Comparative Analysis of Phenology Algorithms of the Spring Barley Model in APSIM 7.9 and APSIM Next Generation: A Case Study for High Latitudes

Research on fertilization decision method for rice tillering stage based on the coupling of UAV hyperspectral remote sensing and WOFOST

Introduction

The use of chemical fertilizers in rice field management directly affects rice yield. Traditional rice cultivation often relies on the experience of farmers to develop fertilization plans, which cannot be adjusted according to the fertilizer requirements of rice. At present, agricultural drones are widely used for early monitoring of rice, but due to their lack of rationality, they cannot directly guide fertilization. How to accurately apply nitrogen fertilizer during the tillering stage to stabilize rice yield is an urgent problem to be solved in the current large-scale rice production process.

Methods

WOFOST is a highly mechanistic crop growth model that can effectively simulate the effects of fertilization on rice growth and development. However, due to its lack of spatial heterogeneity, its ability to simulate crop growth at the field level is weak. This study is based on UAV remote sensing to obtain hyperspectral data of rice canopy and assimilation with the WOFOST crop growth model, to study the decision-making method of nitrogen fertilizer application during the rice tillering stage. Extracting hyperspectral features of rice canopy using Continuous Projection Algorithm and constructing a hyperspectral inversion model for rice biomass based on Extreme Learning Machine. By using two data assimilation methods, Ensemble Kalman Filter and Four-Dimensional Variational, the inverted biomass of the rice biomass hyperspectral inversion model and the localized WOFOST crop growth model were assimilated, and the simulation results of the WOFOST model were corrected. With the average yield as the goal, use the WOFOST model to formulate fertilization decisions and create a fertilization prescription map to achieve precise fertilization during the tillering stage of rice.

Results

The research results indicate that the training set R2 and RMSE of the rice biomass hyperspectral inversion model are 0.953 and 0.076, respectively, while the testing set R2 and RMSE are 0.914 and 0.110, respectively. When obtaining the same yield, the fertilization strategy based on the ENKF assimilation method applied less fertilizer, reducing 5.9% compared to the standard fertilization scheme.

Discussion

This study enhances the rationality of unmanned aerial vehicle remote sensing machines through data assimilation, providing a new theoretical basis for the decision-making of rice fertilization.

Performance of APSIM to Simulate the Dynamics of Winter Wheat Growth, Phenology, and Nitrogen Uptake from Early Growth Stages to Maturity in Northern Europe

Performance of the APSIM (Agricultural Production Systems sIMulator) wheat model was assessed to simulate winter wheat phenology, biomass, grain yield, and nitrogen (N) uptake for its potential to optimize fertilizer applications for optimal crop growth and minimal environmental degradation. The calibration and evaluation dataset had 144 and 72 different field growing conditions (location (~7) × year (~5) × sowing date (2) × N treatment (7-13)), respectively, and included seven cultivars. APSIM simulated phenological stages satisfactorily with both model calibration and evaluation data sets with r² of 0.97 and RMSE of 3.98-4.15 BBCH (BASF, Bayer, Ciba-Geigy, and Hoechst) scale. Simulations for biomass accumulation and N uptake during early growth stages (BBCH 28-49) were also reasonable with r² of 0.65 and RMSE of 1510 kg ha^-1, and r² of 0.64-0.66 and RMSE of 28-39 kg N ha^-1, respectively, with a higher accuracy during booting (BBCH 45-47). Overestimation of N uptake during stem elongation (BBCH 32-39) was attributed to (1) high inter-annual variability in simulations, and (2) high sensitivity of parameters regulating N uptake from soil. Calibration accuracy of grain yield and grain N was higher than that of biomass and N uptake at the early growth stages. APSIM wheat model showed high potential for optimizing fertilizer management in winter wheat cultivation in Northern Europe.

Impact of Climate Change on Dryland Agricultural Systems: A Review of Current Status, Potentials, and Further Work Need

Dryland agricultural system is under threat due to climate extremes and unsustainable management. Understanding of climate change impact is important to design adaptation options for dry land agricultural systems. Thus, the present review was conducted with the objectives to identify gaps and suggest technology-based intervention that can support dry land farming under changing climate. Careful management of the available agricultural resources in the region is a current need, as it will play crucial role in the coming decades to ensure food security, reduce poverty, hunger, and malnutrition. Technology based regional collaborative interventions among Universities, Institutions, Growers, Companies etc. for water conservation, supplemental irrigation, foliar sprays, integrated nutrient management, resilient crops-based cropping systems, artificial intelligence, and precision agriculture (modeling and remote sensing) are needed to support agriculture of the region. Different process-based models have been used in different regions around the world to quantify the impacts of climate change at field, regional, and national scales to design management options for dryland cropping systems. Modeling include water and nutrient management, ideotype designing, modification in tillage practices, application of cover crops, insect, and disease management. However, diversification in the mixed and integrated crop and livestock farming system is needed to have profitable, sustainable business. The main focus in this work is to recommend different agro-adaptation measures to be part of policies for sustainable agricultural production systems in future.

Quantification of the Impact of Temperature, CO2, and Rainfall Changes on Swedish Annual Crops Production Using the APSIM Model

Ongoing climate change is already affecting crop production patterns worldwide. Our aim was to investigate how increasing temperature and CO2 as well as changes in precipitation could affect potential yields for different historical pedoclimatic conditions at high latitudes (i.e., >55°). The APSIM crop model was used to simulate the productivity of four annual crops (barley, forage maize, oats, and spring wheat) over five sites in Sweden ranging between 55 and 64°N. A first set of simulations was run using site-specific daily weather data acquired between 1980 and 2005. A second set of simulations was then run using incremental changes in precipitation, temperature and CO2 levels, corresponding to a range of potential future climate scenarios. All simulation sets were compared in terms of production and risk of failure. Projected future trends showed that barley and oats will reach a maximum increase in yield with a 1°C increase in temperature compared to the 1980–2005 baseline. The optimum temperature for spring wheat was similar, except at the northernmost site (63.8°N), where the highest yield was obtained with a 4°C increase in temperature. Forage maize showed best performances for temperature increases of 2–3°C in all locations, except for the northernmost site, where the highest simulated yield was reached with a 5°C increase. Changes in temperatures and CO2 were the main factors explaining the changes in productivity, with ~89% of variance explained, whereas changes in precipitation explained ~11%. At the northernmost site, forage maize, oats and spring wheat showed decreasing risk of crop failure with increasing temperatures. The results of this modeling exercise suggest that the cultivation of annual crops in Sweden should, to some degree, benefit from the expected increase of temperature in the coming decades, provided that little to no water stress affects their growth and development. These results might be relevant to agriculture studies in regions of similar latitudes, especially the Nordic countries, and support the general assumption that climate change should have a positive impact on crop production at high latitudes.