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Sustainability of Maize–Soybean Rotation for Future Climate Change Scenarios in Northeast China

March 2025
Journal of Agronomy and Crop Science 211(2)

Authors:

Beijing agricultural technology extension station

Climate change poses a global challenge to agricultural production and food security, especially in developing countries. In Northeast China, a major grain-producing region, the Maize–Soybean rotation is crucial for sustainable agricultural development. However, previous studies have mainly focused on single crops and lacked attention to soil health and regional scale analysis. This study utilises the APSIM model to predict crop yields and soil organic carbon (SOC) under two Representative Concentration Pathways 4.5 and 8.5 (RCP4.5 and RCP8.5) future climate scenarios in different latitude regions of Northeast China. The result shows that climate change has significant spatial and temporal variations on crop yield and soil organic carbon storage in the Maize–Soybean rotation system. Compared to the baseline (1980–2010), maize yields change from −11.6 to 42.8 kg 10a−1 (RCP4.5) and 7.1 to 39.8 kg 10a−1 (RCP8.5), and soybean yields vary from −13.1 to 3.9 kg 10a−1 (RCP4.5) and −16.2 to −5.6 kg 10a−1 (RCP8.5). SOC increases slowly from 0 to 20 cm and decreases from 20 to 40 cm, resulting in a decrease of 21–334 kg ha−1 10a−1 (RCP4.5) and 26–280 kg ha−1 10a−1 (RCP8.5) in predicted future soil organic carbon storage. PLS-PM results show that future precipitation change has a negative impact on SOC accumulation, and temperature rise in the RCP8.5 scenario has a negative impact on SOC storage. SOC storage is positively correlated with crop yields, and the correlation is stronger under RCP8.5, which has a higher explanation for crop yields changes. Climate change significantly affects crop yields and SOC stocks in the Maize–Soybean rotation system of Northeastern China, especially during extreme weather. Therefore, adaptation strategies should fit local needs, early-maturing regions opt for drought-resistant, early varieties and employ conservation tillage and water-saving methods, while medium and late-maturing areas select late varieties, adjust sowing and enhance fertiliser efficiency.

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Journal of Agronomy and Crop Science, 2025; 211:e70033

https://doi.org/10.1111/jac.70033

Journal of Agronomy and Crop Science

ORIGINAL ARTICLE

Elevated Carbon Dioxide

Sustainability of Maize–Soybean Rotation for Future

Climate Change Scenarios in Northeast China

1College of Plant Science and Technology, Beijing University of Agriculture, Beijing, China | 2Beijing Agricultural Technology Extension Station,

Beijing,China | 3Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing,China

Correspondence: Runzhi Li (lirunzhi7639@163.com)

Received: 17 September 2024 | Revised: 28 January 2025 | Accepted: 31 January 2025

Funding: This work was supported by the National Key Research and Development Program of China (Grant No. 2022YFD1500702); the Modern

Agro- industry Technology Research System- Green Manure (CARS- 22- G- 16); and the United Nations Development Programme (UNDP) Runtian Project

(00 12183 8).

ABSTRACT

Climate change poses a global challenge to agricultural production and food security, especially in developing countries. In

Northeast China, a major grain- producing region, the Maize–Soybean rotation is crucial for sustainable agricultural develop-

ment. However, previous studies have mainly focused on single crops and lacked attention to soil health and regional scale

analysis. This study utilises the APSIM model to predict crop yields and soil organic carbon (SOC) under two Representative

Concentration Pathways 4.5 and 8.5 (RCP4.5 and RCP8.5) future climate scenarios in different latitude regions of Northeast

China. The result shows that climate change has significant spatial and temporal variations on crop yield and soil organic carbon

storage in the Maize–Soybean rotation system. Compared to the baseline (1980–2010), maize yields change from −11.6 to 42.8 kg

10a−1 (RCP4.5) and 7.1 to 39.8 kg 10a−1 (RCP8.5), and soybean yields vary from −13.1 to 3.9 kg 10a−1 (RCP4.5) and −16.2 to −5.6 kg

10a−1 (RCP8.5). SOC increases slowly from 0 to 20 cm and decreases from 20 to 40 cm, resulting in a decrease of 21–334 kg ha−1

10a−1 (RCP4.5) and 26–280 kg ha−1 10a−1 (RCP8.5) in predicted future soil organic carbon storage. PLS- PM results show that

future precipitation change has a negative impact on SOC accumulation, and temperature rise in the RCP8.5 scenario has a neg-

ative impact on SOC storage. SOC storage is positively correlated with crop yields, and the correlation is stronger under RCP8.5,

which has a higher explanation for crop yields changes. Climate change significantly affects crop yields and SOC stocks in the

Maize–Soybean rotation system of Northeastern China, especially during extreme weather. Therefore, adaptation strategies

should fit local needs, early- maturing regions opt for drought- resistant, early varieties and employ conservation tillage and water-

saving methods, while medium and late- maturing areas select late varieties, adjust sowing and enhance fertiliser efficiency.

1 | Introduction

Climate change represents a global challenge that threatens

agricultural production and food security across the world,

particularly in developing countries where food supply is frag-

ile and insecure (Leng etal.2015; Ray etal.2019; IPCC2021).

Countries in sub- Saharan Africa are particularly suscepti-

ble to the impacts of climate change. A significant portion

of Ethiopia grapples with persistent and periodic food inse-

curity stemming from recurring drought events (Mekonnen

et al. 2021). Rising temperatures, changes in precipitation

patterns, and the increasing frequency of extreme weather

Rui Liu and Hon grun Liu should be c onsidered joint fir st author.

2 of 16 Journal of Agronomy and Crop Science, 2025

events adversely affect crop yields (Piao etal.2010; FAO and

WFP 2018), and lead to intensified soil organic carbon min-

eralisation. For example, research indicated that excessive

rainfall results in an abundance of soil moisture, which can

adversely affect crop growth both above and below the soil

surface, severely disrupting agricultural production in the

United States (Rosenzweig et al. 2002). Corey Lesk discov-

ered that severe drought and extreme heat conditions led to

a substantial decline of 9%–10% in national grain yields (Lesk

etal.2016). The reduction in soil organic carbon decreases soil

fertility and structural stability, thereby threatening the sus-

tainability of agricultural production (Beillouin et al. 2022;

Qiao etal.2022; Jiang etal.2023). Therefore, exploring meth-

ods to enhance crop yields while minimising environmental

impacts in the context of climate change is of profound signif-

icance (Challinor etal.2014; Ray etal.2015).

Northeast China, primarily comprising Heilongjiang, Jilin

and Liaoning provinces, is a crucial grain- producing region in

the country (Guo2015). This region's maize and soybean plan-

tations cover 30.6% and 52.3% of the national area, with yields

comprising 33.4% and 51.8% of the country's grain output

(National Bureau of Statistics of China2023). These crops are

crucial for bolstering the local Agri- economy and fulfilling na-

tional needs for food and oilseeds (FAO2019; Liu etal.2021).

The Maize–Soybean rotation in Northeastern China has a

long history and is an essential practice for maintaining soil

fertility, improving soil health and achieving sustainable ag-

ricultural development (Liu etal.2013; Chen etal.2018; Song

etal.2022). However, under the scenario of climate change,

traditional rotation practices may face challenges, necessi-

tating new management strategies to address these changes

and ensure the effectiveness and sustainability of rotation sys-

tems (Yin etal.2016; Guo etal.2024; Xu etal.2024; Dhillon

etal.2024; Huang and Liu2024).

Generally, research on the impact of climate change on agricul-

ture has focused on observational experiments and model sim-

ulations (Huang2014; Chen and Gong2021; Yang etal.2024).

Crop models are currently the most scientific and ideal method

for quantifying the impacts of climate change (Mera 2006;

Tao etal.2018; Zhao etal.2023; Long etal.2024). The APSIM

(Agricultural Production Systems sIMulator) model is an ef-

fective tool for studying the impacts of climate change on crop

production and soil dynamics (Keating et al. 2003; Rurinda

etal.2015; Kivi etal.2022). In recent years, the APSIM model

has been widely used to assess the impact of climate change

on agricultural systems due to its capability to simulate crop

growth, soil moisture and nutrient cycling (Yang etal.2018; Liu,

Harrison etal.2023; Liu, Liu etal. 2023; Xiao etal.2024; Liu

etal.2024).

In Northeast China, previous studies on the impact of climate

change on agriculture have primarily focused on single crops

such as maize, with relatively few studies on Maize–Soybean

rotation and a lack of attention to soil health (Su etal.2021;

Jägermeyr et al. 2021; Guilpart et al. 2022). Therefore, this

study focuses on the Maize–Soybean rotation system and uti-

lises the APSIM model to predict crop yields and soil organic

carbon under future climate scenarios in different latitude re-

gions of Northeastern China. The objectives of this study are

to (1) investigate the effects of Maize–Soybean rotation pat-

terns in different maturing regions on the yield and perfor-

mance of maize and soybean under future change scenarios;

(2) The effects of Maize–Soybean rotation patterns in differ-

ent cropping areas on SOC and SOCD under future change

scenarios were studied; (3) The system reveals the effects of

climate change in different maturing regions on soil carbon

content and crop yield change trends under climate change

scenarios. These predictions will help understand the poten-

tial impacts of climate change on the agricultural system in

Northeastern China and provide references for developing

adaptive measures, thereby promoting the sustainable devel-

opment of regional agriculture.

2 | Material and Methods

2.1 | Study Area

The Northeast region (118° E- 135° E, 38° N- 55° N) consists

of Heilongjiang, Jilin, Liaoning and parts of eastern Inner

Mongolia. It borders Russia to the north, while the Yellow Sea

and Bohai Sea lie to the south. The expansive region boasts rich,

fertile soil, predominantly black soil, coupled with an abun-

dance of water resources. These optimal conditions create a

prime environment for agricultural growth and development.

The Northeast region spans from the central temperate zone to

the cold temperate zone from south to north, belonging to a tem-

perate monsoon climate with distinct four seasons. The sum-

mers are warm and rainy, while winters are cold and dry. The

annual precipitation ranges 400–1000 mm, transitioning from

humid to semi- humid to semi- arid regions. The accumulated

temperature above 0°C is 2500°C–4000°C, with a frost- free pe-

riod of 90–180 days. The main cropping system in the Northeast

region is one crop per year.

To analyse the impact of climate change on crop yields and soil

organic carbon (SOC) in the Maize–Soybean rotation system, this

study selected typical locations in the early, mid and late- maturing

regions of Northeast China for simulations. The early- maturing

region includes research stations in Yichun, Qiqihar, Jiamusi,

Shuangyashan and Jixi; the mid- maturing region includes

Summary

• The APSIM model reveals the impact of climate

change on the yield and SOC of the Maize–Soybean

rotation in Northeast China, with spatio- temporal dif-

ferences in changes.

• PLS- PM elucidates the interplay between climate,

SOC storage and crop yield, highlighting intricate ag-

ricultural system dynamics.

• Precipitation and temperature changes have different

effects on SOC accumulation and storage. SOC is pos-

itively correlated with crop yield and the correlation is

stronger under RCP8.5.

• The crop yield and SOC in different maturity regions

are dif ferently affec ted by climate change, and the cop-

ing strategies should be adapted to local conditions.

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stations in Changchun, Siping, Liaoyuan, Yanji and Tonghua; and

the late- maturing region includes stations in Fushun, Shenyang,

Benxi, Anshan and Dandong (Figure1). These regions encompass

the heartlands of the early, medium and late maturation zones.

They are characterised by diverse soil types, including black and

brown soils, and landscapes that span plains, hills and mountains.

This selection comprehensively considers various variables such

as soil characteristics, topographical features and geomorphologi-

cal conditions. This approach ensures the broad applicability and

scientific integrity of the model's predictions.

The soil profile data for the study were obtained from the

International Soil Reference Information Centre ISRIC (https://

www. isric. org/ ), the national soil information service platform

(http:// ww w. soili nfo. cn/ map/ index. aspx) and Chinese soil da-

tabase (http:// vdb3. soil. csdb. cn/ ). The index includes the bulk

density (g·cm−3), soil total nitrogen content (%), soil field water

capacity (mm·mm−1), clay (%), silt (%), sand (%), soil organic car-

bon content (%), soil pH and other indexes of different soil levels,

and some soil missing data were obtained from the experiment.

The basic information of each research site is shown in Table1.

2.2 | Future Climate Prediction and Calibration

The climate data required for the experiment include daily ra-

diation, maximum temperature, minimum temperature, rain-

fall, evapotranspiration, etc. The data part of the historical

meteorological data (1980–2010) was obtained from the Climate

Data Sharing Service of China Meteorological Administration

(http:// data. cma. cn/ ) and for future climate (2011–2100) predic-

tion, we used the data in CMIP5 (Coupled Model Intercomparison

Project Phase5) under RCP4.5 and RCP8.5 (Representative con-

centration pathways) climate scenarios using data from the

HadGEM2- ES model (Vanli Vanli etal.2019; Ahmadi etal.2021).

The specific calculation formula of future climate calibration is

as follows:

where Pcorrection was the original site data under the future cli-

mate model, Psimulation was the data under the future climate

model,

Pmeasured

was the observed meteorological data from 1980

to 2010, and

Psimulation

was the average of predicted data under

the future climate model.

2.3 | APSIM Model Calibration and Verification

2.3.1 | Crop Varieties and Parameters

In this experiment, three maize and soybean varieties were se-

lected accordi ng to the accumulated temperature chara cteristics in

Northeast China. The maize and soybean varieties planted in each

(1)

correction =Psimulation

Pmeasured

simulation

FIGUR E  | The experimental station of the Chinese Academy of Agricultural Sciences.

4 of 16 Journal of Agronomy and Crop Science, 2025

region were: the early- maturing regions were DeMeiya 3 (DMY3)

and Jiadou 33 (JD33); the mid- maturing regions are Xianyu 335

(XY335) and Hemong 126 (HN126); the late- maturing regions are

Danyu 405 (DY405) and Jinong 75 (JN75) (Figure1). These vari-

eties are extensively cultivated in Northeast China, with the ‘trial

and error’ approach employed to fine- tune and validate the variety

parameters. The experimental varieties and parameters planted in

different cropping areas are shown in Table2.

2.3.2 | Model Calibration and Verification

The model parameters were calibrated (years of 2005–2010)

and validated (years of 2011–2016) by applying field- measured

maize and soybean yields before applying the model in this

study. The following statistical indicators were used to evaluate

the applicability of APSIM model in this study area: coefficient

of determination R2 between simulated and measured values,

root mean square error RMSE, normalised root mean square

error nRMSE and D- value (Willmott 1981; Zhao et al. 2020).

The R2 and D values can reflect the consistency between the

measured value and the simulated value. The closer the value is

to 1, the closer the regression line between the measured value

and the simulated value is to the 1:1 line, the better the simu-

lation effect. RMSE and nR MSE are the absolute and relative

errors between the measured value and the simulated value, and

the smaller the value, the better the simulation effect (Mentaschi

etal.2013; Yang etal.2014). When nRMSE is ≤ 30%, the simu-

lation result is acceptable; when it is ≤ 20%, the simulation re-

sult is ‘good’; when it is ≤ 10%, the simulation result is ‘excellent’

(He etal.2020; Feleke etal.2021; Zhou etal.2022). The model

performance evaluation indicators were computed using the fol-

lowing equations:

where Si and Oi were the simulated and observed values;

and

were the mean of simulated and observed values, respectively; n

was the sample number.

2.4 | Soil Organic Carbon Density

We also calculated the soil organic carbon density (SOCD)

(Arunrat etal. 2020), which is the storage capacity of soil or-

ganic carbon in the soil layer at a certain depth per unit area,

with the formula shown in Equation(6):

(2)

n

i=1



Si−S



Oi−O





i=1

−S





−O



(3)

RMSE









i=1Si−S

(4)

nRMSE

RSME

×100

(5)

=1−

n

i=1



Si−Oi

2



i=1



−O



−O





TABLE  | Representative site data for the Northeast regions.

Maturing

region Station

Latitude and

longitude

Altitude

(m) Agrotype

SOC

(0–20 cm)

(g kg−1)

SOC

(20– 40 cm)

(g kg−1)pH

Early-

maturing

Yichun (YC) 128.8° E , 47.7° N 264.8 Dark brown soil 16.1 7. 5 4.8

Qiqihar (QQHR) 123.9° E, 47.4° N 147.1 Alkaline soil 9.2 4.2 9.3

Jiamusi (JMS) 130.3° E, 46. 8° N 82.0 Meadow soil 32.8 32.3 5.5

Shuangyashan (SYS) 131.2° E, 46 .6° N 175.3 Lithosol 19.6 18.5 5.4

Jixi (JX) 130.9° E, 45.3° N 272.5 Dark brown soil 16.1 7.5 5.8

Mid-

maturing

Changchun (CC) 125. 2° E, 43.2° N 236.8 Albic soil 16.5 15.4 6.3

Siping (SP) 124.3° E , 43. 2° N 165.7 Black soil 25.0 18.4 6.2

Liaoyuan (LY) 125.1° E, 42.9° N 252.9 Albic soil 16.4 15.3 5.9

Yanji (YJ) 129.5° E, 42.8 ° N 257.3 Dark brown soil 14.7 13.8 6.5

Tonghua (TH) 125.9° E, 41.7° N 402.9 Dark brown soil 20.4 18.3 5.7

Late-

maturing

Fushun (FS) 124.1° E, 41.9° N 118.5 Brown soil 19.8 6.1 5.8

Shenyang (SY) 123.5° E , 41.7° N 49.0 Brown soil 11.9 6.2 6.3

Benxi (BX) 123.8° E, 41.3° N 185.4 Brown soil 19.8 6.0 6.8

Anshan (AS) 123.0° E, 41.1° N 77.3 Yellow brown soil 19.7 6.1 4.8

Dandong (DD) 124 .3° E, 40.1° N 13.8 Brown soil 24.9 15.9 6.0

5 of 16

where SOCD is the soil organic carbon density (kg·m−2), SOCDi

is the organic carbon density of the i- th soil layer (kg·m−2), Ci is

the soil organic carbon content (g·kg- 1), Di is the soil bulk weight

(g·cm−3); Hi is the soil layer thickness (cm), Si is the volume con-

tent of soil gravel > 2 mm (%).

2.5 | Data Analysis

In this experiment, the study area was mapped using ArcGIS10.8,

and the measured data were analysed and plotted using

Microsoft Excel 2021, IBM SPSS Statistics 25 and Origin 2021.

The least significant difference (LSD) post- test at p < 0.05 was

used to identify differences. Partial least squares path modelling

(PLS- PM) was used to explore the effects and the contributions

of climate change in different maturing regions on trends of

soil organic carbon and crop yield under two climate scenarios

(early- , mid- and late- maturing regions are defined as 1, 2 and 3,

respectively, and the data used is the change slope of each indi-

cator over a hundred years). Before the analysis, the normality

of the data was inspected through descriptive statistics (mean,

standard deviation, skewness and kurtosis) and the Shapiro–

Wilk test. Non- conforming data were either transformed or an-

alysed using non- parametric tests. The homogeneity of variance

was evaluated with the Levene test. In case of unequal variance,

the Welch correction was used in the analysis of variance, and

the weighted least squares method was adopted in the regres-

sion analysis. These procedures ensured the rationality of the

data assumptions and provided strong statistical support for the

conclusions.

3 | Results

3.1 | Model Calibration and Evaluation

Table3 show the APSIM model calibration results at the 15 rep-

resentative stations (Figure 1) from 2005 to 2016 in Northeast

China according to different maturing regions. In this experi-

ment, for the yield of maize and soybean, R2 and D- index of the

APSIM model after calibration and validation are mostly above

0.9, and nRMSE is less than 10%, indicating a good fit between

the measured and simulated values (Table3). The comprehen-

sive evaluation indicates that the model simulation result is ‘ex-

cellent’; therefore, the model can accurately simulate the change

of crop yield under future climate change.

3.2 | Changes in Projected Climate

Figure2 shows precipitation and temperature changes in different

maturing regions under different future climate scenarios. With

1981–2010 as the baseline period, under the RCP4.5 scenario, the

annual precipitation from 2011 to 2100 is projected to increase by

47.2–78.1 mm, and the annual average temperature is expected to

rise by 2.0°C–2.2°C; under the RCP8.5 scenario, the annual pre-

cipitation during the same period is anticipated to increase by

35.6–75.3 mm, and the annual average temperature is predicted to

increase by 2.8°C–3.3°C. Compared with RCP4.5, the prediction

under RCP8.5 is more reliable and stable, with a higher R2 value.

Under both scenarios, the upward trend in temperature is more

significant and stable than the fluctuation trend in precipitation.

A statistical analysis was performed on the changes of precip-

itation and temperature in different maturing regions under

(6)

SOCD

∑k

i=1

SOCDi=

∑k

i=1

Ci×Di×Hi×

(

1−Si

)

∕

100

TABLE  | Planting varieties and parameters information in different regions.

Crop Parameter Meaning description and unit E M L

Maize DMY3 X Y335 DY405

tt_emerg_to_endjuv Thermal time from emergence to end of juvenile/(°C·day) 155 165 175

tt_endjuv_to_init Thermal time from juvenile to floral initiation/(°C·day) 25 30 55

photoperiod_slope Photoperiod slope/(°C·h−1)22 23 22

tt_flower_to_start_grain Thermal time from flowering to the start

of the filling period/(°C·day)

100 120 135

tt_flower_to_maturity Thermal time from flowering to maturity/(°C·day) 760 860 960

rue Radiation use efficiency/(g·M J−1)1.75 1.75 1.75

Soybean JD33 HN126 JN75

tt_emerg_to_endjuv Thermal time from emergence to end of juvenile/(°C·day) 45 60 165

tt_endjuv_to_init Thermal time from juvenile to floral initiation/(°C·day) 350 380 30

photoperiod_slope Photoperiod slope/(°C·h−1)14 15 23

tt_flower_to_start_grain Thermal time from flowering to the start

of the filling period/(°C·day)

276 285 120

tt_start_to_end_grain Thermal time from flowering to maturity/(°C·day) 560 610 830

tt_maturity_to_harvest Thermal time from maturity to harvest/(°C·day) 5 8 15

Note: E, early- matur ing region; M, mid- maturing region; L, late- mat uring region.

6 of 16 Journal of Agronomy and Crop Science, 2025

TABLE  | Simulated yield and measured yield of maize based APSIM model.

Crop Variet y Type Year

Simulated

yield (t ha−1)

Measured

yield (t ha−1)R2D

nRMSE

(%)

Maize DMY3

(Early- maturing)

Calibration 2005 9.53 9.52 0.99 0.93 4

2007 9.89 9.56

2009 11.71 11.03

Validation 2011 11.47 11.5 0.9 0.75 4

2013 11.85 11.58

2015 11.54 11.59

XY335

(Mid- maturing)

Calibration 2005 7.21 7.40 10.99 3

2007 11.34 11.74

2009 10.74 10.92

Validation 2011 10.75 10.23 0.94 0.8 6

2013 9.55 9.96

2015 11.60 10.87

DY4 05

(Late- maturing)

Calibration 2005 10.45 10.49 0.99 0.97 1

2007 10.96 10.92

2009 11.22 11.07

Validation 2011 10.21 10.52 0.94 0.94 2

2013 11.46 11.79

2015 10.82 10.73

Soybean JD33

(Early- maturing)

Calibration 2006 2.68 2.77 0.97 0.89 2

2008 2.88 2.89

2010 2.64 2.70

Validation 2012 2.88 2.89 0.98 0.97 2

2014 3.15 3.03

2016 2.64 2.64

HN126

(Mid- maturing)

Calibration 2006 3.46 3.53 0.94 0.94 2

2008 3.46 3.39

2010 3.19 3.11

Validation 2012 3.30 3.39 0.9 0.86 4

2014 3.28 3.25

2016 2.86 3.06

JN75

(Late- maturing)

Calibration 2006 3.15 3.16 0.83 0.9 1

2008 3.25 3.23

2010 3.21 3.24

Validation 2012 3.14 3.19 0.98 0.95 3

2014 3.43 3.57

2016 3.59 3.65

7 of 16

two climate scenarios (Figure 3). From the perspective of

precipitation, the increase of precipitation was significantly

increased from early- to late- maturing region (p < 0.0 01), and

the increase of precipitation under RCP8.5 was significantly

higher than that under RCP4.5 (p < 0.01), and there was an

interaction effect between the maturing region and climate

FIGUR E  | Trends of annual average precipitation and temperature in early- maturing (A, B), mid- maturing (C, D) and late- maturing (E, F) re-

gions under two future climate scenarios. The dashed line is the fitted linear trend line.

FIGUR E  | Changes of the average values of precipitation (A) and temperature (B) from 2011 to 2100 compared with 1981–2010. Different low-

ercase letters indicated significant difference at 0.05 level. E, early- maturing region; M, mid- maturing region; L, late- maturing region; ns, p > 0.05;

**p < 0.01; ***p < 0. 001.

8 of 16 Journal of Agronomy and Crop Science, 2025

scenario. From the perspective of temperature, the tempera-

ture increase from early- to late- maturing regions decreased

significantly (p < 0.001), and the increase rate under RCP8.5

was significantly higher than that under RCP4.5 (p < 0. 0 01),

and there was no interaction effect between climate scenar-

ios and maturing regions, indicating that the temperature in-

crease of different maturing zones under different scenarios

showed the same difference.

3.3 | Changes in Projected Crop Yields

The crop yield of Maize–Soybean rotation was predicted using

the APSM crop model (Figure 4). Under the RCP4.5 scenario

from 2011 to 2100 compared to 1981–2010, the annual yield of

maize in early- and mid- maturing regions increased by 59 and

428 kg ha−1, respectively, while it decreased by 116 kg ha−1 in the

late- maturing region. For soybean, the annual y ield increased by

FIGUR E  | Trends of maize and soybean y ields in early- maturing (A), mid- maturing (B) and late- maturing (C) regions under two future climate

scenarios. The dashed line is the fitted linear trend line.

9 of 16

39 kg ha−1 in the early- maturing region, decreased by 47 kg ha−1

in the mid- matur ing region, and 131 kg ha−1 in the late- maturing

region. Under the RCP8.5 scenario, the annual yield of maize

increased by 354, 398 and 71 kg ha−1 in early- , mid- and late-

maturing regions, respectively. Meanwhile, the annual yield of

soybean decreased by 162, 93, and 56 kg ha−1.

A statistical analysis was performed on the changes of maize

and soybean yield in different maturity regions under two cli-

mate scenarios (Figure5). The results showed that, in terms of

maize yield, the increase of maize yield in both early- maturing

and late- maturing regions showed a trend of first increasing

and then decreasing, and there was no significant difference be-

tween the two climatic scenarios. Under the RCP4.5 scenario,

soybean yield gradually decreased from increased to decreased,

and under RCP8.5 scenario, the yield decreases gradually de-

creased. However, there was no significant difference between

different climate scenarios, cropping regions and their interac-

tions (p > 0.05), indicating that the effects of climate change on

crop yield trends in different cropping regions were consistent.

3.4 | Changes in Projected Soil Organic Carbon

Storage

The effects of different cropping zones on soil organic carbon

content (SOC) in the 0–20 and 20–40 cm tilling layers under two

climate scenarios were calculated (Figure6). From early- to late-

maturing region, SOC showed a decreasing trend. The 0- 20 cm

SOC increased year by year in different maturing regions, and

the 20- 40 cm SOC decreased year by year, and the decrease was

greater. Compared with the base period, under the RCP4.5 sce-

nario, the SOC in the 0–20 cm layer of the early- , mid- and late-

maturing region increased by 1.7, 0.8 and 1.3 g kg−1, respectively,

and the SOC in the 20–40 cm layer decreased by 2.2, 2.8 and

1.1 g kg−1, respectively. Under the RCP8.5 scenario, the SOC in

the 0–20 cm layer of the early- , mid- and late- maturing region in-

creased by 1.4, 1.0 and 1.8 g kg−1, respectively, and the SOC in the

20– 40 cm layer decreased by 2.1, 2.6 and 1.6 g kg−1, respe ctively.

Statistical analysis was made on SOC changes of 0–20 and 20-

40 cm soil in different mature areas under two climate scenar-

ios (Figure 7). The results showed that the SOC increase rate

of 0- 20 cm soil was 0.19–0.33 g kg−1 10a−1, and there was no sig-

nificant difference among different cropping areas and climate

scenarios ( p > 0.05). The SOC reduction rate of 20 - 40 cm soil was

0.18–0. 46 g kg−1 10a−1, and there was a significant trend from

early- maturing to late- maturing area that the SOC increased

first and then decreased (p < 0.05), but there was no significant

difference under different climate scenarios (p > 0 .05).

The simulation results show that under RCP4.5 and RCP8.5,

soil organic carbon density (SOCD) in Northeast China will de-

crease year by year (Figure8). Compared with the base period,

SOCD of 0- 40 cm in early- , mid- and late- maturing regions de-

creased by 21, 334 and 72 kg ha−1 10a−1 under RCP4.5, and 119,

280 and 26 k g ha−1 10a−1 under RCP8.5, respectively. Among

them, the reduction extent in the mid- maturity region was the

greatest. Both the early- maturity region and the late- maturity

region exhibited a trend of initially increasing and then decreas-

ing. The reduction degree under the RCP8.5 scenario was gener-

ally higher than that under the RCP4.5 scenario.

3.5 | PLS- PM Analysis

Partial least squares path modelling (PLS- PM) was used to anal-

yse the impact of climate change in different maturing regions

under two climate scenarios on the future trend of crops yield

(Figure 9). The results showed that under the Maize–Soybean

rotation system, precipitation change in the future climate sce-

nario would have a significant negative impact on SOC accumu-

lation, and only under the RCP8.5 scenario would temperature

rise have a significant negative impact on SOC storage. There

was a significant positive correlation between SOC storage and

crop yields, and the correlation was stronger under RCP8.5. The

model accounted for 63% and 32% of precipitation variability,

84% and 65% of temperature variability, 41% versus 53% in maize

yields, and 45% versus 53% in soybean yields. Crop yield vari-

ability was better explained under RCP8.5. Nonetheless, RCP4.5

yielded a superior overall model fit, with a GoF of 0.59 com-

pared to 0.53 for RCP8.5. In conclusion, under a certain degree

of climate change, the Maize–Soybean crop rotation system can

effectively cope with it, but under more extreme climate condi-

tions, it may require further optimisation of planting systems

and measures.

FIGUR E  | Changes of the average values of maize (A) and soybean (B) yield from 2011 to 2100 compared with 1981–2010. Different lowercase

letters indicated significant difference at 0.05 level. E, early- maturing region; M, mid- maturing region; L, late- maturing region; ns, p > 0.05.

10 of 16 Journal of Agronomy and Crop Science, 2025

4 | Discussion

The results of this study indicate that climate change signifi-

cantly affects the Maize–Soybean rotation system in Northeast

China. Under future climate scenarios, the crop yields in the ear-

ly- , mid- and late- maturing regions are expected to show vary-

ing degrees of change. The APSIM model simulations predict

that temperature rises and changes in precipitation patterns will

result in modified growing conditions, particularly in regions

with shorter growing seasons, such as the early- maturing areas.

These regions are more susceptible to adverse climate effects,

such as drought and heat stress, which may reduce crop yields

(Zhu, Liu, Qiao etal.2022; Zhu, Liu etal.2022; Sun etal.2023;

Riedesel et al. 2024). Photosynthesis is fundamental to crop

growth and yield development. While the rate of photosynthesis

can escalate with temperature, excessively high temperatures

dampen the activity of key enzymes, leading to a decline in

photosynthetic efficiency (Tian et al. 2024). In dry conditions,

plants close their stomata to conserve water, a response that re-

stricts carbon dioxide intake and decelerates the photosynthetic

FIGUR E  | Trends of 0- 20 cm and 20- 40 cm soil organic carbon content in early- maturing (A), mid- maturing (B) and late- maturing (C) regions

under two future climate scenarios. The dashed line is the fitted linear trend line.

11 of 16

FIGUR E  | Changes of the average values of soil organic content in 0- 20 cm (A) and 20- 40 cm (B) from 2011 to 2100 compared with 1981–2010.

Different lowercase letters indicated significant difference at 0.05 level. E, early- maturing region; M, mid- maturing region; L, late- maturing region;

ns, p > 0.05.

FIGUR E  | Trends in SOCD in 0–40 cm of farmland soils from 1981 to 2100.

FIGUR E  | PLS- PM analysis of the combined effects of different maturing regions on crop yield changes under future climate scenarios. Single-

headed arrows indicate the hypothesised direction of causation. The indicated values are the path coefficients. Red arrows indicate a positive effect, where-

as blue arrows indicate a negative effect. The arrow width is proportional to the strength of the relationship. R2 on the parameters indicates the percentage

of the variance explained by other variables. *p < 0.05; ***p < 0.001. L1 is the first soil layer (0–20 cm) and the L2 is the second soil layer (20–40 cm).

12 of 16 Journal of Agronomy and Crop Science, 2025

process. Concurrently, as temperatures climb, respiration inten-

sifies, particularly at night, potentially depleting more carbo-

hydrates and diminishing the carbon available for growth and

yield. Additionally, drought and heat stress can prompt lipid

peroxidation in plant cell membranes, compromising cellular

integrity and impairing overall cellular function (Shenawa and

Alfalahi 2021). However, in mid- and late- maturing regions,

the effects of climate change may be less severe and even ben-

eficial to some extent due to extended growing periods and in-

creased temperatures that favour crop growth. This variation

in crop yield trends across different regions highlights the need

for region- specific adaptation strategies in response to climate

change. For example, breeding heat- tolerant varieties and op-

timising sowing times could help mitigate negative impacts in

vulnerable regions. Crops in regions with medium and late ma-

turity require an extended period for development and matura-

tion. This longer growth duration can enhance photosynthetic

activity, boost biomass accumulation and ultimately result

in more robust seed filling. A moderate rise in temperature

can stimulate metabolic processes, including photosynthesis,

thereby promoting crop growth.

Another significant finding of this study is the impact of climate

change on soil organic carbon (SOC) content in the Maize–

Soybean rotation system. The results suggest that SOC levels

will likely decline over time due to rising temperatures and

changes in precipitation patterns, particularly in early- maturing

regions. This decline is concerning because SOC plays a critical

role in maintaining soil fertility, water retention and overall soil

health (Layek etal. 2022; Zhang etal. 2024). The loss of SOC

can lead to reduced soil productivity, affecting long- term agri-

cultural sustainability (Santos etal. 2023; Burger et al.2023).

The results also indicate that the magnitude of SOC reduction

differs across regions, with early- maturing regions experienc-

ing the most significant losses. Regions with early- maturing

crops feature abbreviated growth cycles, which translates into

a lower influx of plant residues and root biomass into the soil.

Additionally, these areas are often subject to drier climatic con-

ditions. Under such circumstances, moisture plays a pivotal role

in regulating microbial activity and the breakdown of SOC. The

drought can suppress microbial activity, potentially triggering a

swift decomposition of SOC (Liang etal.2024). In contrast, mid-

and late- maturing regions may experience relatively smaller

declines in SOC, in these regions, the extended growing cycles

allow for greater input of plant residues and root biomass into

the soil, contributing to the preservation or elevation of SOC

levels. The adoption of conservative soil management tech-

niques, like no- tillage or the use of cover crops, can safeguard

soil structure and minimise SOC decomposition. Moreover, fa-

vourable climatic conditions, such as moderate rainfall, facili-

tate the stabilisation of soil microbial activity and SOC retention

(Liu, Harrison etal.2023; Liu, Liu etal. 2023; Ma etal.2024).

These findings emphasise the importance of implementing soil

management practices, such as cover cropping, conservation

tillage and organic matter amendments, to mitigate SOC loss

and enhance soil health in the face of climate change (Ma and

Shi2024; Wu etal.2024).

The variability in crop yield and SOC trends across different

maturing regions in Northeast China highlights the need for

tailored adaptation strategies to achieve sustainable agriculture.

In early- maturing regions, where climate change is expected to

have more severe impacts, adaptation measures should focus on

improving crop resilience to heat stress and drought conditions.

This could involve the development of drought- resistant maize

and soybean varieties, as well as the adoption of water- saving ir-

rigation technologies (Couëdel etal.2021; Nguyen etal.2023). In

mid- and late- maturing regions, where climate change may pro-

vide some benefits, such as extended growing seasons, strategies

should focus on optimising planting schedules and improving

nutrient management to take full advantage of favourable con-

ditions (Lu etal.2015; Zimmermann etal.2017; Zhu, Liu, Qiao

etal.2022; Zhu, Liu etal.2022). Additionally, maintaining and

enhancing SOC levels should be a priority across all regions, as

SOC is essential for long- term soil health and agricultural pro-

ductivity. Practices such as crop residue retention, agroforestry

and organic amendments could help maintain SOC levels and

promote sustainable agricultural production.

The Maize–Soybean rotation system plays a crucial role in

ensuring regional food security in Northeast China (Chen

etal.2018). The results of this study indicate that this rotation

system can still provide stable yields under future climate sce-

narios, particularly in mid- and late- maturing regions. However,

early- maturing regions may face challenges in maintaining

food production due to the negative impacts of climate change

on crop yields and SOC levels. To address these challenges, it

is essential to implement adaptive measures that improve the

resilience of the Maize–Soybean rotation system to climate

variability (Ray etal.2015; Guilpart etal. 2022). Advanced ag-

ricultural technologies, including genetically enhanced crops,

high- efficiency irrigation and sophisticated crop management,

are instrumental in offsetting the detrimental effects of climate

change on agricultural productivity (Sabir et al. 2024; Wang

etal.2024). For instance, the development of drought- and salt-

resistant crop strains enables agriculture to thrive under more

severe environmental conditions. To guarantee consistent crop

yields, technological innovations must align with evolving cli-

mate patterns. As temperatures rise due to climate change, the

adoption of heat- resistant crop varieties and enhanced irriga-

tion methods becomes essential for sustaining production lev-

els (Ahmad etal.2024). Promoting the adoption of sustainable

agricultural practices, such as integrated nutrient management

and conservation agriculture, could help enhance food security

by improving crop yields and soil health. Policymakers should

also consider investing in agricultural research and extension

services to support farmers in adopting climate- resilient tech-

nologies and practices.

This study provides valuable insights into the potential impacts

of climate change on the Maize–Soybean rotation system in

Northeast China and offers a theoretical basis for developing

adaptive strategies to promote sustainable agriculture. To coun-

teract climate change challenges, strategies must target regional

adaptations. Early- maturing regions should adopt drought-

resistant, early yielding crops for abbreviated seasons and low

moisture, advance sowing times and implement conservation

tillage to prevent soil erosion and water loss. Water conserva-

tion through methods like drip and sprinkler irrigation, along

with soil moisture retention techniques such as mulching with

crop residues or biodegradable films, should be encouraged.

Meanwhile, medium and late- maturing areas should opt for

13 of 16

late- maturing varieties for longer growth periods, align planting

with shifting growing seasons, and fine- tune fertiliser use for

optimal and sustainable nutrient uptake.

There are several limitations in this study. Firstly, the different

ways of returning straw to the field have diverse effects on soil

nutrients, but these were not compared in our study and are

not captured by the model. Secondly, the APSIM model does

not consider the complex environmental and human factors

present in field experiments. Therefore, its results are only ap-

proximations and cannot fully replace field data when guiding

actual management decisions. Agricultural producers should

thus choose reasonable farm management measures according

to local conditions to ensure high maize yields. Simultaneously,

we cannot overlook the decline in soil organic carbon density

(SOCD) due to global temperature rise. SOCD is crucial for

maintaining sustainable agricultural development and soil fer-

tility, highlighting the need to optimise the conservation policy

of the black soil in Northeast China. Future research should

delve into refining the APSIM model's predictive accuracy by

contemplating the integration of additional environmental

factors, such as the intricacies of soil microbial community

dynamics and the influence of varying atmospheric CO2 con-

centrations on crop physiological responses. It should also in-

vestigate how different agricultural management practices,

such as fertiliser application tactics and irrigation procedures,

interact with climate change to affect crop yields and soil or-

ganic carbon levels. Moreover, the research should entail long-

term field studies to substantiate the model's findings and to

appraise the practical utility of adaptation strategies in real-

world agricultural settings.

5 | Conclusions

This study quantified the effects of climate change on the pro-

duction and SOC of Maize–Soybean rotation in Northeast China

by using the APSIM model and historical climate (baseline,

1980–2010) and two future climate projections (2011–2100).

Climate data show that the average daily mild precipitation

will increase significantly under both climate scenarios in the

future, and the increase under RCP8.5 is significantly higher

than that under RCP4.5, while the increase of precipitation will

increase, and the increase of temperature will decrease from

early- to late- maturing regions. The results of APSIM model

show that the effects of climate change on crop yield and soil

organic carbon storage under the Maize–Soybean rotation sys-

tem in Northeast China are significantly different in spatial and

temporal variations. Compared with the baseline (1980–2010),

the changes of maize yield in different maturity zones under

the two future climate scenarios ranged from −11.6 to 42.8 kg

10a−1 (RCP4.5) and from 7.1 to 39.8 kg 10a−1 (RCP8.5). Soybean

yields varied from −13.1 to 3.9 kg 10a−1 (RCP4.5) and − 16.2 t o

−5.6 kg 10a−1 (RCP8.5). Under the RCP4.5 scenario, the annual

yield of maize increased in early- and mid- maturing regions

and decreased in late- maturing regions and that of soybean in-

creased in early- maturing regions and decreased in mid- and

late- maturing regions. In RCP8.5 scenario, maize increased,

and soybean decreased in all maturing regions. The influence of

climate change on crop yield trends in different growing areas

was consistent, and the difference was not significant. Under

the two future climate scenarios, soil organic soc. increased

slowly from 0 to 20 cm SOC and decreased from 20–40 cm SOC,

resulting in a decrease of 21–334 kg ha−1 10a−1 (RCP4.5) and 26–

280 kg ha−1 10a−1 (RCP8.5) in the predicted future soil organic

carbon storage compared with baseline. PLS- PM results show

that future precipitation change has a negative impact on SOC

accumulation, and temperature rise has a negative impact on

SOC storage in the RCP8.5 scenario. SOC storage is positively

correlated with crop yield, and the correlation is stronger under

RCP8.5, which has a higher explanation for crop yield changes.

Adapting agricultural practices is crucial for mitigating climate

change's impacts on crops and soil. Effective Maize–Soybean ro-

tation can enhance sustainability and food security in Northeast

China, but tailored strategies are needed for different regions

to address specific climate challenges. Early- maturing regions

advance drought- resistant, early varieties and employ conser-

vation tillage for water- saving irrigation. Medium and late-

maturing areas utilise later- maturing varieties for prolonged

growth, tweak planting times and refine fertiliser use.

Author Contributions

Rui Liu: data curation, investigation, resources, writing – original

draft. Hongrun Liu: investigation, data curation, resources, writing

– original draft. Tianqun Wang: investigation, data curation. Ting

Wan g: data curation. Zhenzong Lu: investigation, data curation. Xue

Yuan: investigation, data curation. Runzhi Li: supervision, writing –

original draft, project administration, resources, funding acquisition,

methodology, conceptualization.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available from the

corresponding author upon reasonable request.

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Metabonomics Analysis Reveals the Influence Mechanism of Three Potassium Levels on the Growth, Metabolism and Accumulation of Medicinal Components of Bupleurum scorzonerifolium Willd. (Apiaceae)

Article

Full-text available

Apr 2025

Bupleurum scorzonerifolium Willd. is a commonly used bulk Chinese herbal remedy. Due to the large-scale mining of wild Bupleurum scorzonerifolium Willd., its natural resources are gradually exhausted. In addition, there are some problems in Bupleurum scorzonerifolium Willd. cultivation, such as lack of guidance, excessive application of fertilizers and so on, which lead to the yield and quality of Bupleurum to be below the standard value. Therefore, it is significant to clarify the regulation of quality and yield under different levels of fertilizers. In this study, three different levels of potassium fertilizer were applied; then, the metabolites in different parts of Bupleurum were analyzed by gas chromatography–mass spectrometry (GC–MS) to detect the alterations in the metabolic spectrum and recognize both the accumulation and distribution of key metabolites in response to each level of potassium fertilizer. The contents of various mineral elements, such as sodium, calcium, potassium, magnesium, manganese, zinc, iron, and copper, in different parts of Bupleurum under different potassium levels were determined. Potassium fertilizer had a significant impact on the absorption and distribution of these mineral elements. There were synergistic and antagonistic effects between each element and K⁺. The results showed that low and high potassium levels could promote the progression of main shoots and roots, but inhibited the accumulation of dry matter in lateral shoots and flowers. Low potassium levels stimulated the content of saikosaponin a in all plant parts, while high potassium levels inhibited the accumulation of most saikosaponin a,c and d. A total of 77 metabolites were identified by GC–MS, of which glycerol, d-glucose, silane and copper phthalocyanine were highlighted as the key metabolites in response to potassium fertilizer. The abovementioned metabolites are mapped into insulin signaling pathways, streptomycin biosynthesis, galactose metabolism and other metabolic pathways, sustaining the metabolic regulation of Bupleurum scorzonerifolium Willd.

Conservation Tillage Mitigates Soil Organic Carbon Losses While Maintaining Maize Yield Stability Under Future Climate Change Scenarios in Northeast China: A Simulation of the Agricultural Production Systems Simulator Model

Article

Full-text available

Dec 2024

Global warming may reduce maize yields and soil organic carbon (SOC), potentially threatening global food security and soil health. To address this concern in Northeast China, one of the world’s major maize production areas, the maize Agricultural Production Systems Simulator Model (APSIM) was used to evaluate how different tillage methods and straw return practices affect maize yields and SOC under future climate scenarios. The purpose of this study is to deal with the threat of global warming to the yield and SOC in the northeastern maize-producing areas, explore sustainable agricultural management strategies to stabilize the yield, enhance the soil carbon pool, counter the impact of climate change, and seek ways to ensure regional food and soil health. This study explored three tillage methods—plowing tillage (PT), rotary tillage (RT), and no-tillage (NT)—and two straw return methods—straw return (SR) and no straw return (SN)—under two Representative Concentration Pathway (RCP) scenarios: RCP4.5 and RCP8.5. The results showed that under the climate change scenarios: (1) For different tillage methods, no-tillage (NT) management showed the greatest increase in crop yield at 6.2%. SOC is highest under NT in the 0–20 cm soil layer under both straw return methods and climate scenarios. (2) For different straw return methods, SOC decreases when the straw is removed (SN) but increases when the straw is returned (SR) in both scenarios. Soil organic carbon density (SOCD) declines but can be mitigated by straw return. (3) Overall, tillage and straw return practices can significantly impact SOC under RCP4.5 but not under RCP8.5. Tillage and straw return practices together explain more than 50% yield changes under climate change scenarios. Through the modeling approach, this study revealed the potential benefits of integrating tillage and straw management practices to sustain maize yields and SOC. These practices can mitigate long-term climate change impacts on crop yields and soil health.

A Review on the High-Efficiency Detection and Precision Positioning Technology Application of Agricultural Robots

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The advancement of agricultural technology has increasingly positioned robotic detection and localization techniques at the forefront, ensuring critical support for agricultural development through their accuracy and reliability. This paper provides an in-depth analysis of various methods used in detection and localization, including UWB, deep learning, SLAM, and multi-sensor fusion. In the domain of detection, the application of deep algorithms in assessing crop maturity and pest analysis is discussed. For localization, the accuracy of different methods in target positioning is examined. Additionally, the integration of convolutional neural networks and multi-sensor fusion with deep algorithms in agriculture is reviewed. The current methodologies effectively mitigate environmental interference, significantly enhancing the accuracy and reliability of agricultural robots. This study offers directional insights into the development of robotic detection and localization in agriculture, clarifying the future trajectory of this field and promoting the advancement of related technologies.

Improving Northeast China’s soybean and maize planting structure through subsidy optimization considering climate change and comparative economic benefit

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LAND USE POLICY

Climate change induced heat and drought stress hamper climate change mitigation in German cereal production

Article

Oct 2024
FIELD CROP RES

Cereal Crops in the Era of Climate Change: An Overview

Chapter

Sep 2024

Cereal crops, being the most consumed food material on the planet, face significant challenges confronted by climate change. The detrimental impacts of climate change have threatened food security globally by hampering the production and quality of major cereal crops like rice, wheat, and maize. Understanding and assessing the challenges these crops face is critically important for developing meticulous strategies to enhance resilience. In this chapter, we explored an overview of the adverse effects of climate change on the production of cereal crops. We discussed how the issue of climate change is rising day by day with the emissions of greenhouse gases from multiple sources and highlighted the various stresses that cereal crops are facing. Moreover, this chapter explains the economic consequences of climate change on cereal production and examines the strategies being adapted to mitigate these changes. A comparison of the production of the top 5 cereal crops between 2000 and 2022 with the top 3 producers has been discussed briefly. Modern breeding technologies, agronomic best management (ABM) approaches, and climate-smart farming practices must be investigated to tackle the challenges. By using genomics approaches like marker assisted selection, genomic selection and genome editing like CRISPR/Cas9, researchers can create crop varieties that are more resistant to the negative consequences of climate change. Moreover, we emphasized the collaboration of various sectors in making an effective sustainable practice policy, its implementation and educating the farmers through the support by government which is the need of the hour. Through these collective developments and the promotion of sustainable agricultural methods, we can minimize the impact of climate change.

Simulation of the physiological and photosynthetic characteristics of C3 and C4 plants under elevated temperature and CO2 concentration

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Sep 2024
ECOL MODEL

Future soil organic carbon stocks in China under climate change

Article

Aug 2024

Remote Sensing and Precision Agriculture: A Sustainable Future

Chapter

Aug 2024

To address challenges like resource scarcity, destruction of the environment, and food security, the chapter examines the transformative impact of remote sensing and precision agriculture. In addition to its potential for soil mapping, crop monitoring, health assessment, and early pest and disease identification, it draws attention to the value of adopting technology into agricultural sustainability. Using data integration and analysis, the chapter additionally highlights the relationship between remote sensing and precision agriculture, with Geographic Information Systems (GIS) functioning as a fundamental component for incorporating data. The foundation for comprehensive analysis is the combination of field data and remote sensing, which is backed by machine learning algorithms and data processing methods. The application of remote sensing and precision agriculture has multiple sustainability advantages, such as resource optimization, environmental awareness, enhanced production, financial success, and resistance to market changes. In addition, technological challenges place an extreme value on technology adoption and education and identify new trends including the combination of remote sensing with the Internet of Things (IoT) and artificial intelligence. The chapter’s conclusion draws together essential ideas, highlighting the interdependence of precision agriculture, sustainable agricultural methods, and remote sensing. It serves as a preview of a day when agricultural areas will be advanced in technology and ecologically harmonious.

Optimizing food crop layout considering precipitation uncertainty: Balancing regional water, carbon, and economic pressures with food security

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Aug 2024
J CLEAN PROD

Differences of soil carbon pools and crop growth across different typical agricultural fields in China: The role of geochemistry and climate change

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Jul 2024
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Sustainability of Maize–Soybean Rotation for Future Climate Change Scenarios in Northeast China

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