An Econometric Analysis on the Effect of Climate Change on Wheat Cropping Area in China

[Objective] This study aimed to explore the impact of climate change on wheat cropping by using province-specific historical data during 1996-2007. [Method] We established a panel data econometric model with lagged wheat cropping area and province-specific fixed-effects model to control the unobserved factors. [Result] The results showed that the temperature positively affects wheat cropping area, while precipitation does not have such impact. [Conclusion] The study provided empirical evidence for analysis of the determinants of wheat cropping area in China.

A Convolutional Neural Network Model for Wheat Crop Disease Prediction

Wheat is the most important cereal crop,and its low production incurs import pressure on the economy.It fulfills a significant portion of the daily energy requirements of the human body.The wheat disease is one of the major factors that result in low production and negatively affects the national economy.Thus,timely detection of wheat diseases is necessary for improving production.The CNN-based architectures showed tremendous achievement in the image-based classification and prediction of crop diseases.However,these models are computationally expensive and need a large amount of training data.In this research,a light weighted modified CNN architecture is proposed that uses eight layers particularly,three convolutional layers,three SoftMax layers,and two flattened layers,to detect wheat diseases effectively.The high-resolution images were collected from the fields in Azad Kashmir(Pakistan)and manually annotated by three human experts.The convolutional layers use 16,32,and 64 filters.Every filter uses a 3×3 kernel size.The strides for all convolutional layers are set to 1.In this research,three different variants of datasets are used.These variants S1-70%:15%:15%,S2-75%:15%:10%,and S3-80%:10%:10%(train:validation:test)are used to evaluate the performance of the proposed model.The extensive experiments revealed that the S3 performed better than S1 and S2 datasets with 93%accuracy.The experiment also concludes that a more extensive training set with high-resolution images can detect wheat diseases more accurately.

Development and application of plant transformation techniques

Genetic transformation is a powerful biotechnology for introducing novel genes into economically important plants from distantly-related plants or even unrelated species such as microbes and animals.This feat is impossible to be achieved by conventional breeding techniques.Development of transgenic plants has been a controversial subject since 1971 when the first genetically modified organism（GMO）was developed（James and Krattiger1996）.

Significant reduction of ammonia emissions while increasing crop yields using the 4R nutrient stewardship in an intensive cropping system

Ammonia (NH_3) emissions should be mitigated to improve environmental quality.Croplands are one of the largest NH_3sources,they must be managed properly to reduce their emissions while achieving the target yields.Herein,we report the NH_3 emissions,crop yield and changes in soil fertility in a long-term trial with various fertilization regimes,to explore whether NH_3 emissions can be significantly reduced using the 4R nutrient stewardship (4Rs),and its interaction with the organic amendments (i.e.,manure and straw) in a wheat–maize rotation.Implementing the 4Rs significantly reduced NH_3 emissions to 6 kg N ha~(–1) yr~(–1) and the emission factor to 1.72%,without compromising grain yield (12.37 Mg ha~(–1) yr~(–1))and soil fertility (soil organic carbon of 7.58 g kg~(–1)) compared to the conventional chemical N management.When using the 4R plus manure,NH_3 emissions (7 kg N ha~(–1) yr~(–1)) and the emission factor (1.74%) were as low as 4Rs,and grain yield and soil organic carbon increased to 14.79 Mg ha~(–1) yr~(–1) and 10.09 g kg~(–1),respectively.Partial manure substitution not only significantly reduced NH_3 emissions but also increased crop yields and improved soil fertility,compared to conventional chemical N management.Straw return exerted a minor effect on NH_3 emissions.These results highlight that 4R plus manure,which couples nitrogen and carbon management can help achieve both high yields and low environmental costs.

Smart Techniques for LULC Micro Class Classification Using Landsat8 Imagery

Wheat species play important role in the price of products and wheat production estimation.There are several mathematical models used for the estimation of the wheat crop but these models are implemented without considering the wheat species which is an important independent variable.The task of wheat species identification is challenging both for human experts as well as for computer vision-based solutions.With the use of satellite remote sensing,it is possible to identify and monitor wheat species on a large scale at any stage of the crop life cycle.In this work,nine popular wheat species are identified by using Landsat8 operational land imager(OLI)and thermal infrared sensor(TIRS)data.Two thousand samples of eachwheat crop species are acquired every fifteen days with a temporal resolution of ten multispectral bands(band two to band eleven).This study employs random forest(RF),artificial neural network,support vector machine,Naive Bayes,and logistic regression for nine types of wheat classification.In addition,deep neural networks are also developed.Experimental results indicate that RF shows the best performance of 91%accuracy while DNN obtains a 90.2%accuracy.Results suggest that remotely sensed data can be used in wheat type estimation and to improve the performance of the mathematical models.

Effects of sowing methods on nitrogen compounds and protease activities of whole‐crop wheat silage

Background:Whole‐crop wheat(Triticum aestivum)has high nutritive value,and it has become one of the main sources of roughage for ruminants in some countries or regions.This study investigates the effects of no tillage on nitrogen compounds and protease activities of whole‐crop wheat silage.Methods:Wheat was planted on the 9th day(NB9)and 5th day(NB5)before rice harvest and on the first day after rice harvest.Sowing before harvest involved no tillage and sowing after rice harvest involved either no tillage(NA1)or conventional tillage(CK).Results:Compared to CK,the crude protein content of NB9 and NB5 decreased by 16.4%and 9.58%,respectively.With the delay of the sowing date,the contents of non‐protein nitrogen,rapidly degraded protein,and slowly degraded protein in whole‐plant wheat tended to increase.Compared to NA1 wheat silage,the NH_(3)‐N content of NB9 and NB5 silages decreased by 52.7%and 34.4%,respectively.The acid protease activity of NA1 was significantly higher than that of other treatments(p<0.05).Conclusions:There was no significant difference in the degree of protein degradation between NA1 and CK silages.Although the degree of protein degradation in early sowing was low,the degree of fermentation was also weak.

Role of organic amendments in reducing leaching of sulfosulfuron through wheat crop cultivated soil

A field study with wheat crop was undertaken to examine the efficacy of two soil amendments,cereal straw and fresh cow dung slurry,in reducing leaching of sulfosulfuron to groundwater and its residue load in soil.The herbicide was extracted by the QuEChER’S method and quantified through high performance liquid chromatography.The amendments had no effect on the general properties of groundwater.Both cereal straw and fresh cow dung reduced leaching of sulfosulfuron into groundwater but cereal straw was more effective,probably because its addition decreases soil pH,thereby causing faster hydrolysis of the herbicide.At harvest time,residues of the herbicide in grains,straw and soil were below recommended maximum residue limits(0.1mg kg-1)with both amendments.Thus,both the amendments can be recommended for wheat cropping systems to control sulfosulfuron leaching to groundwater.

Field scale interaction and nutrient exchange between surface water and shallow groundwater in the Baiyang Lake region,North China Plain

Fertilizer input for agricultural food production, as well as the discharge of domestic and industrial water pollutants, increases pressures on locally scarce and vulnerable water resources in the North China Plain. In order to：（a） understand pollutant exchange between surface water and groundwater,（b） quantify nutrient loadings, and（c） identify major nutrient removal pathways by using qualitative and quantitative methods, including the geochemical model PHREEQC） a one-year study at a wheat（Triticum aestivum L.） and maize（Zea mays L.） double cropping system in the Baiyang Lake area in Hebei Province, China, was undertaken. The study showed a high influence of low-quality surface water on the shallow aquifer. Major inflowing pollutants into the aquifer were ammonium and nitrate via inflow from the adjacent Fu River（up to 29.8 mg/L NH4-N and 6.8 mg/L NO3-N）, as well as nitrate via vertical transport from the field surface（up to 134.8 mg/L NO3-N in soil water）. Results from a conceptual model show an excess nitrogen input of about 320 kg/ha/a. Nevertheless,both nitrogen species were only detected at low concentrations in shallow groundwater,averaging at 3.6 mg/L NH4-N and 1.8 mg/L NO3-N. Measurement results supported by PHREEQC-modeling indicated cation exchange, denitrification, and anaerobic ammonium oxidation coupled with partial denitrification as major nitrogen removal pathways. Despite the current removal capacity, the excessive nitrogen fertilization may pose a future threat to groundwater quality. Surface water quality improvements are therefore recommended in conjunction with simultaneous monitoring of nitrate in the aquifer, and reduced agricultural N-inputs should be considered.