DOI: 10.5061/dryad.b8gtht7jv
title: Rice yield and nutrient balance in southwest China

description: The optimal application of nutrients, such as nitrogen and phosphorus, to
 the soil is crucial for achieving high crop yields with minimal
 environmental impact. However, the effect of spatio-temporal changes in
 soil nutrient supply on crop yield is poorly understood in China. Here, we
 present a framework that combines environmental data, fertilizer field
 experiments, and machine learning to estimate the rice yield responses to
 different nutrient conditions and overall farmland nutrient sustainability
 in southwest China from 2009 to 2019. This dataset contains data on the
 spatial distribution of rice yield forecasts and farmland nutrient
 balances in Southwest China in 2009 and 2019.
description: Crop soil-based yield (YS) and fertilized yield (YF) can
 effectively reflect the contribution of farmland inherent soil
 productivity and fertilization for rice yield, respectively. Here, using
 1027 field experiments (plain: 213, hill: 611, mountain: 203) as training
 samples, we established GWRK models to simulate the effects of soil and
 fertilizer nutrient supply on YS and YF of rice. YS refers to rice yield
 without fertilization in the current year, expressed as the yield of
 treatment 1 (N0P0K0) of the "3414" experiment. The YS is
 important for two reasons: firstly, it shows the spatial distribution of
 rice yield reduction under the hypothetical condition of no fertilizer
 use; and secondly, the fertilizer increased yield (YF-YS) provides the
 possibility to calculate the theoretical fertilizer requirement of rice in
 order to assess the nutrient balance. The independent variables
 (explanatory variables) corresponding to the YS model include 9
 environmental factors: soil OM (X1), pH (X2), soil AP (X3), soil AK (X4),
 PRE (X5), TEM (X6), NDVI (X7), altitude (X8), and slope (X9). <br>YF
 refers to rice yield under normal fertilization conditions (representative
 fertilization rate and cultivation management of local farmers). Since YF
 significantly depends on YS, we adopted the relative yield coefficient
 (RYC) as the dependent variable fitting model to improve the prediction
 accuracy, which is expressed as the ratio of the yield of treatment 1 and
 treatment 6 (N0P0K0/N2P2K2) in the "3414" experiment. Based on
 the YS model, the RYC model adds 3 fertilization quantity factors: FRN
 (X10), FRP (X11), and FRK (X12), increasing the number of independent
 variables to 12. Finally, YF is calculated on the basis of RYC and YS.
 Fertilizer increased yield (FIY) is used to express the increase in rice
 yield due to fertilizer application by subtracting the soil base yield
 from the fertilizer applied yield; fertilizer contribution ratio (FCR) was
 used to measure the contribution of fertilizer to rice yield. Finally, the nutrient balance rates were input
 into the SOFM model to assess the farmland nutrient sustainability in the
 Sichuan Basin.
description: # Rice yield and nutrient balance in southwest China
 [https://doi.org/10.5061/dryad.b8gtht7jv](https://doi.org/10.5061/dryad.b8gtht7jv) The optimal application of nutrients, such as nitrogen and phosphorus, to the soil is crucial for achieving high crop yields with minimal environmental impact. However, the effect of spatio-temporal changes in soil nutrient supply on crop yield is poorly understood in China. Here, we present a framework that combines environmental data, fertilizer field experiments, and machine learning to estimate the rice yield responses to different nutrient conditions and overall farmland nutrient sustainability in southwest China from 2009 to 2019. This dataset contains data on the spatial distribution of rice yield forecasts and farmland nutrient balances in Southwest China in 2009 and 2019. ## Description of data and file structure The spatial resolution of the raster data is 30m. The data format is gdb/tiff and the file names mean the following: * FNSZ: farmland nutrient sustainability zones (1: Degradation Risk Zone; 2: Sustainable Zone; 3: Excess Risk Zone). * Spatial distribution of Soil organic matter (g/kg), Soil pH, Soil available phosphorus (mg/kg), Soil available potassium (mg/kg), Nitrogen fertilizer rate (kg/ha), Phosphorus fertilizer rate (kg/ha), Potassium fertilizer rate (kg/ha) in 2009 and 2019: MO2009, MO2019, pH2009, pH2019, AP2009, AP2019, AK2009, AK2019, FRN2009, FRN2019, FRP2009, FRP2019, FRK2009, FRK2019. * Spatial distribution of regression coefficients of soil-based yield GWRK model: YS\_GWRK\_C\_Intercept, YS\_GWRK\_C\_Soil OM, YS\_GWRK\_C\_Soil AP, YS\_GWRK\_C\_Soil AK, YS\_GWRK\_C\_Soil pH, YS\_GWRK\_C\_Altitude, YS\_GWRK\_C\_Slope, YS\_GWRK\_C\_PRE, YS\_GWRK\_C\_TEM, YS\_GWRK\_C\_NDVI, YS\_GWRK\_C\_Residual. * Spatial distribution of regression coefficients of relative yield GWRK model: YF\_GWRK\_C\_Intercept, YF\_GWRK\_C\_Soil OM, YF\_GWRK\_C\_Soil AP, YF\_GWRK\_C\_Soil AK, YF\_GWRK\_C\_Soil pH, YF\_GWRK\_C\_Altitude, YF\_GWRK\_C\_Slope, YF\_GWRK\_C\_PRE, YF\_GWRK\_C\_TEM, YF\_GWRK\_C\_NDVI, YF\_GWRK\_C\_Residual. * Spatial distributions of the soil-based yield and the fertilized yield (kg/ha): YS2009, YS2019, YF2009, YF2019. * Spatial distributions of rice Nitrogen, Phosphorus, and Potassium fertilizer utilization efficiency: REN, REP, REK * Spatial distributions of rice Nitrogen, Phosphorus, and Potassium theoretical fertilizer rate (kg/ha): TFRN2009, TFRN2019, TFRP2009, TFRP2019, TFRK2009, TFRK2019. ###### Contact G.T. Liao, (liaoguitang1982@163.com) with questions or comments regarding access and usage. Version updates will be made as needed with such updates noted in the metadata.