Water production function of artificial grassland crop in arid desert area of Northern Xinjiang

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Water production function of artificial grassland crop in arid desert area of Northern Xinjiang

LIU Hu1*, YIN Chunyan2,3, WEI Yongfu1, ZHANG Ruiqiang1, GAO Tianming1

(1.InstituteofWaterResourcesforPastoralArea,MinistryofWaterResources,Hohhot010020,China; 2.YantaiInstituteofCoastalZoneResearch,ChineseAcademyofSciences,Yantai264003,Shandong,China; 3.SoilandWaterConservationBureauofWushenBanner,WushenBanner017300,InnerMongolia,China)

Summary The goal of traditional irrigation is to provide proper moisture for crops and to get the highest yield per unit area. However, on background of water deficits and associated economics in the arid desert regions of Northern Xinjiang, relations between water input and crop yield have become a hot topic. In this study, insufficient irrigation test was carried out in the northern region, and the yields of alfalfa, Sudan grass and silage corn were analyzed under different soil water conditions, and the water production function model of these artificial grasslands was determined; meanwhile, the water sensitivity index or coefficient for alfalfa, Sudan grass and silage corn in each growth stage was calculated by using Jensen model, Stewart model and Jensen model, respectively. The test results showed that the models had a high precision, and the average relative errors for alfalfa, Sudan grass and silage corn were 6.51%, 9.24% and 9.25%, respectively. The research results can provide theoretical and technical support for the rational development of limited water and soil resources in Altay grassland and pasture of Xinjiang.

Key wordsNorthern Xinjiang; artificial grassland; crop water production model; Jensen model; Stewart model

CLC numberS 275.3Document codeA

The crop water production function, namely the model of crop response to water, can predict the influence of water deficit of different degrees in different stages on the yield in the crop growth process. This function analyzes the relationship between water and yield on assumption that other agricultural technical and agricultural factors are consistent, reflecting the response of crop yield to water change[1-2]. It is the most basic function to regulate deficit irrigation, and also an important basis for reasonable allocation of the water resources, to achieve the maximum crop yield by optimizing the irrigation system[1-2]. Water sensitive index (λorK) of crop water function is the key for guidance and implement of water-saving irrigation and water management, making different crops adapt to water deficit in different periods.

Up to now, crop water production function has been intensively studied in the world. SINGHetal.[3], BLANK[4]and STEWARTetal.[5]established the additive model to describe the relationship between water and yield. JENSEN[6]put forward the multiplicative model in 1986. In China, KANGetal.[7]carried out the relevant researches on distinguishing methods for crop water deficit status and irrigation index. TANG[8]discussed the water requirement law and characteristic of rice cultivated in aerobic soil with different water deficit levels in different growth periods, analyzed the change law of different water deficit periods and water deficit degrees with the yield, and calculated the water sensitive coefficient and water production function of the rice. The research of SUNetal.[9]showed that Jensen model could better reflect the relationship between water and yield of winter wheat. SUN[10]analyzed the influence of drought on the yield in different periods, and calculated the water sensitive coefficients of wheat, corn and cotton in different growth periods by regression analysis method. ZHANG[11]analyzed the water sensitive coefficients of corn cultivated under different soil conditions in different growth periods, fit the growth curve through distribution fitting method, and analyzed the accumulation function of sensitive indexes in Jensen model. WANG[12]analyzed and obtained the water requirement law of super rice through the insufficient irrigation test data, and made calculations with several crop water production functions, thinking that Jensen model was the best crop water production function for Yongji, Jilin Province. However, CHENGetal.[13]thought that Jensen model could better reflect the relationship between rice water and yield in East Jilin Province, and the relationship model between number of days after rice transplanting and sensitive index was established through the insufficient irrigation test. CHEN[14]analyzed the influence of different water stresses on the final yield and the applicability of frequently-used water production functions in central Liaoning, and the crop water sensitive index in each period was calculated. SONG[15]solved the water production function of irrigation quota and evapotranspiration with crop yield in Shihezi, and preliminarily obtained the suitable drip irrigation system for silage corn and oil sunflower.

The crop water production functions have been intensively studied all over the world, but not in Northern Xinjiang. At present, crop water production function was unavailable for this region, especially the water production function for alfalfa and Sudan grass. In this paper, the relationships were investigated among forage crop yield treated with different irrigations, the total evapotranspiration in the whole growth period and the water consumption in each stage, to develop the sensitive index of artificial grassland in different growth periods, to calculate the crop-water model of artificial grassland in arid desert area of Northern Xinjiang, aimed at providing technical support and theoretical basis for efficient and reasonable allocation and utilization of the water resources, in the process of developing, managing and utilizing irrigated forage grass fields in the area.

1Materials and methods

1.1Overview of the research area

The experimental area located inside the Altay Experiment Station, Institute of Water Resources for Pastoral Area of Ministry of Water Resources in Fuhai County, Altay Prefecture, Xinjiang (87°40′22″ E, 46°10′45″ N). The field surface elevation is 558 m above sea level. In this area, the terrain is flat with barren soil. The soil layer is 40 cm thick, and is mainly composed of sandy-loam soil. The annual average sunshine duration is 2 881 hours, and the total annual average solar radiation amount is 546.7 kJ/cm2. The annual average temperature is 3.4 ℃, and the annual accumulated average temperature above 10 ℃ is 2 904.9 ℃. The annual average frost-free period is about 150 days. The experimental area is a Gobi desert area, with arid climate insufficient in rainfall. The annual average evaporation is 1 830 mm, and the annual average precipitation is 112.7 mm.

1.2Experiment design

The low pressure pipe irrigation is adopted in this study. Seventeen irrigation treatments were set up as shown in Table 1. Every treatment is repeated three times, and the plot layout is conducted according to irrigation experiment standard[16]. The area of the experimental plot is 10 m×6 m=60 m2; between two experimental plots, a 1 m-wide protection zone was set up to avoid interaction. In the middle of the field, there is an underground water observation well. The experimental grasses are alfalfa (Algonquin), Sudan grass (Qitai Sudan grass) and silage corn (Xinyu 10). The silage corn should be reaped and stored in good time during the milk stage, and the alfalfa should be reaped and stored in good time at the end of the flowering period; after harvest of the alfalfa, water and fertilizer management should be strengthened to resume the growth as soon as possible.

Table 1　Design of experimental treatments of alfalfa, Sudan grass and silage corn

The water contents listed in the table are all the lower limit values and the upper limit ones of the field moisture capacity.

1.3Observation contents of the experiment

The observation contents of the experiment include meteorological data, increment of underground water, soil physical properties, soil water content and crop yield.

The meteorological data are from the field meteorological station inside the HOBO U30 Station, including daily maximum, minimum and average temperatures, evaporation, sunshine duration, wind speed at 2 m height, maximum, minimum and average relative humidities, precipitation, radiation and so on. The increment of underground water was measured through the observation well, once every three days. The soil water diffusivity (D) was measured through taking back undisturbed soil from the project area to make horizontal soil column; the soil water characteristic curve was obtained by adopting 1600-type 5 Bar pressure film instrument and then the soil hydraulic conductivity (K) was deduced. A positioning flux method was adopted to calculate the increment of underground water to the planned moisture layer. Soil physical properties included the water content, field moisture capacity, soil bulk density, and porosity before the experiment start. Soil water contents at the depths of 0, 10, 30 and 50 cm were measured in every five days, using TRIME-PICO TDR portable soil water measurement instrument. Crop yield was achieved by measuring the yield at the final stage of every growth period using quadrat method.

2Results

2.1Analysis on experimental data

The water balance equation was adopted to calculate actual evapotranspiration (ETa) of the artificial grassland (the computing process was not presented). According to the experimental data, we could obtain maximum evapotranspiration (ETm), the actual yield (Ya) and the maximum yield (Ym) of different forage grasses in the whole growth period, as shown in Table 2.

Table 2　ETa, ETm, Ya and Ym of alfalfa, Sudan grass and silage corn under different treatment conditions

ETais the actual evapotranspiration under different water deficit treatments; ETmis the maximum evapotranspiration when the irrigation is sufficient;Yastands for the actual yield of grass under different water deficit treatments;Ymstands for the maximum yield when the irrigation is sufficient.

In the growth and developmental process of alfalfa, water stress could lead to a series of bad consequences. The most obvious manifestation was that the plant became lower and that the crop yield decreased. The results of different treatments showed that the crop yield decreased obviously with the decrease of soil water content. According to the Table 2, under the sufficient irrigation condition, the yield of alfalfa did not decrease. When the soil water content was controlled at 50% of the field moisture capacity, the reduction rate of yield reached 29.77%.

The yields of Sudan grass varied with different water content conditions (Table 2). The highest yield was observed under the sufficient irrigation condition, and the lowest one was under the heavy drought, with the yield reduction rate of 21.18%. Moreover, the yield reduction and its rate varied along with different drought degrees, which were linearly dependent on the soil water content.

According to the Table 2, water stress could lead to a series of bad consequences for silage corn at different stages of the growth and developmental process, and the most obvious influence was reduction of the crop yield. In case of drought in a single stage, the heavy drought in the booting stage was the most serious (the yield reduced by 55.96%). That is because this stage is the reproductive growth stage of the corn; the heavy drought in this stage will seriously affect the yield at later stage. Among the seedling stage, jointing stage and filling stage, the seedling stage was the most sensitive one (the yield reduced by 36.76%), because silage corn in the project area was sowed in early summer, and dry, hot and rainless days with high-temperature came after sowing. Therefore, in the seedling stage, irrigation should be conducted to supplement sufficient water. Besides, the water physiological activity of silage corn was different from other crops, the key cultivation management of which was to promote tillering and jointing rather than “hardening of seedling” for other crops. If suffering from drought in this stage, the yield may reduce by 19.88%. The project area was mainly composed of Gobi desert and soil with fast evaporation and poor water retention capacity, so the water on the surface would infiltrate or evaporate quickly after irrigation. Therefore, “more times with less amount” irrigation system should be adopted to avoid the state of being dry surface and wet inner layer so as to promote tillering.

2.2Model selection for the growth period

At present, the crop water production function models mainly reflected the whole growth period and various growth stages. Based on the results achieved by previous research, combining the objective conditions of the experimental station, Jensen model, Minhas model, Blank model, Singh model and Stewart model were selected to analyze and study the response of crop yield of artificial grassland to water deficit levels.

Jensen model: Water deficit in a certain growth stage does not only affect this growth stage, but also will exert a hysteresis effect on the next stage; it is a multiplicative model established with a staged relative evapotranspiration as an independent variable.

(Equation 1)

whereYastands for the actual yield of grass under different water deficit treatments, kg/hm2;Ymstands for the maximum yield when the irrigation is sufficient, kg/hm2; ETais the actual evapotranspiration under different water deficit treatments; ETmis the maximum evapotranspiration when the irrigation is sufficient;iis the number of the stages;nis the total number of stages of the model established;λis the water sensitive index, reflecting the sensitivity of grass to the influence of water deficit in different stages on the crop yield. ETm/ETa≥1.0,λi≥0. The largerλiis, the smallerYa/Ymis after the multiple multiplication, showing that the influence of stageion the yield is larger. On the contrary, the smallerλiis, the smaller the influence on the yield is.λiis the key parameter of the multiplicative model. Jensen model was originally derived from the yield of maize seeds.

Minhas model: It is a crop water production model with the relative water deficit in different growth stages as an independent variable and is also a multiplicative model.

(Equation 2)

where the meanings ofYa,Ym, ETa, ETmandλiare the same with the equation 1;α0stands for correction factor of other factors rather than water deficit toYa/Ym; in the single-factor water production function,α0=1[17];b0stands for power exponent of the independent variable; usually,b0=2.0.

Blank model: It is an additive model with a staged relative evapotranspiration as an independent variable, and it is represented by its product and sensitive coefficient of corresponding stage.

(Equation 3)

where the meanings ofYa,Ym, ETaand ETmare the same with the equation 1;Kistands for sensitive coefficient of water deficit of artificial grass in different growth stages to the grass yield. Because ETa/ETm≤1.0,Ki>0, the largerKiis, the largerYa/Ymis after multiple addition of various stages, showing that the influence on the yield is smaller.

Singh model: It is a crop water production model with relevant water deficit of various growth stages as a variable, and it is represented by its product and sensitive coefficientKiof corresponding stage. It is an additive model.

(Equation 4)

where the meanings ofYa,Ym, ETaand ETmare the same with the equation 1;b0stands for empirical coefficient (b0=2).

Stewart model: It is an additive model with relevant water deficit of various growth stages as a variable, and it is represented by its product and sensitive coefficientKiof corresponding stage.

(Equation 5)

where the meanings ofYa,Ym, ETaand ETmare the same with the equation 1.

2.3Water deficit sensitive indexes in different growth stages

2.3.1Deduction and analysis of crop sensitive indexes

The crop sensitive indexes represented the influence of water deficit on the crop yield in different stages, and they changed along with the environment. Besides, the sensitivity to water deficit changed for different crops, as well as different growth stages. Larger sensitivity in a stage would bring higher yield reduction by unit water deficit. The crop sensitive indexes of alfalfa, Sudan grass and silage corn were deduced according to the selected five models. The models were all transformed to be unified linear equations by formal transformation, taking logarithm for both sides of the equations; the least square method was adopted, to obtain a normal equation system about water deficit sensitive indexes; the matrix method was used to solve the above normal equation system, and then get the crop stage sensitive indexes or coefficients. The calculation results were shown in Tables 3-5.

Table 3　Crop water deficit sensitive indexes or coefficients of alfalfa in different water production function models

Table 4　Crop water deficit sensitive indexes or coefficients of Sudan grass in different water production function models

Table 5　Crop water deficit sensitive indexes or coefficients of silage corn in different water production function models

Next, the crop sensitive indexes according to the calculation results in the Tables 3-5 were analyzed as follows:

For alfalfa, in Singh model a negative value appeared in the returning green stage. According to its physical significance, the water deficit in this growth stage posed an certain promoting rather than inhibiting effect on the yield, which was contradictory to the actual situation. However, this may have something to do with the crop physiology, or the constraint that should be kept between positive and negative terms, when searching optimization in the least square method was adopted; in such a case, the existence of negative value was reasonable. However, the correlation coefficient of this modelR2=0.495 8, which was relatively low; andF

Generally, the appearance of negative values in the model can be explained from the following two aspects. First, in the early growth period, negative values may appear easily. Studies showed that the water deficit in early stage could make the crops adapt to water deficit and gain higher yield. Therefore, it is reasonable for the water sensitivity index to be negative in the early growth stage. The super compensation effect, that the yield of alfalfa can increase by water deficit in early growth stage, needs further study in the future. Second, CHENetal.[17]pointed out that multiple regression analysis was a statistical method which could be adopt to solve the water sensitive index. In the model deduction process, according to the requirement of processing data by least square method, when residual sum of squares is minimum because of positive and negative coordination, negative values may appear.

For Sudan grass, in Jensen model, Minhas model and Singh model,FF0.05, showing obvious regression effect. The sensitive index in the seedling stage was the largest in Blank model. According to its definition, the sensitivity is the smallest for the stage where the sensitive coefficientKiis the largest. The model reflected that the influence of water deficit on the yield was the smallest in the seedling stage, which was inconsistent with the experience in harvesting of Sudan grass. In Stewart model, the order of sensitive coefficients was filling-milk stage > booting-flowering stage > tillering-jointing stage > seedling stage. Therefore, the Sudan grass in filling-milk stage was most sensitive to water.

For silage corn, in Jensen model, the water sensitive indexλin booting-flowering stage was the highest; in the growth period, the order forλwas booting-flowering stage > seedling stage > filling-milk stage > tillering-jointing stage > sowing-seedling stage. According to the definition of Jensen model, higher yield reduction rate (lowerYa/Ym) came along with largerλafter water deficit. Therefore, the stage order about sensitive degree of water deficit to the yield, which was reflected in Jensen model in the Table 5, should be consistent with the water physiological theory and local actual irrigation experience for silage corn. The correlation coefficient of this modelR2=0.893 3, andF>F0.05, so the regression effect was obvious. Hence, Jensen model was more suitable for predicting the yield of silage corn in arid desert area in Northern Xinjiang. In Stewart model and Blank model,F>F0.05, so the regression effect was obvious. Meanwhile, the sensitive coefficients in Stewart model and Blank model were largest in the seedling stage and tillering-jointing stage, respectively. According to their definitions, the sensitivity is the smallest for the stage where the sensitive coefficientKiis the largest. Namely, the two models reflected that the influence of water deficit on the yield was the smallest in the seedling stage and tillering-jointing stage, which was inconsistent with the experience in harvest of silage corn. The sensitive indexes in three stages of Minhas model were larger than 1, which was unreasonable.

2.3.2Determination of crop water production functions

According to the above discussion and analysis, the water production function model built for alfalfa and silage corn in arid desert area in Northern Xinjiang is Jensen model, and for Sudan grass is Stewart model.

Water production function of alfalfa was expressed by

Water production function of Sudan grass was expressed by

Water production function of silage corn was expressed by

In order to test the reliability of the models, we substituted the group data of evapotranspiration and yield of alfalfa, Sudan grass and silage corn in Xinjiang experimental area under different treatments into the above models, and then the predicted yield and relative errors were calculated and presented in Table 6. The test result showed that the maximum relative errors of the yield for alfalfa, Sudan grass and silage corn simulated by the models were 11.71%, 15.47% and 22.27%, respectively; and the average relative errors were 6.51%, 9.24% and 9.25%, respectively. Therefore, the models have a relatively high accuracy.

3Discussion

For alfalfa, the drought in the returning green-branching stage led to the minimum yield reduction rate and the minimum water production efficiency; the drought in the whole growth period led to the minimum yield reduction rate and the maximum water production efficiency. The rule of Sudan grass was similar with alfalfa; a linear relation was found between the crop yield reduction rate and drought situations. For silage corn, the yield under the light drought in the booting-flowering stage was lower than sufficient irrigation; its water production efficiency was maximum among all the insufficient irrigation treatments, and the water consumption was only 76% of the sufficient irrigation. And the drought in jointing-heading stage led to the greatest influence on the yield and the maximum yield reduction rate. Whereas the drought

Table 6　Results of crop water production model simulating the yield of artificial grassland

in the seedling-jointing stage led to the minimum water production efficiency.

Jensen model, Stewart model and Jensen model were respectively adopted for alfalfa, Sudan grass and silage corn in arid desert area of Northern Xinjiang, and the relationship between the water consumption and the yield could be reflected relatively correctly; the average relative errors of the models for alfalfa, Sudan grass and silage corn were 6.51%, 9.24% and 9.25%, respectively. The sensitive indexes of alfalfa in different growth stages under Jensen model were 0.037 1, 0.563 0, 0.404 4, 0.160 5, 0.512 7 and 0.363 9, respectively; the sensitive coefficients of Sudan grass in different growth stages under Stewart model were 1.252 7, 1.510 2, 1.746 7 and 1.828 2, respectively; the sensitive indexes of silage corn in different growth stages under Jensen model were 0.035 2, 0.633 2, 0.081 2, 0.904 4 and 0.512 7, respectively. The most sensitive growth stages of the three crops were branching-bud stage (1st harvest), filling-milk stage and booting-flowering stage, respectively.

The crop sensitive indexes change not only along with crop types, growth period, agriculture and its technical measures, but also related to climate zones and different hydrological years. The values ofλandKin this study were based on the experimental results of Altay Prefecture in Xinjiang from 2012 to 2014, showing the general law of water deficit in different growth stages on the yield. For application in the production as an annual average value, further studies should be conducted to find out the change law ofλ(orK) along with hydrological years. Besides,λ(orK) is also related to soil water potential, potential evapotranspiration, rainfall and temperature. Therefore, we can further analyze the interrelation ofλorKwith other factors, discuss the internal mechanism ofλorKchange and then better apply it in the actual production.

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劉虎1*， 尹春艷2,3， 魏永富1， 張瑞強(qiáng)1， 高天明1(1.水利部牧區(qū)水利科學(xué)研究所，呼和浩特 010020;2.中國(guó)科學(xué)院煙臺(tái)海岸帶研究所，山東 煙臺(tái) 264003；3.烏審旗水土保持局，內(nèi)蒙古 烏審旗 017300)

摘要在北疆地區(qū)開(kāi)展非充分灌溉試驗(yàn)，分析北疆干旱荒漠地區(qū)主要人工草地在不同土壤水分條件下的作物產(chǎn)量，確定了紫花苜蓿、蘇丹草和青貯玉米的水分生產(chǎn)函數(shù)模型，并分別采用Jensen模型、Stewart模型和Jensen模型得出了各生育階段的敏感指數(shù)和敏感系數(shù)。檢驗(yàn)結(jié)果表明，所確定的模型有較高的精度，平均相對(duì)誤差為6.51%、9.24%和9.25%。該研究結(jié)果可為合理開(kāi)發(fā)阿勒泰草原乃至新疆牧區(qū)有限的水土資源提供理論和技術(shù)支撐。

關(guān)鍵詞北疆； 人工草地； 作物水分生產(chǎn)模型； Jensen模型； Stewart模型

*Corresponding author：LIU Hu (http://orcid.org/0000-0001-9116-2512), Tel:+86-471-4690554, E-mail:liuhuycy@163.com

Foundation item： Supported by Xinjiang Science and Technology Supporting Project “Research on Integration and Modeling of High-quality Forage Comprehensive Water-saving Techniques in Altay Prefecture” (No. 201531115) and China Institute of Water Resources and Hydropower Research Special President Funding Project “Research on Integration Model of Water-Saving and High-Yield Techniques for Irrigated Forage Grass Fields in North Xinjiang” (No. MK2014J03).

Received： 2015-09-07； Accepted： 2015-11-27； Published online： 2016-03-20

URL:http://www.cnki.net/kcms/detail/33.1247.S.20160321.1424.010.html