Use of information and communication technologies, as well as robotics, routinely saves labor and refines agricultural tasks; thus, innovative “smart farming” to maintain and enhance the quality of crops can improve the sustainability of agriculture. When managing crop growth using remote‑sensing drones, the normalized difference vegetation index (NDVI)—used to assess growth—typically changes depending on sunlight conditions. In this study we have attempted to develop an empirical correction to correct for differences in sunlight conditions in drone NDVI images of paddy rice. Based on observations using a field sensor installed in a paddy field, and considering the effects of morning dew, we determined that 10:00 AM is the most appropriate time for NDVI observations in paddy rice, when the morning dew has largely evaporated. This observation time differs from that used in the radiative transmission models described in previous studies. In the drone observations, sections with lower NDVI were more strongly affected by solar altitude, and thus by time of day. Therefore, we found that when correcting NDVI according to sunlight conditions, it is necessary to adjust the correction parameters depending on the NDVI values. Based on the aforementioned results, we corrected the drone‑observed NDVI and succeeded in mitigating the decline in NDVI value associated with changes in sunlight conditions, in terms of both NDVI values and NDVI images, within plots established in the experimental field.
Root respiration (Rr) plays a crucial role in the global carbon balance, because Rr accounts for about a half of soil respiration in typical forest ecosystems. Plant roots are different in metabolism and functions according to size. Fine roots, which are typically defined as roots < 2 mm in diameter, perform important ecosystem functions and consequently govern belowground carbon cycles mainly because of their high turnover rates. However, the phenological variation of fine root functions is not well understood yet. To quantitatively examine the fine root functions, we adopted an approach to partition Rr into growth respiration (Rg) and maintenance respiration (Rm) using a modified traditional model, in which Rg was proportional to root production, and Rm was proportional to root biomass and exponentially related to soil temperature. We conducted a field experiment on soil respiration and fine root biomass and production over a year in a larch‑dominated young forest developing on the bare ground after removing surface organic soil to parameterize the model. The model was significantly parameterized using the field data measured in such simplified field conditions, because we could control spatial variation in heterotrophic respiration and contamination from roots other than fine roots. The annual Rr of all roots was 94 g C m‑2 yr‑1 and accounted for 25% of total soil respiration on average. The annual Rr was partitioned into fine root Rg, fine root Rm and coarse root Rm by 30, 44 and 26%, respectively; coarse root Rg was presumed to be negligible. Fine root Rg and Rm varied according to the seasonal variations of fine root production and soil temperature, respectively; the contribution of fine root biomass was minor because of its small seasonality. The contribution of Rg to total fine root respiration was lower in the cold season with low production.
Crop yield is mainly affected by climatic factors such as temperature and precipitation. Besides these factors, improved seeds, irrigation access, and fertilizers also affect yield. In the present study, we collected crop yield data for major crops such as maize, rice, and wheat from the Koshi River Basin, Nepal. We investigated the yield trends over 30 years (1987‑2016) and related the yields with climatic factors (temperature and precipitation). We also investigated the trends in the use of improved seeds, irrigation access, and fertilizer use in our study area. Results showed that there was an increase in yield of maize, rice, and wheat over 30‑year period. Maize yield slightly increased with increasing average temperature. Rice yields significantly decreased with increasing temperature and precipitation, whereas wheat yield increased with increasing the diurnal temperature range. The present study suggests that future yields of maize, rice, and wheat will be affected by the increasing temperature than precipitation in the Koshi River Basin, Nepal.
Spring phenology is essential in modeling the carbon balance of high‑latitude ecosystems and is possibly sensitive to climate change. In the present study, we evaluated the onset of the growing season for three species (paper birch, bog blueberry, and bog Labrador tea) in interior Alaska from 2012 to 2019 using photos taken using time‑lapse cameras. We also evaluated the onset of the growing season at the ecosystem scale from 2010 to 2019 on the basis of the CO2 flux by the eddy covariance method at the site. On the basis of the growing degree‑day (GDD) model with the parameters estimated using the Bayesian approach, we found that the interannual variations in the spring onsets were explained by the model, and the thermal forcing requirement differed among the species. At the ecosystem scale, the spring onset was closely linked to the snow disappearance date. Under the possible future climate scenarios indicated by the representative concentration pathway 8.5 scenario, the spring onsets were predicted to be one to three weeks earlier than the present dates for the three species. The ecosystem‑scale onsets were also predicted to be five days to a little over a month earlier at the end of this century. The future spring onset is highly sensitive to the snow disappearance date for high‑latitude vegetation; thus, further understanding of climate change before snowmelting is required.