rice cultivation is a way of life for the people of North Eastern Hill Region of India. Till today, the production and productivity of rice in this region is below the national average because of its fragile ecosystem and the varied physio-graphic conditions pertaining to this region. Neither the wider recommendation of agricultural technology such as variety nor the use of a single technology or variety can solve this problem of low yield. However, the development of location specific high yielding rice variety using the existing land races prevalent in the area can be one of the promising technique for improving the production and productivity of rice cultivation in this region.

Discussion on High-Yield Cultivation and Assembly Technology of Selenium-Rich Rice and Rapeseed Rotation in Taoyuan County

Taoyuan County is a large grain and rapeseed production county. Taking advantage of the resource advantage of soil rich in selenium in Taoyuan County [1], it promotes high-yield cultivation and assembly technology of selenium-rich rice and rapeseed rotation in one-season rice area, optimizing the aggregate structure of the soil, improving the soil ecology and reducing the content of heavy metals in the soil [2], laying the foundation for the continuous increase in agricultural efficiency and farmers' income. Through rice and rapeseed rotation, the overwintering base of rice field borers are reduced, and the incidence of pests and diseases in the coming year is effectively reduced [3]. The implementation of supporting technologies for rice-rapeseed rotation cropping and the widespread promotion of high-quality varieties and planting techniques have improved the level of farmers’ planting. After one season of rice harvesting, most of the farmland is left unused. Using winter fallow fields to develop selenium-enriched rapeseed industry can actually increase the income of farmers.

Mechanized Cultivation Increases Labour Efficiency

Farm mechanization facilitates to increase agricultural productivity and improves farm management by replacing human labour. Therefore, mechanical intervention is a vital adaptation strategy for a sustainable rice production system. Thus, this study aims to (i) estimate the amount of mechanical in-tervention required in rice cultivation, particularly in transplanting and harvesting operation; and (ii) delineate the impact of farm mechanization on rice productivity and employment generation. The primary data were collected through a household survey and key informant interviews. These data were used for projecting rice area, labour requirement, and off-farm employment opportunities in rice cultivation. Besides, secondary data were collected from published literature. The break-even la-bour requirement per hectare rice cultivation was 2.88 work-days. Labour productivity might be doubled by 2030 subject to bringing 42% and 36% of the total rice area under mechanical transplant-ing and harvesting, respectively. The projected demands of the transplanter and combine harvester to achieve the goal are 49,172 and 28,382, respectively. The mechanized rice transplanting, weeding, and harvesting reduced labour requirement by 29, 26, and 34%, respectively, compared to the manual op-erations. Besides, mechanization meets up the demand for labour at a seasonal peak and increases rice productivity. Furthermore, it creates some off-farm (e.g., operating) and non-farm (e.g., manufac-turing, repairing, and trading) employment opportunities for operating, maintenance, fabricating, and marketing of the machinery. Syncronize farming is required for enhancing the field efficiency of the farm machine at present size and shape of plots. Besides, the synchronized farming is beneficial for providing service to the farmers at their affordable rental charge. Government assistance should continue and strengthen for the procurement of transplanters and combine harvesters. A holistic ap-proach combining the public and private intervention is essential for achieving the mechanized farm-ing goal for sustainable rice farming in Bangladesh. Bangladesh Rice J. 24 (2): 49-66, 2021

A Scalable Machine Learning Pipeline for Paddy Rice Classification Using Multi-Temporal Sentinel Data

The demand for rice production in Asia is expected to increase by 70% in the next 30 years, which makes evident the need for a balanced productivity and effective food security management at a national and continental level. Consequently, the timely and accurate mapping of paddy rice extent and its productivity assessment is of utmost significance. In turn, this requires continuous area monitoring and large scale mapping, at the parcel level, through the processing of big satellite data of high spatial resolution. This work designs and implements a paddy rice mapping pipeline in South Korea that is based on a time-series of Sentinel-1 and Sentinel-2 data for the year of 2018. There are two challenges that we address; the first one is the ability of our model to manage big satellite data and scale for a nationwide application. The second one is the algorithm’s capacity to cope with scarce labeled data to train supervised machine learning algorithms. Specifically, we implement an approach that combines unsupervised and supervised learning. First, we generate pseudo-labels for rice classification from a single site (Seosan-Dangjin) by using a dynamic k-means clustering approach. The pseudo-labels are then used to train a Random Forest (RF) classifier that is fine-tuned to generalize in two other sites (Haenam and Cheorwon). The optimized model was then tested against 40 labeled plots, evenly distributed across the country. The paddy rice mapping pipeline is scalable as it has been deployed in a High Performance Data Analytics (HPDA) environment using distributed implementations for both k-means and RF classifiers. When tested across the country, our model provided an overall accuracy of 96.69% and a kappa coefficient 0.87. Even more, the accurate paddy rice area mapping was returned early in the year (late July), which is key for timely decision-making. Finally, the performance of the generalized paddy rice classification model, when applied in the sites of Haenam and Cheorwon, was compared to the performance of two equivalent models that were trained with locally sampled labels. The results were comparable and highlighted the success of the model’s generalization and its applicability to other regions.