Hidetoshi Asai

Enhancing rice yield in upland rice systems through genetic improvement remains a major challenge in the tropics. This review aims to provide the trends on upland rice cultivation over the last 30 years and recent distribution of upland rice in the tropics, and to report progress in studies on genetic improvement for enhancing productivity in Africa, Asia, and Latin America. While upland rice cultivation area has reduced in Asia and Latin America over the last 30 years, the area in Africa has increased. The current share of upland rice area in total rice area is related to rainfall and gross national income per capita, especially in Africa, and higher share is associated with lower rice self-sufficiency at national level. Breeding programs in Asia and Latin America have developed high-yielding varieties using indica materials as parents. In Africa, New Rice for Africa (NERICA) varieties were developed from crosses between improved tropical japonica and Oryza glaberrima. However, recent studies report that there is scope for improving existing NERICA using upland indica materials from Asia. In highlands of Africa, there are ongoing breeding programs using japonica varieties, such as the Nepalese Chhomrong Dhan. Key important plant traits used in the breeding programs are not largely different across regions, especially intermediate plant height and tillering capacity (which may be related to weed-suppressive ability), and high harvest index. In conclusion, we propose an international network for breeding upland rice with accelerating seed exchange across regions that could enhance upland rice productivity through genetic improvement.

Visible and near-infrared (Vis-NIR) diffuse reflectance spectroscopy with partial least squares (PLS) regression is a quick, cost-effective, and promising technology for predicting soil properties. The advantage of PLS regression is that all available wavebands can be incorporated in the model, while earlier studies indicate that PLS models include redundant wavelengths, and selecting specific wavebands can refine PLS analyses. This study evaluated the performance of PLS regression with waveband selection using Vis-NIR reflectance spectra to estimate the total carbon (TC) and total nitrogen (TN) in soils collected mainly from the surface of upland and lowland rice fields in Madagascar (n = 59; after outliers were removed). We used iterative stepwise elimination-based PLS (ISE-PLS) to estimate soil TC and TN and compared the predictive ability with standard full-spectrum PLS (FS-PLS). The predictive abilities were assessed using the coefficient of determination (R2), the root mean squared error of cross-validation (RMSECV), and the residual predictive deviation (RPD). Overall, ISE-PLS using first derivative reflectance (FDR) showed a better predictive accuracy than ISE-PLS for both TC (R2 = 0.972, RMSECV = 0.194, RPD = 5.995) and TN (R2 = 0.949, RMSECV = 0.019, RPD = 4.416) in the soil of Madagascar. The important wavebands for estimating TC (12.59% of all wavebands) and TN (3.55% of all wavebands) were selected from all 2001 wavebands over the 400–2400 nm range using ISE-PLS. These findings suggest that ISE-PLS based on Vis-NIR diffuse reflectance spectra can be used to estimate soil TC and TN contents in Madagascar with an improved predictive accuracy.

Plant height (PH) is an important agronomical parameter to assess the growth status in upland rice fields. Recently, field-based phenotyping using unmanned aerial vehicles (UAVs) has received increasing attention as a cost-effective, well-suited sensing technology to measure PH. In this study, we evaluated feasibility of a low-cost small UAV for estimating PH in upland rice fields in Laos with a canopy height model (CHM). Images of the upland field, including 501 plots (= 167 accessions × 3 replicates), were captured by a commercial small UAV (DJI Phantom 4) before emergence and in the near-flowering stage to generate digital surface models (DSMs). The CHM was developed from the difference of the DSMs using UAV images obtained before emergence and before flowering. The CHM metrics of each plot were then calculated using 90–99th percentiles and the top 1–10% largest pixel values of CHM and were compared with the manually measured field PH (78.25–189.75 cm). The predictive accuracy was assessed in the 90–99th percentiles and top 1–10% values of CHM metrics with 5-fold cross-validation procedures. Simple linear regression analyses between the field PH and CHM metrics showed that the top 3% CHM metrics had the best correlation with the field PH (R2 = 0.712, root-mean-square error (RMSE) = 9.142 cm, p < 0.001). Cross-validation procedures also confirmed that the top 3% CHM metrics were the best in terms of accuracy for estimating PH, with an error of 6.963% (8.823 cm) error.