Spatial yield variability and site-specific nitrogen prescription for the improved yield and grain quality of rice
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| Spatial yield variability and site-specific nitrogen prescription for the improved yield and grain quality of rice | |
| Seoul National University, Korea, Nông nghiệp, , 2005 ; | |
| Tác giả | Nguyễn Tuấn Anh |
| Người hướng dẫn | Prof. Byun Woo Lee |
| Từ khóa | Tiến sỹ |
| DOI luận án quốc tế DOI [ URL] [ PDF] | |
Mục lục
- 1 Analysis of within-field spatial variaton of rice yield and growth in relation to soil properties
- 2 Response of rice yield to different rate of nitrogen application under variable soil condition
- 3 Management of within-field spatial yield variability based on site-specific prescription of nitrogen topdressing at panicle initiation stage
- 4 General conclusions
Analysis of within-field spatial variaton of rice yield and growth in relation to soil properties[sửa]
For developing the site-specific fertilizer management strategies of crop, it is essential to know the spatial variability of soil factors and to assess their influence on the variability of crop growth and yield. Within-field spatial variability of rice yield and plant growth was examined in relation to spatial variation of soil properties in two trial paddy fields that have soil characteristics of most commonly found in Korea and each area of ca. 6,600 m2 from 2002 to 2003. The fields were managed without fertilizer or with uniform application of N, P, and K fertilizer under direct-seeded and transplanted rice. Stable soil properties such as content of clay (Clay), total nitrogen (TN), organic mater (OM), and silica (Si) and cation exchange capacity (CEC), plant growth, and grain yield of rice were measured for each grid of 10 x 10m. The two fields showed quite similar spatial variation in soil properties, showing the smallest coefficient of variation (CV) in Clay (7.6%) and the largest in Si (21.4%). The spatial variation of plant growth parameters measured at panicle initiation (PIS) and heading stage (HD) ranged from 6 to 38%, and that of rice yield ranged from 11 to 21%. Spatial variation of rice yield was larger in no fertilizer treatment and direct seeding culture compared to fertilizer-applied treatment and transplanted culture, respectively. Spatial variability of rice yield and its distribution patterns across field were similar between the two experimental years. CEC, OM, TN, and available Si showed significant correlations with variation of plant growth and rice yield. Multiple linear regression model with stepwise procedure selected independent variables of N fertilizer level, climate condition and soil properties, explaining as much as 76% of yield variability, of which 21.6% is ascribed to soil properties. Among the soil properties, the most important soil factors causing yield spatial variability was OM, followed by Si, TN, and CEC. Boundary line response of rice yield to soil properties was represented well by Mitcherich equation (negative exponential equation) that was used to quantify the influence of soil properties to rice yield, and then the Law of the Minimum was used to identify the soil limiting factor for 21 each grid. This boundary line approach using five stable soil properties as limiting factor explained an average of about 50% of the spatial yield variability. Although the determination coefficient was not very high, an advantage of the method was that it identified clearly which soil parameter was yield limiting factor and where it was distributed in the field. It is suggested that this approach is highly recommendable for identifying and quantifying the limiting factors for rice growth, and addressing the spatial variability of rice yield.
Response of rice yield to different rate of nitrogen application under variable soil condition[sửa]
Rice yield and plant growth response to nitrogen (N) fertilizer may vary within a field, probably due to spatially variable soil conditions. An experiment designed for studying the response of rice yield to different rates of N in combination with variable soil conditions was carried out at a field where spatial variation in soil properties, plant growth, and yield across the field was documented from our previous studies for two years. The field with area of 6,600 m2 was divided into six strips running east-west so that variable soil conditions could be included in each strip. Each trip was subjected to different N application level (six levels from 0 to 165kg/ha), and schematically divided into 12 grids (10m x 10m for each grid) for sampling and measurement of plant growth and rice grain yield. Most of plant growth parameters and rice yield showed high variations even at the same N fertilizer level due to the spatially variable soil condition. However, the maximum plant growth and yield response to N fertilizer rate that was analyzed using boundary line analysis followed the Mitcherlich equation (negative exponential function), approaching a maximum value with increasing N fertilizer rate. Assuming the obtainable maximum rice yield is constrained by a limiting soil property, the following model to predict rice grain yield was obtained: Y= 10765{1-0.4704*EXP (-0.0117*FN)}*MIN (Iclay, Iom, Icec, ITN, ISi) where FN is N fertilizer rate (kg/ha), I is index for subscripted soil properties (Nguyen et al., 2004), and MIN ( ) is an operator for selecting the minimum value. The observed and predicted yield was well fitted to 1:1 line (Y=X) with determination coefficient of 0.564. As this result was obtained in a very limited condition and did not explained the yield variability so high, this result may not be applied to practical N management. However, this approach has proved to have potential for quantifying the yield response of grain yield to N fertilizer rate under variable soil conditions and formulating the site64 specific N prescription for the management of spatial yield variability in a field if sufficient data set is acquired for boundary line analysis
Management of within-field spatial yield variability based on site-specific prescription of nitrogen topdressing at panicle initiation stage[sửa]
Rice yield and protein content have been shown to be highly variable across paddy fields. In order to characterize this spatial variability of rice within a field, the twoyear experiments were conducted previously in 2002 and 2003 in the two rice fields. In year 2004, an experiment was conducted in one of the two fields with area of 6600 m2 to know if prescribed N for site-specific fertilizer management at PIS (VRT) could reduce spatial variation in yield and protein content while increasing yield compared to conventional uniform N application (UN, 33 kg N/ha at PIS) method. The trial field was divided into two parts and each part was subjected to UN and VRT treatment. Each part was schematically divided in 10 x 10m grids for growth and yield measurement or VRT treatment. VRT nitrogen prescription for each grid was calculated based on the nitrogen (N) uptake (from panicle initiation to harvest) required for target rice protein content of 6.6%, natural soil N supply, and recovery of top-dressed N fertilizer. The required N uptake for target rice protein content was calculated from the equations that were obtained from the previous two-year experiment, prediction of rice yield and protein content from plant growth parameters at panicle initiation stage (PIS), and N uptake from PIS to harvest. The plant growth parameters for this calculation were predicted nondestructively by canopy reflectance measurement. Soil N supply for each grid was obtained from the experiment of year 2003, and N recovery was assumed to be 60% according to the previous reports. The prescribed VRT N ranged from 0 to 110 kg N/ha with average of 57 kg/ha that was higher than 33 kg/ha of UN. The results showed that VRT application successfully worked not only to reduce spatial variability of rice yield and protein content but also to increase rough rice yield by 960 kg/ha. The coefficient of variation (CV) for rice yield was reduced significantly reduced to 8.13% for VRT from 14.6% for UN and also similar trend was observed for protein content of milled rice. Although N use efficiency of VRT compared to UN was not quantified due to lack of no N control 88 treatment, the procedure used in this paper for VRT estimation was believed to be reliable and promising method for managing within-field spatial variability of yield and protein content. The method should be received further study before it could be practically used for site-specific crop management in large-scale rice field.
General conclusions[sửa]
A series of related and continued field research were carried out in Korea through 2002-2004. The main objectives of these studies were (1) to analyze within-field spatial variability of plant growth, rice yield, and soil properties and the relationship among them, (2) to understand the rice crop response to the rate of nitrogen fertilizer under variable soil conditions for formulating the site-specific prescription rule of nitrogen fertilizer amount, and (3) to assess the possibility for managing the spatial variability of rice yield and grain quality by site-specific prescription of nitrogen topdressing using the information on spatial variability of plant growth and nitrogen nutrition status measured non-destructively at panicle initiation stage and the spatial variability of soil fertility. The results obtained from these studies can be summarized and concluded as follow: Chapter 3 presented the results addressing from objective (1). Spatial variation in plant growth and yield was significantly dependent on soil variability regardless of fertilizer application, rice culture and years of cultivation. The spatial variation in crop yield was larger under direct-seeded and no nitrogen fertilization compared to transplanted culture with N application. Multiple linear regression model based on plant growth parameters at PIS and heading stage explained an average of about 66% of spatial yield variability of rice crop, while model based on soil properties explained an average of about 55% of spatial yield variability. Overall analysis of fertilizer application, climate condition and soil properties were carried out using multiple regression model with stepwise procedure accounted up to 76% of spatial yield variability, and OM among stable soil properties was the most important soil property causing the spatial yield variability, followed by available Si, TN, and CEC. Boundary line analysis and an application of the Law of the minimum were well applied for an analysis of spatial variation of rice yield response to soil properties. This procedure explained more than 50% of yield spatial variability. Soil CEC, available Si, clay content, organic matter, and total nitrogen were identified as yield limiting factors in the order of their selected frequencies. This approach using boundary line method and the 115 Law of the Minimum is simple and use logical mathematic method that provides a best fit to the analysis of spatial variation of yield response to soil variables. This procedure also has an advantage in solving problem of collinearities frequently encountered in regression analysis so that lose of one or more collinear factors from the model could be prevented. Especially, the procedure could clearly address the common question in precision agriculture: what and where yield-limiting factor is. This procedure was therefore recommended, particularly for the analysis of spatial variation of rice yield response to soil properties as well as identifying yield-determining factor for rice growth. Chapter 4 presented the results addressing objective (2). Rice yield and plant growth response to N fertilizer under variable soil conditions were shown to be highly varied within a field. Applied N fertilizer had high effect on plant growth and yield of rice even within-field spatial variation in soil properties was observed. Variation in applied N rate explained 58% of rice yield variation while the model’s coefficient of determination was increased only to 0.62 by considering the spatial variation of one soil property, and also boundary line approach with soil limiting factor did not improve the model prediction power. However, obtained results suggested that boundary line approach would be applied for site-specific N prescription if sufficient soil database including spatial variation and distribution of soil limiting factors were available. Chapter 5 presented the results addressing objective (3). A possibility for management of spatial yield variability and grain protein contents using prescribed nitrogen topdressing at panicle initiation stage has been examined. The prescription of N fertilizer requirement for management of spatial variation in rice yield and protein contents at PIS using basic spatial information from the previous study and in-situ prediction of variation in plant growth status was formulated successfully. Spatial variation of plant growth at VRT turned out to reduce slightly at HD after the prescription treatment of nitrogen topdressing and, especially, spatial variation of plant growth was significantly reduced at harvest stage. Design of experimental field for testing a possibility of management of spatial yield variability using prescription of nitrogen at PIS has successfully worked not only to reduce spatial yield and protein content variability 116 but also increased rice yield in a paddy field of Korea. Although N use efficiency of VRT compared to UN was not quantified due to lack of no N control treatment, the procedure used in this paper for VRT estimation was believed as a reliable and promising method for managing within-field spatial variability of yield and protein content. However, the method should be received further study before it could be practically used for sitespecific crop management in large-scale rice field.
