Climate Change Modelling and Its Roles to Chinese Crops Yield

Climate has been changing in the last fifty years in China and will continue to change regardless any efforts for mitigation. Agriculture is a climate-dependent activity and highly sensitive to climate changes and climate variability. Understanding the interactions between climate change and agricultural production is essential for society stable development of China. The first mission is to fully understand how to predict future climate and link it with agriculture production system. In this paper, recent studies both domestic and international are reviewed in order to provide an overall image of the progress in climate change researches. The methods for climate change scenarios construction are introduced. The pivotal techniques linking crop model and climate models are systematically assessed and climate change impacts on Chinese crops yield among model results are summarized. The study found that simulated productions of grain crop inherit uncertainty from using different climate models, emission scenarios and the crops simulation models. Moreover, studies have different spatial resolutions, and methods for general circulation model （GCM） downscaling which increase the uncertainty for regional impacts assessment. However, the magnitude of change in crop production due to climate change （at 700 ppm CO2 eq correct） appears within ±10% for China in these assessments. In most literatures, the three cereal crop yields showed decline under climate change scenarios and only wheat in some region showed increase. Finally, the paper points out several gaps in current researches which need more studies to shorten the distance for objective recognizing the impacts of climate change on crops. The uncertainty for crop yield projection is associated with climate change scenarios, CO2 fertilization effects and adaptation options. Therefore, more studies on the fields such as free air CO2 enrichment experiment and practical adaptations implemented need to be carried out.

Assessment of Crop Yield in China Simulated by Thirteen Global Gridded Crop Models

Global gridded crop models(GGCMs) have been broadly applied to assess the impacts of climate and environmental change and adaptation on agricultural production. China is a major grain producing country, but thus far only a few studies have assessed the performance of GGCMs in China, and these studies mainly focused on the average and interannual variability of national and regional yields. Here, a systematic national-and provincial-scale evaluation of the simulations by13 GGCMs [12 from the GGCM Intercomparison(GGCMI) project, phase 1, and CLM5-crop] of the yields of four crops(wheat, maize, rice, and soybean) in China during 1980–2009 was carried out through comparison with crop yield statistics collected from the National Bureau of Statistics of China. Results showed that GGCMI models generally underestimate the national yield of rice but overestimate it for the other three crops, while CLM5-crop can reproduce the national yields of wheat, maize, and rice well. Most GGCMs struggle to simulate the spatial patterns of crop yields. In terms of temporal variability, GGCMI models generally fail to capture the observed significant increases, but some can skillfully simulate the interannual variability. Conversely, CLM5-crop can represent the increases in wheat, maize, and rice, but works less well in simulating the interannual variability. At least one model can skillfully reproduce the temporal variability of yields in the top-10 producing provinces in China, albeit with a few exceptions. This study, for the first time, provides a complete picture of GGCM performance in China, which is important for GGCM development and understanding the reliability and uncertainty of national-and provincial-scale crop yield prediction in China.

An Integrated Analysis of Yield Prediction Models:A Comprehensive Review of Advancements and Challenges

The growing global requirement for food and the need for sustainable farming in an era of a changing climate and scarce resources have inspired substantial crop yield prediction research.Deep learning(DL)and machine learning(ML)models effectively deal with such challenges.This research paper comprehensively analyses recent advancements in crop yield prediction from January 2016 to March 2024.In addition,it analyses the effectiveness of various input parameters considered in crop yield prediction models.We conducted an in-depth search and gathered studies that employed crop modeling and AI-based methods to predict crop yield.The total number of articles reviewed for crop yield prediction using ML,meta-modeling(Crop models coupled with ML/DL),and DL-based prediction models and input parameter selection is 125.We conduct the research by setting up five objectives for this research and discussing them after analyzing the selected research papers.Each study is assessed based on the crop type,input parameters employed for prediction,the modeling techniques adopted,and the evaluation metrics used for estimatingmodel performance.We also discuss the ethical and social impacts of AI on agriculture.However,various approaches presented in the scientific literature have delivered impressive predictions,they are complicateddue to intricate,multifactorial influences oncropgrowthand theneed for accuratedata-driven models.Therefore,thorough research is required to deal with challenges in predicting agricultural output.

Various Organic Nutrient Sources in Combinations with Inorganic Fertilizers Influence the Yield and Quality of Sweet Corn(Zea mays L.saccharata)in New Alluvial Soils of West Bengal,India

Nutrient management plays a crucial role in the yield and quality of sweet corn.A field experiment was conducted in consecutive two kharif seasons in 2018 and 2019 to investigate the effect of various organic sources of nutrients in combination with inorganic sources on the yield and quality of sweet corn under new alluvial soils of West Bengal,India.Treatments were:T_(1):Control(without fertilizers);T_(2):100%recommended dose(RDF)of chemical fertilizers(CF)(RDF CF_(100%));T_(3):100%recommended dose of N(RDN)through vermicompost(VC)(RDN VC_(100%));T_(4):50 RDN through CF+50%RDN through VC(RDN CF_(50%)+RDN VC50%);T_(5):50%RDF through CF+50%RDN through organic source(OS)1,Soligro(Ascophyllum nodosum)granular(RDN CF_(50%)+RDN OS_(150%));T6:50%RDN through CF+50%RDN through OS 2,Bioenzyme(liquid)(RDN CF_(50%)+RDN OS250%);T7:50%RDN through CF+50%RDN through OS 3,Opteine(Ascophyllum nodosum)filtrate[RDN CF_(50%)+RDN OS350%];T8:50%RDN through VC+50%RDF through OS 1,Soligro(Ascophyllum nodosum)granular(RDN VC50%+RDN OS_(150%)).The OS of fertilizers were VC,SoliGro Gr(OS1)(Ascophyllum nodosum),Bioenzyme liquid(OS2),and Opteine(Ascophyllum nodosum)filtrate(OS3).The inorganic source was traditional CF applied at the RDF(150:75:75 kg ha^(−1) of N:P2O5:K2O).The VC was used to supply 100%RDN as one source or 50%RDN when combined with CF or OS.Maximum fruit yield(10.75 and 10.79 t ha^(−1) in 2018 and 2019,respectively)was recorded when RDF was substituted through CF only,being statistically at par with 50%CF+50%VC on a nitrogen equivalent basis(9.92 and 10.00 t ha^(−1) in 2018 and 2019,respectively)and 100%VC(8.22 and 8.32 t ha^(−1) in 2018 and 2019,respectively).Compared to chemical sources of nutrients,VC-based treatments produced a larger percentage of large-size cob(>25 cm).The 100%VC increased antioxidant(8.35 and 8.45 mg g^(−1)),carotenoid(0.59 and 0.61 mg/100 g),and phenol(55.06 and 55.02 mg 100 g^(−1))content compared with its 50%dose in combination with other sources.The study revealed the potentiality of organic sources towards achieving improved cob quality of sweet corn.

Analyzing the Impact of Feature Selection on Crop Yield Prediction

In agriculture sector, machine learning has been widely used by researchers for crop yield prediction. However, it is quite difficult to identify the most critical features from a dataset. Feature selection techniques allow us to remove the extraneous and noisy features from the original feature set. The feature selection techniques help the model to focus only on the important features of the data, thus reducing execution time and improving efficiency of the model. The aim of this study is to determine relevant subset features for achieving high predictive performance by using different feature selection techniques like Filter methods, Wrapper methods and embedded methods. In this work, different feature selection techniques like Rank-based feature selection technique, weighted feature selection technique and Hybrid Feature Selection Technique have been applied to the agricultural data. The optimal feature set returned by different feature selection techniques is used for yield prediction using Linear regression, Random Forest, and Decision Tree Regressor. The accuracy of prediction obtained using the above three methods has been analyzed by using different evaluation parameters. This study helps in increasing predictive accuracy with the minimum number of features.

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.

Ensemble learning prediction of soybean yields in China based on meteorological data

The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning.Although previous studies have used machine learning algorithms to predict soybean yield based on meteorological data,it is not clear how different models can be used to effectively separate soybean meteorological yield from soybean yield in various regions. In addition, comprehensively integrating the advantages of various machine learning algorithms to improve the prediction accuracy through ensemble learning algorithms has not been studied in depth. This study used and analyzed various daily meteorological data and soybean yield data from 173 county-level administrative regions and meteorological stations in two principal soybean planting areas in China(Northeast China and the Huang–Huai region), covering 34 years.Three effective machine learning algorithms(K-nearest neighbor, random forest, and support vector regression) were adopted as the base-models to establish a high-precision and highly-reliable soybean meteorological yield prediction model based on the stacking ensemble learning framework. The model's generalizability was further improved through 5-fold crossvalidation, and the model was optimized by principal component analysis and hyperparametric optimization. The accuracy of the model was evaluated by using the five-year sliding prediction and four regression indicators of the 173 counties, which showed that the stacking model has higher accuracy and stronger robustness. The 5-year sliding estimations of soybean yield based on the stacking model in 173 counties showed that the prediction effect can reflect the spatiotemporal distribution of soybean yield in detail, and the mean absolute percentage error(MAPE) was less than 5%. The stacking prediction model of soybean meteorological yield provides a new approach for accurately predicting soybean yield.

Effects of residual plastic film on crop yield and soil fertility in a dryland farming system

Plastic film mulch in agricultural production becomes essential to maintaining crop yields in arid and semiarid areas.However,the presence of residual film in farmland soil has also drawn much attention.In this study,three experiments were conducted.The first two experimental designs included 0,450,1350,and 2700 kg ha^(-1) of residual film pieces of approximately 5 cm side length added to field soil(0-20 cm soil depth)for seven years and added to pots for four years.In the third experiment,1350 kg ha^(-1)of the residual film with different side lengths(2-5,5-10,10-15,and 15-20 cm)was added to field soil for six years to explore the effect of residual film fragment size on soil nutrients,soil microorganisms,crop growth and yields.The residual film had little effect on the soil moisture at a field depth of 0-2(or 0-1.8)m.There were no significant effects on organic carbon,total nitrogen,inorganic nitrogen,total phosphorus or available phosphorus in the 0-20 cm soil layer.The presence of residual film decreased the richness and diversity of the bacterial community of the surface soil of the residual film,but it had no significant effect on the microbial community of the non-surface soil.The emergence rates of wheat and lentils occasionally decreased significantly with different amounts of residue fragments added to the field.At 450-2700 kg ha^(-1),the residual film reduced the plant height and stem diameter of maize and significantly reduced the shoot biomass of harvested maize by 11-19%.The average yields of maize and potato over the seven years decreased,but there were almost no significant statistical differences among the treatments.These results provide important data for a comprehensive scientific understanding of the effects of residual film on soil and crops in dryland farming systems.

In vitro and in silico studies of salicylic acid on systemic induced resistance against bacterial leaf blight disease and enhancement of crop yield

Salicylic acid(SA)is an effective elicitor to promote plant defenses and growth.This study aimed to investigate rice(Oryza sativa L.)cv.Khao Dawk Mali 105 treated with salicylic acid(SA)-Ricemate as an enhanced plant protection mechanism against bacterial leaf blight(BLB)disease caused by Xanthomonas oryzae pv.oryzae(Xoo).Results indicated that the use of SA-Ricemate as a foliar spray at concentrations of more than 100 mg L^(-1)can reduce the severity of BLB disease by 71%.SA-Ricemate treatment also increased the hydrogen peroxide(H_(2)O_(2))content of rice leaf tissues over untreated samples by 39–61%.Malondialdehyde(MDA)in rice leaves treated with SA-Ricemate also showed an increase of 50–65%when comparing to non-treated samples.The differential development of these defense compounds was faster and distinct when the SA-Ricemate-treated rice was infected with Xoo,indicating plant-induced resistance.Besides,SA-Ricemate elicitor at a concentration of 50–250 mg L^(-1)was correlated with a substantial increase in the accumulation of total chlorophyll content at 2.53–2.73 mg g^(-1)of fresh weight which suggests that plant growth is activated by SA-Ricemate.The catalase-and aldehyde dehydrogenase-binding sites were searched for using the CASTp server,and the findings were compared to the template.Chemsketch was used to design and optimize SA,which was then docked to the catalase and aldehyde dehydrogenase-binding domains of the enzymes using the GOLD 3.0.1 Software.SA is shown in several docked conformations with the enzymes catalase and aldehyde dehydrogenase.All three catalase amino acids(GLN7,VAL27,and GLU38)were discovered to be involved in the creation of a strong hydrogen bond with SA when SA was present.In this mechanism,the aldehyde dehydrogenase amino acids LYS5,HIS6,and ASP2 were all implicated,and these amino acids created strong hydrogen bonds with SA.In field conditions,SA-Ricemate significantly reduced disease severity by 78%and the total grain yield was significantly increased which was an increase of plant height,tiller per hill,and panicle in three field trials during Aug–Nov 2017 and 2018.Therefore,SA-Ricemate can be used as an alternative elicitor on replacing harmful pesticides to control BLB disease with a high potential of increasing rice defenses,growth,and yield components.

Spatial Pattern of Cotton Yield Variability and Its Response to Climate Change in Cotton Belt of Pakistan

Cotton is a revenue source for cotton-producing countries;as the second-largest crop in Pakistan,it significantly contributes to its economy.Over the past few decades,cotton productivity has become unstable in Pakistan,and climate change is one of the main factors that impact cotton yield.Due to climate change,it becomes very important to understand the change trend and its impact on cotton yield at the regional level.Here,we investigate the relationship of standardized cotton yield variability with the variability of climate factors using a 15-yr moving window.The piecewise regression was fitted to obtain the trend-shifting point of climate factors.The results show that precipitation has experienced an overall decreasing trend of–0.64 mm/yr during the study period,with opposing trends of–1.39 mm/yr and 1.52 mm/yr before and after the trend-shifting point,respectively.We found that cotton yield variability increased at a rate of 0.17%/yr,and this trend was highly correlated with the variability of climate factors.The multiple regression analysis explains that climate variability is a dominant factor and controlled 81%of the cotton production in the study area from 1990 to 2019,while it controlled 73%of the production from 1990 to 2002 and 84%from 2002 to 2019.These findings reveal that climate factors affact the distinct spatial pattern of changes in cotton yield variability at the tehsil level.

Influence of Straw Incorporation on Maize Yield,N Accumulation and Remobilization on Slope Farmland in Northeast China

Slope farmland is a main type of agricultural land throughout northeast China.Long-term high intensity utilization and unreasonable farming have caused the deterioration of soil resources and a decrease in crop production.Here,it was hypothesized that crop straw incorporation might help to reduce nutrient losses and increase maize yields across time and space.A field experiment for testing straw management practices on maize across three slope positions(top,back and bottom slopes)was conducted in Northeast China in 2018 and 2019.In this study,the dry matter accumulation(DMA),N accumulation(NA),N remobilization,postsilking N uptake and grain yield were measured under SI(straw incorporation)and NSI(no straw incorporation)across three slope positions of 100-m-long consecutive black soil slope farmland during the maize(Zea mays L.)growth stages.Compared with NSI,SI significantly increased DMA and NA at the silking and maturity stages.SI typically increased the N remobilization in all slope positions,and significantly increased N remobilization efficiency and contribution of N remobilization to grain on the back and bottom slopes.However,post-silking N uptake was only increased by SI on the top slope.SI generally increased the crop yield in three slope positions.In the SI treatments,the bottom slope was the highest yield position,followed by the top,and then the back slopes,suggesting that the bottom slope position of regularly incorporated straw might have increased the potential for boosting maize yield.Overall,the study demonstrated the outsized potential of straw incorporation to enhance maize NA and then increase the grain yield in black soil slope farmland.

AI Method for Improving Crop Yield Prediction Accuracy Using ANN

Crop Yield Prediction(CYP)is critical to world food production.Food safety is a top priority for policymakers.They rely on reliable CYP to make import and export decisions that must be fulfilled before launching an agricultural business.Crop Yield(CY)is a complex variable influenced by multiple factors,including genotype,environment,and their interactions.CYP is a significant agrarian issue.However,CYP is the main task due to many composite factors,such as climatic conditions and soil characteristics.Machine Learning(ML)is a powerful tool for supporting CYP decisions,including decision support on which crops to grow in a specific season.Generally,Artificial Neural Networks(ANN)are usually used to predict the behaviour of complex non-linear models.As a result,this research paper attempts to determine the correlations between climatic variables,soil nutrients,and CYwith the available data.InANN,threemethods,Levenberg-Marquardt(LM),Bayesian regularisation(BR),and scaled conjugate gradient(SCG),are used to train the neural network(NN)model and then compared to determine prediction accuracy.The performance measures of the training,as declared above,such as Mean Squared Error(MSE)and correlation coefficient(R),were determined to assess the ANN models that had been built.The experimental study proves that LM training algorithms are better,while BR and SCG have minimal performance.

Straw return increases crop production by improving soil organic carbon sequestration and soil aggregation in a long-term wheat-cotton cropping system

Straw return is a promising strategy for managing soil organic carbon(SOC)and improving yield stability.However,the optimal straw return strategy for sustainable crop production in the wheat(Triticum aestivum L.)-cotton(Gossypium hirsutum L.)cropping system remains uncertain.The objective of this study was to quantify the long-term(10 years)impact of carbon(C)input on SOC sequestration,soil aggregation and crop yields in a wheat-cotton cropping system in the Yangtze River Valley,China.Five treatments were arranged with a single-factor randomized design as follows:no straw return(Control),return of wheat straw only(Wt),return of cotton straw only(Ct),return of 50%wheat and 50%cotton straw(Wh-Ch)and return of 100%wheat and 100%cotton straw(Wt-Ct).In comparison to the Control,the SOC content increased by 8.4 to 20.2%under straw return.A significant linear positive correlation between SOC sequestration and C input(1.42-7.19 Mg ha^(−1)yr^(−1))(P<0.05)was detected.The percentages of aggregates of sizes>2 and 1-2 mm at the 0-20 cm soil depth were also significantly elevated under straw return,with the greatest increase of the aggregate stability in the Wt-Ct treatment(28.1%).The average wheat yields increased by 12.4-36.0%and cotton yields increased by 29.4-73.7%,and significantly linear positive correlations were also detected between C input and the yields of wheat and cotton.The average sustainable yield index(SYI)reached a maximum value of 0.69 when the C input was 7.08 Mg ha^(−1)yr^(−1),which was close to the maximum value(SYI of 0.69,C input of 7.19 Mg ha^(−1)yr^(-1))in the Wt-Ct treatment.Overall,the return of both wheat and cotton straw was the best strategy for improving SOC sequestration,soil aggregation,yields and their sustainability in the wheat-cotton rotation system.

Effects of different ration of NPK fertilizer on the grain yield and protein content in forage rice

Different ratios of NPK were adopted in this research to study its effects on the objective traits of 2 early forage-rice varieties, and to obtain the optimum ratio to further improve the application technique in theory. At the same time, the possibility of increasing yield and protein content in the grain through cultivation technique was also studied. The conclusions were：

Combined Application of Biostimulants and EDTA Improved Wheat Productivity under Cadmium Stress

Wheat(Triticum aestivum L.)exhibits a greater capacity for cadmium(Cd)absorption compared to other cereal crops,leading to elevated daily Cd intake,and posing a significant threat to public health.For the mitigation of Cd stress in sustainable and environmentally friendly way,a pot study was designed by using exogenous application of various biostimulants,i.e.,Nigella sativa and Ocimum sanctum extracts:0%,10%,and 20%in combination with the chelating agent ethylenediaminetetraacetic acid(EDTA)using 0 and 5 mg kg^(-1) under various levels of Cd stress(i.e.,0,5,10,and 15 mg kg^(-1) soil).Results revealed that Cd stress significantly reduced the seed emergence,growth,root,and allometric characters and yield-related parameters of wheat crops.The most observable reduc-tion was documented in wheat plants exposed to a higher Cd concentration(15 mg kg^(-1)),followed by the lower Cd level(control).The combined application of bio-stimulants and EDTA minimized the negative impacts of Cd stress.The highest increase in seedling emergence(5.44%),leaf area(50.60%),number of tillers(31.02%),grain yield per plant(24.28%),biological yield(13.97%),and decrease in Cd levels in grains(40%)was noticed where 20%foliar application of N.sativa and 10%of O.sanctum biostimulants were done using 5 mg kg^(-1) of soil-applied EDTA.This intervention demonstrated a notable reduction in Cd-induced negative effects,highlighting the potential of these substances in promoting sustainable wheat cultivation in contaminated environments.Moreover,it is an eco-friendly and approachable method at thefield level able to ensure food safety.

Responses of Crop Yields and Soil Fertility to Long-term Nutrient Lacking

[Objective] Long-term （over 18 years） fertilization experiments were con- ducted to study the responses of crop yields and soil fertility to long-term nutrient lacking at Zhengzhou in China. [Method] The present study consisted of five treat- ments： 1 CK （no fertilizer or manure）, （2） NP （nitrogen and phosphorus fertilizer applied）, 31 NK （nitrogen and potassium fertilizer applied）, 4 PK （phosphorus and potassium fertilizer applied） and ：5 NPK （nitrogen, phosphorus and potassium fertil- izer applied）. [Result] Lacking of nitrogen or phosphorus led to a low yield; however, there was no significant difference in grain yields between the NP and NPK treat- ments which maintained a higher yield. Receiving no phosphorus, soil available phosphorus content declined to about 2.5 mg/kg. The concentration of soil ex- changeable potassium remained constant at a level of 60 mg/kg under the treat- ments without potassium fertilizer addition. Soil potassium spontaneous supply ca- pacity fluctuated around 100%.[Conclusion] In fluvo-aquic soil, nitrogen and phos- phorus were two key limiting factors to grain yields, biomass and yield component factors of wheat and maize, while potassium was not. However, potassium defi- ciency may occur in the future if there was still no potassium fertilizer applied.

Impacts of Micro- and Nano-Plastics on Soil Properties and Plant Production in Agroecosystems: A Mini-Review

Micro- and nano-plastics (MNPs) are tiny plastic particles resulting from plastic product degradation. Soil MNPs have been identified as potential influential factors affecting various soil properties and crop biomass productivity. This mini-review provides a synthesis of recent findings concerning their effects on soil physicochemical properties, microorganisms, organic carbon content, soil nutrients, greenhouse gas emissions, soil fauna, and their impacts on plant ecophysiology, growth, and production. The results indicate that MNPs may markedly impede soil aggregation ability, increase porosity, decrease soil bulk density, enhance water retention capacity, influence soil pH and electrical conductivity, and escalate soil water evaporation. Exposure to MNPs may predominantly induce changes in soil microbial composition, reducing the diversity and complexity of microbial communities and microbial activity while enhancing soil organic carbon stability, influencing soil nutrient dynamics, and stimulating organic carbon decomposition and denitrification processes, leading to elevated soil respiration and methane emissions, and potentially decreasing soil nitrous oxide emission. Additionally, MNPs may adversely affect soil fauna, diminish seed germination rates, promote plant root growth, yet impair plant photosynthetic efficacy and biomass productivity. These findings contribute to a better understanding of the impacts and mechanistic foundations of MNPs. Future research avenues are suggested to further explore the impacts and economic implications.

Crop Yield and Soil Properties in the First 3 Years After Biochar Application to a Calcareous Soil

It remains unclear whether biochar applications to calcareous soils can improve soil fertility and crop yield. A long-term field experiment was established in 2009 so as to determine the effect of biochar on crop yield and soil properties in a calcareous soil. Five treatments were： 1） straw incorporation; 2） straw incorporation with inorganic fertilizer; 3）, 4） and 5） straw incorporation with inorganic fertilizer, and biochar at 30, 60, and 90 t ha-l, respectively. The annual yield of either winter wheat or summer maize was not increased significantly following biochar application, whereas the cumulative yield over the first 4 growing seasons was significantly increased. Soil pH, measured in situ, was increased by a maximum of 0.35 units after 2 yr following biochar application. After 3 yr, soil bulk density significantly decreased while soil water holding capacity increased with adding biochar of 90 t ha^-1. Alkaline hydrolysable N decreased but exchangeable K increased due to biochar addition. Olsen-P did not change compared to the treatment without biochar. The results suggested that biochar could be used in calcareous soils without yield loss or significant impacts on nutrient availability.

Effect of Long-Term Application of K Fertilizer and Wheat Straw to Soil on Crop Yield and Soil K Under Different Planting Systems

Effect of application of K fertilizer and wheat straw to soil on crop yield and status of soil K in the plough layer under different planting systems was studied. The experiments on long-term application of K fertilizer and wheat straw to soil in Hebei fluvo aquic soil and Shanxi brown soil in northern China were begun in 1992. The results showed that K fertilizer and straw could improve the yields of wheat and maize with the order of NPK ＋ St 〉 NPK 〉 NP ＋ St 〉 NP, and treatment of K fertilizer made a significant difference to NP, and the efficiency of K fertilizer in maize was higher than in wheat under rotation system of Hebei. In contrast with Shanxi, the wastage of soil potassium was a more serious issue in the rotation system in Hebei, only treatment of NPK ＋ St showed a surplus of potassium and the others showed a wane. K fertilizer and straw could improve the content of water-soluble K, nonspecifically adsorbed K, non-exchangeable K, mineral K, and total K in contrast to NP; however, K fertilizer and straw reduce the proportion of mineral K and improve proportion of other forms of potassium in the two locating sites. Compared with the beginning of orientation, temporal variability character of soil K content and proportion showed a difference between the two soil types; furthermore, there was a decrease in the content of mineral K and total K simultaneously in the two locating sites. As a whole, the effect of K fertilizer applied to soil directly excelled to wheat straw to soil. Wheat straw to soil was an effective measure to complement potassium to increase crop yield and retard the decrease of soil K.

The Possible Effect of Climate Warming on Northern Limits of Cropping System and Crop Yield in China

Significantly increasing temperature since the 1980s in China has become a consensus under the background of global climate change and how climate change affects agriculture or even cropping systems has attracted more and more attention from Chinese government and scientists. In this study, the possible effects of climate warming on the national northern limits of cropping systems, the northern limits of winter wheat and double rice, and the stable-yield northern limits of rainfed winter wheat-summer maize rotation in China from 1981 to 2007 were analyzed. Also, the possible change of crop yield caused by planting limits displacement during the periods 1950s-1981 and 1981-2007 was compared and discussed. The recognized calculation methods of agricultural climatic indices were employed. According to the indices of climatic regionalization for cropping systems, the national northern limits of cropping systems, winter wheat and double rice, and the stable-yield northern limits of rainfed winter wheat-summer maize rotation during two periods, including the 1950s-1980 and 1981-2007, were drawn with ArcGIS software. Compared with the situation during the 1950s- 1980, the northern limits of double cropping system during 1981-2007 showed significant spatial displacement in Shaanxi, Shanxi, Hebei, and Liaoning provinces and Beijing municipality, China. The northern limits of triple cropping system showed the maximum spatial displacement in Hunan, Hubei, Anhui, Jiangsu, and Zhejiang provinces, China. Without considering variety change and social economic factors, the per unit area grain yield of main planting patterns would increase about 54-106% if single cropping system was replaced by double cropping system, which turned out to be 27- 58% if double cropping system was replaced by triple cropping system. In Liaoning, Hebei, Shanxi, Shaanxi, Gansu, and Qinghai provinces, Inner Mongolia and Ningxia autonomous regions, China, the northern limits of winter wheat during 1981-2007 moved northward and expanded westward in different degrees, compared with those during the 1950s-1980. Taking Hebei Province as an example, the northern limits of winter wheat moved northward, and the per unit area grain yield would averagely increase about 25% in the change region if the spring wheat was replaced by winter wheat. In Zhejiang, Anhui, Hubei, and Hunan provinces, China, the planting northern limits of double rice moved northward, and the per unit area grain yield would increase in different degrees only from the perspective of heat resource. The stable- yield northern limits of rainfed winter wheat-summer maize rotation moved southeastward in most regions, which was caused by the decrease of local precipitation in recent years. During the past 50 yr, climate warming made the national northern limits of cropping systems move northward in different degrees, the northern limits of winter wheat and double rice both moved northward, and the cropping system change would cause the increase of per unit area grain yield in the change region. However, the stable-yield northern limits of rainfed winter wheat-summer maize rotation moved southeastward due to the decrease of precipitation.