Spectral indices measured with proximal sensing using canopy reflectance sensor, chlorophyll meter and leaf color chart for in-season grain yield prediction of basmati rice

A number of optical sensing tools are now available and can potentially be used for refining need-based fertilizer nitrogen (N) topdressing decisions. Algorithms for estimating field-specific fertilizer N needs are based on predictions of yield made while the crops are still growing in the field. The present study was conducted to establish and validate yield prediction models using spectral indices measured with proximal sensing using GreenSeeker canopy reflectance sensor, soil and plant analyzer development (SPAD) chlorophyll meter, and two different types of leaf color charts (LCCs) for five basmati rice genotypes across different growth stages. Regression analysis was performed using normalized difference vegetation index (NDVI) recorded with GreenSeeker sensor and leaf greenness indices measured with SPAD meter and LCCs developed by Punjab Agricultural University, Ludhiana (India) (PAU-LCC) and the International Rice Research Institute, Philippines (IRRI-LCC). The exponential relationship between NDVI and grain yield exhibited the highest coefficient of determination (R²) and minimum normalized root mean square error (NRMSE) at panicle initiation stage and explained 38.3%-76.4% variation in yield using genotype-specific models. Spectral indices pooled for different genotypes were closely related to grain yield at all growth stages and explained 53.4%-57.2% variation in grain yield. Normalizing different spectral indices with cumulative growing degree days (CGDD) decreased the accuracy of yield prediction. Normalization with days after transplanting (DAT), however, did not reduce or improve the predictability of yield. The ability of each model to predict grain yield was validated with an independent dataset collected from two experiments conducted at different sites with a range of fertilizer N doses. The NDVI-based genotype-specific models exhibited a robust linear correlation (R²=0.77, NRMSE=7.37%, n=180) between observed and predicted grain yields only at 35 DAT (i.e., panicle initiation stage), while the SPAD, PAU-LCC, and IRRI-LCC consistently provided reliable predictions (with respective R² of 0.63, 0.60, and 0.53 and NRMSE of 10%, 10%, and 13.6%) even with genotype invariant models with 900 data points obtained at different growth stages. The study revealed that unnormalized values of spectral indices, namely NDVI, SPAD, PAU-LCC, and IRRI-LCC, can be satisfactorily used for in-season estimation of grain yield for basmati rice. As LCCs are very economical compared with chlorophyll meters and canopy reflectance sensors, they can be preferably used by small farmers in developing countries.     

苏打盐碱水灌溉的土壤添加石膏肥和有机肥后水溶性碳和微生物碳研究

Microbial biomass carbon (MBC), a small fraction of soil organic matter, has a rapid turnover rate and is a reservoir of labile nutrients. The water-extractable carbon pools provide a fairly good estimate of labile C present in soil and can be easily quantified. Changes in soil MBC and water-extractable organic carbon pools were studied in a 14-year long-term experiment in plots of rice-wheat rotation irrigated with canal water (CW), sodic water (SW, 10-12.5 mmol c L-1 residual sodium carbonate), and SW amended with gypsum with or without application of organic amendments including farmyard manure (FYM), green manure (GM), and wheat straw (WS). Irrigation with SW increased soil exchangeable sodium percentage by more than 13 times compared to irrigation with CW. Sodic water irrigation significantly decreased hot water-extractable organic carbon (HWOC) from 330 to 286 mg kg-1 soil and cold water-extractable organic carbon (CWOC) from 53 to 22 mg kg-1 soil in the top 0-7.5 cm soil layer. In the lower soil layer (7.5-15 cm), reduction in HWOC was not significant. Application of gypsum alone resulted in a decrease in HWOC in the SW plots, whereas an increase was recorded in the SW plots with application of both gypsum and organic amendments in both the soil layers. Nevertheless, application of gypsum and organic amendments increased the mean CWOC as compared with application of gypsum alone. CWOC was significantly correlated with MBC but did not truly reflect the changes in MBC in the treatments with gypsum and organic amendments applied. For the treatments without organic amendments, HWOC was negatively correlated with MBC (r = 0.57*) in the 0-7.5 cm soil layer, whereas for the treatments with organic amendments, both were positively correlated. Irrigation with SW significantly reduced the rice yield by 3 t ha-1 and the yield of rice and wheat by 5 t ha-1 as compared to irrigation with canal water. Application of amendments significantly increased rice and wheat yields. Both the rice yield and the yield of rice and wheat were significantly correlated with MBC (r = 0.49**-0.56**, n = 60). HWOC did not exhibit any relation with the crop yields under the treatments without organic amendments; however, CWOC showed a positive but weak correlation with the crop yields. Therefore, we found that under sodic water irrigation, HWOC or CWOC in the soils was not related to MBC.     

Long-term fertilizer nitrogen management—Soil health conundrum

<正>The mental model that fertilizer nitrogen (N) acts as a replacement for N mineralized from soil organic matter (SOM) needs to be revisited. Soil organic matter, the storehouse of N in soil, is one of the most important indicators of soil health. It supplies more N to crop plants than the current-year fertilizer N even when applied at high rates. Limited research shows that the application of fertilizer N above the optimum levels on a long-term basis deteriorates soil health by mineraliz...