Inter-provincial Differences in Rice Multi-cropping Changes in Main Double-cropping Rice Area in China: Evidence from Provinces and Households

Since the early 1980 s, the multi-cropping index for rice has decreased significantly in main double-cropping rice area in China, which is the primary double-cropping rice(DCR) production area. This decline may bring challenges to food security in China because rice is the staple food for more than 60% of the Chinese population. It has been generally recognized that rapidly rising labor costs due to economic growth and urbanization in China is the key driving force of the ‘double-to-single' rice cropping system adaption. However, not all provinces have shown a dramatic decline in DCR area, and labor costs alone cannot explain this difference. To elucidate the reasons for these inter-provincial distinctions and the dynamics of rice cropping system adaption, we evaluated the influencing factors using provincial panel data from 1980 to 2015. We also used household survey data for empirical analysis to explore the mechanisms driving differences in rice multi-cropping changes. Our results indicated that the eight provinces in the study can be divided into three spatial groups based on the extent of DCR area decline, the rapidly-declining marginal, core, and stable zones. Increasing labor cost due to rapid urbanization was the key driving force of rice cropping system adaption, but the land use dynamic vary hugely among different provinces. These differences between zones were due to the interaction between labor price and accumulated temperature conditions. Therefore, increasing labor costs had the greatest impact in Zhejiang, Anhui, and Hubei, where the accumulated temperature is relatively low and rice multi-cropping index declined dramaticly. However, labor costs had little impact in Guangdong and Guangxi. Differences in accumulated temperature conditions resulted in spatially different labor demands and pressure on households during the busy season. As a result, there have been different profits and rice multi-cropping changes between provinces and zones. Because of these spatial differences, regionally appropriate policies that provide appropriate subsidies for early rice in rapidly-declining marginal zone such as Zhejiang and Hubei should be implemented. In addition, agricultural mechanization and the number of agricultural workers have facilitated double-cropping; therefore, small machinery and agricultural infrastructure construction should be further supported.

China’s Potential of Grain Production Due to Changes in Agricultural Land Utilization in Recent Years

The changes in utilization of agricultural land have gradually grown into one of the major factors impacting grain output in China. This study explores the various components of agricultural production in China from the land utilization perspective, involving changes in grain production per unit area, multi-cropping index, and adjustment of agricultural structure. Compared with the record values, different research methodologies are used to analyze the po- tential of above three components. The results indicate that grain production potential of 65.68×109kg was unexploited in 2006, in which 45.8×109kg came from the restructuring in agriculture. So we can infer that the reduction of grain production in China could be primarily attributed to agricultural restructuring in recent years. So the productive poten- tial can be fully restored by increasing agricultural investment, or recovering agricultural structure in favorable condi- tions. So we can say that China’s current condition of food security is good.

Developing crop specific area frame stratifications based on geospatial crop frequency and cultivation data layers

Area Sampling Frames （ASFs） are the basis of many statistical programs around the world. To improve the accuracy, objectivity and efficiency of crop survey estimates, an automated stratification method based on geospatial crop planting frequency and cultivation data is proposed. This paper investigates using 2008-2013 geospatial corn, soybean and wheat planting frequency data layers to create three corresponding single crop specific and one multi-crop specific South Dakota （SD） U.S. ASF stratifications. Corn, soybeans and wheat are three major crops in South Dakota. The crop specific ASF stratifications are developed based on crop frequency statistics derived at the primary sampling unit （PSU） level based on the Crop Frequency Data Layers. The SD corn, soybean and wheat mean planting frequency strata of the single crop stratifications are substratified by percent cultivation based on the 2013 Cultivation Layer. The three newly derived ASF stratifications provide more crop specific information when compared to the current National Agricultural Statistics Service （NASS） ASF based on percent cultivation alone. Further, a multi-crop stratification is developed based on the individual corn, soybean and wheat planting frequency data layers. It is observed that all four crop frequency based ASF stratifications consistently predict corn, soybean and wheat planting patterns well as verified by the 2014 Farm Service Agency （FSA） Common Land Unit （CLU） and 578 administrative data. This demonstrates that the new stratifications based on crop planting frequency and cultivation are crop type independent and applicable to all major crops. Further, these results indicate that the new crop specific ASF stratifications have great potential to improve ASF accuracy, efficiency and crop estimates.

The Impact of Agricultural Practices in China on Land-Atmosphere Interactions

Human-induced land use changes and the resulting alterations in vegetation features are major but poorly recognized drivers of regional climatic patterns.In order to investigate the impacts of anthropogenically-induced seasonal vegetation cover changes on regional climate in China,harmonic analysis is applied to 1982-2000 National Oceanic and Atmospheric Administration（NOAA） Advanced Very High Resolution Radiometer（AVVHRR）-derived normalized difference vegetation index（NDVI） time series（ten day interval data）.For two climatic divisions of South China,it is shown that the first harmonic term is in phase with air temperature,while the second and third harmonics are in phase with agricultural cultivation.The Penman-Monteith Equation and the Complementary Relationship Areal Evapotranspiration（CRAE） model suggest that monthly mean evapotranspiration is out of phase with temperature and precipitation in regions with signiffcant second or third harmonics.Finally,seasonal vegetation cover changes associated with agricultural cultivation are identiffed：for cropped areas,the temperature and precipitation time series have a single maximum value,while the monthly evapotranspiration time series has a bimodal distribution.It is hypothesized that multi-cropping causes the land surface albedo to sharply increase during harvesting,thereby altering the energy distribution ratio and contributing to observed seasonal vegetation cover changes.

Paddy field soil conservation: Indian historical practices

India is an ancient land having high seasonal rain fall (4 months rain & 8 months dry), has paddy cultivation. Becauses silt-sand separation;buoyant sand gets carried;silt agglutinates. Rill fluid dissolves agglutinated soil;vectors as silt → degradation. Indian farmer has unique agricultural field conservation;soil cum fertility maintenance/regeneration heritage. Also use the stubble and cow dung (cellulose) as binder cum multi purpose in-field uses. economic;ecologically safe;and not discussed earlier. Good tool for altruistic administrations.

Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG

In recent times,the use of artificial intelligence(AI)in agriculture has become the most important.The technology adoption in agriculture if creatively approached.Controlling on the diseased leaves during the growing stages of crops is a crucial step.The disease detection,classification,and analysis of diseased leaves at an early stage,as well as possible solutions,are always helpful in agricultural progress.The disease detection and classification of different crops,especially tomatoes and grapes,is a major emphasis of our proposed research.The important objective is to forecast the sort of illness that would affect grapes and tomato leaves at an early stage.The Convolutional Neural Network(CNN)methods are used for detecting Multi-Crops Leaf Disease(MCLD).The features extraction of images using a deep learning-based model classified the sick and healthy leaves.The CNN based Visual Geometry Group(VGG)model is used for improved performance measures.The crops leaves images dataset is considered for training and testing the model.The performance measure parameters,i.e.,accuracy,sensitivity,specificity precision,recall and F1-score were calculated and monitored.The main objective of research with the proposed model is to make on-going improvements in the performance.The designed model classifies disease-affected leaves with greater accuracy.In the experiment proposed research has achieved an accuracy of 98.40%of grapes and 95.71%of tomatoes.The proposed research directly supports increasing food production in agriculture.

Cultivated land change in the Belt and Road Initiative region

The Belt and Road Initiative （BRI）-a development strategy proposed by China - provides unprecedented opportunities for multi-dimensional communication and cooperation across Asia, Africa and Europe. In this study, we analyse the spatio-temporal changes in cul- tivated land in the BRI countries （64 in total） to better understand the land use status of China along with its periphery for targeting specific collaboration. We apply FAd statistics and GlobeLand30 （the world＇s finest land cover data at a 30-m resolution）, and develop three indicator groups （namely quantity, conversion, and utilization degree） for the analysis. The results show that cultivated land area in the BRI region increased 3.73x10^4 km2 between 2000 and 2010. The increased cultivated land was mainly found in Central and Eastern Europe and Southeast Asia, while the decreased cultivated land was mostly concentrated in China. Russia ranks first with an increase of 1.59x10^4 km2 cultivated land area, followed by Hungary （0.66x10^4 km2） and India （0.57x10^4 km2）. China decreased 1.95x10^4 km2 cultivated land area, followed by Bangladesh （-0.22x10^4 km2） and Thailand （-0.22x10^4 km2）. Cultivated land was mainly transferred to/from forest, grassland, artificial surfaces and bare land, and transfer types in different regions have different characteristics： while large amount of culti- vated land in China was converted to artificial surfaces, considerable forest was converted to cultivated land in Southeast Asia. The increase of multi-cropping index dominated the region except the Central and Eastern Europe, while the increase of fragmentation index was prevailing in the region except for a few South Asian countries. Our results indicate that the negative consequence of cultivated land loss in China might be underestimated by the domestic-focused studies, as none of its close neighbours experienced such obvious cultivated land losses. Nevertheless, the increased cultivated land area in Southeast Asia and the extensive cultivated land use in Ukraine and Russia imply that the regional food production would be greatly improved if China＇ ＂Go Out policy＂ would help those countries to intensify their cultivated land use.