Variation In Maize Yield Research Articles

Optimum management strategy for improving maize water productivity and partial factor productivity for nitrogen in China: A meta-analysis

China’s agricultural production suffers significant constraints due to low water productivity (WP) and partial factor productivity for nitrogen (PFPN). The pivotal solution to enhance maize yield, WP, and PFPN is through optimizing field management practices. However, previous studies primarily focused on the effects of individual or coupled field management practices on maize yield, WP, or PFPN in an area or land mass, so it is necessary to conduct a comprehensive quantitative analysis of various field management practices on maize yield, WP, and PFPN at the national scale. In this study, we compiled 286 studies encompassing 6959 pairs of experimental data spanning from 1990 to 2023. Meta-analysis was conducted to observe the variations in maize yield, WP, and PFPN across diverse field management practices (straw returning, nitrogen fertilizer application, irrigation practice, and tillage practice) on a national scale. The results showed that nitrogen fertilizer application (215 kg N ha−1) was the most effective in enhancing maize yield and WP by 68.1 % and 54.8 %, respectively. Additionally, irrigation had the most substantial impact on PFPN, enhancing 16.8 %. Straw mulching, application of slow and controlled release fertilizers, drip irrigation, and subsoiling were identified as the most effective practices in increasing maize yield, WP, and PFPN by Random forest model. Maize straw returning could reduce nitrogen fertilizer application by 20 kg ha−1, while increasing WP and PFPN by 4.2 % and 11.6 %, respectively. Moreover, straw returning could reduce water consumption by 23–60 mm, while increasing WP and PFPN by 2.9 % and 6.8 %, respectively. These findings can provide a reference for the formulation of comprehensive management strategies for sustainable maize production in China and globally.

Relationship between dry matter accumulation and maize yield in Southwest China

AbstractTo explore the differences in dry matter accumulation and yield of maize varieties having different nitrogen‐use efficiencies in Southwest China, a field experiment was conducted in Yongchuan, Chongqing, and Deyang, Sichuan, from 2019 to 2020. Two varieties, the nitrogen‐efficient Zhenghong 311 (ZH 311) and the nitrogen‐inefficient Xianyu 508 (XY 508), were tested across four nitrogen levels (0–360 kg ha−1). The results showed that compared to XY 508, ZH 311 exhibited a significantly higher accumulation of dry matter at various stages and periods, particularly in the roots during the R6 stage, and in the stem sheaths and leaves throughout all stages. Furthermore, the number of kernel rows, number of kernels per row, number of kernels per ear, and grain yield were significantly higher for ZH 311 than XY 508, whereas the 100‐grain weight was significantly lower for ZH 311 than XY 508. The yield difference between the two varieties was the largest when the nitrogen application rate was 240 kg ha−1. The yield performance of ZH 311 was always better than that of XY 508, and less nitrogen was needed to obtain the best yield. The accumulation of maize dry matter had a highly significant effect on the number of kernel rows, kernels per row, and kernels per ear, and grain yield. The direct effect of the number of kernels per ear on grain yield was very low. However, it affected grain yield through the number of kernel rows and kernels per row. The dry matter accumulation of V6−V12 and R3−R6 contributed the most to grain yield, while in vegetative organs, the effect of leaf dry matter accumulation and yield was the greatest. This investigation will provide insights into factors affecting variations in maize yield under low nitrogen conditions and offer guidance for N‐fertilizer management strategies.

Impact of nitrogen fertilizer application on soil fungal diversity and maize yield variation in Shandong Province, China

Maize is one of the major crops cultivated in northern Shandong Province and contributes significantly to China's maize production, and nitrogen (N) application to the soil can have a marked impact on maize yield and soil microbial productivity. The current environmental challenge, which needs urgently to be addressed, is to effectively utilize fertilizer nitrogen to improve crop production without causing environmental stress in sustainable agroecosystems. Soil fungi can improve plant health and crop performance through improved nutrient uptake and reducing plant stress levels. How soil N influences fungal diversity and maize yield at spatial patterns remains unclear. In this study, the influences of different soil N treatments on maize yields and fungal diversity were investigated in maize fields at five N rates (from 0 to 120 kg ha−1). High-throughput sequencing of the internal transcribed spacer (ITS) region revealed that α- and β-diversities of fungi significantly increased at 80 kg N ha−1 (N80) but decreased significantly in the control (N0) and other N treatments. Variations in the NC model predicted strong fungal distribution patterns (R2 = 0.60) among all the treatments. The highest proportion of operational taxonomic units (OTUs) (68.99 %) was observed at N80. The study also found that the complexity of the co-occurrence network significantly stabilized at N80, but a decrease in network complexity was observed at N120. The study showed that, relative to the control, the addition of N to the soil significantly increased the abundance of Ascomycota at the phylum level and Chaetomium, Fusarium, and Mortierella at the genus levels, with average abundances of 5.82 %, 1.67 %, and 19.58 %, respectively. Functional ecology analysis indicated that soil N upregulated the composition of ecological fungal communities by stochastic processes. The composition of major fungal sub-communities was positively correlated with nitrogen/carbon cycling at N80. Moreover, results also indicated that the yields (3.14 t ha−1) and other agronomic effects at N80 were significantly higher than those in the other treatments. This study demonstrates that optimal nitrogen application achieves increases in both fungal diversity and yield, contributing to improved crop production and sustainability at spatial scales.

Quantifying the indirect effects of different air pollutants on crop yields in North China Plain

High concentrations of air pollutants could affect crop yield directly via influencing crop physiology and indirectly via regulating climate conditions. How multiple air pollutants affect crop yields directly and indirectly remains elusive in the North China Plain (NCP), which is the hotspot of air pollution and crop production. Here, we construct data-driven models to quantify the indirect effects of four major air pollutants on the yields of winter wheat and summer maize through modulating climate variations in the NCP during 2005–2020. Our results show that wheat has a greater negative sensitivity to absorbing aerosol optical thickness (AAOD), ozone concentration (O3), sulfur dioxide concentration (SO2) and nitrogen dioxide concentration (NO2) than maize. The indirect effects of AAOD, O3 and SO2 in November and O3 in April control wheat yield variation, while the indirect effects of AAOD and O3 in June, O3 and SO2 in July, O3 and NO2 in August, and O3 in September dominate maize yield variation. Our results emphasize the indirect effects of air pollutants on crop yield via regulating climate variations, which has great implications for improving our understanding of air pollution-climate-crop interactions and guiding targeted adaptation and mitigation efforts.

Exploring the optimal climate conditions for a maximum maize production in Ghana: Implications for food security

In Sub-Saharan African (SSA) countries like Ghana, where a significant portion of the population relies on agriculture for their livelihoods and sustenance, climate variability poses a substantial threat to crop productivity and food security. Therefore, it is crucial to employ advanced methodologies to study the intricate relationship between climate change and crop yield. This study therefore aims to assess the impact of different climatic variables on the variation of maize yield in Ghana from 1992 to 2018 and the pivotal role of machine learning techniques in predicting the variations in maize yield, considering the complex climate-crop yield interactions. The machine learning techniques utilized include the Random Forest (RF) Model, the Extreme Gradient Boosting (XGBoost) model, and the Artificial Neural Network (ANN) model for prediction. The results demonstrate that rising temperatures and precipitation have a positive impact on Ghana's maize yield. Additionally, the study identified a critical range of climatic conditions that maximized maize production during the study period. Specifically, an average temperature between 27.9∘C and 28.1∘C, coupled with a precipitation range of 1290 mm to 1390 mm, corresponds to the optimal conditions for achieving maize yields exceeding 2.0 MT/ha. Among the machine learning models utilized for the prediction, the ANN emerged as the optimal model with an approximate mean squared error of 1%. Ultimately, our results provide a comprehensive and actionable framework for stakeholders in the agricultural sector, equipping them with the knowledge and tools needed to adapt to climate change and optimize maize production in Ghana.

Climate trends and maize production nexus in Mississippi: empirical evidence from ARDL modelling

Climate change poses a significant threat to agriculture. However, climatic trends and their impact on Mississippi (MS) maize (Zea mays L.) are unknown. The objectives were to: (i) analyze trends in climatic variables (1970 to 2020) using Mann–Kendall and Sen slope method, (ii) quantify the impact of climate change on maize yield in short and long run using the auto-regressive distributive lag (ARDL) model, and (iii) categorize the critical months for maize-climate link using Pearson’s correlation matrix. The climatic variables considered were maximum temperature (Tmax), minimum temperature (Tmin), diurnal temperature range (DTR), precipitation (PT), relative humidity (RH), and carbon emissions (CO2). The pre-analysis, post-analysis, and model robustness statistical tests were verified, and all conditions were met. A significant upward trend in Tmax (0.13 °C/decade), Tmin (0.27 °C/decade), and CO2 (5.1 units/decade), and a downward trend in DTR ( − 0.15 °C/decade) were noted. The PT and RH insignificantly increased by 4.32 mm and 0.11% per decade, respectively. The ARDL model explained 76.6% of the total variations in maize yield. Notably, the maize yield had a negative correlation with Tmax for June, and July, with PT in August, and with DTR for June, July, and August, whereas a positive correlation was noted with Tmin in June, July, and August. Overall, a unit change in Tmax reduced the maize yield by 7.39% and 26.33%, and a unit change in PT reduced it by 0.65% and 2.69% in the short and long run, respectively. However, a unit change in Tmin, and CO2 emissions increased maize yield by 20.68% and 0.63% in the long run with no short run effect. Overall, it is imperative to reassess the agronomic management strategies, developing and testing cultivars adaptable to the revealed climatic trend, with ability to withstand severe weather conditions in ensuring sustainable maize production.

Distinct soil health indicators are associated with variation in maize yield and tile drain nitrate losses

AbstractAssociations between nitrogen (N) management and losses with soil health indicators (SHI) are widely presumed but relatively untested. An on‐farm experiment conducted in central Illinois was conducted to test potential relationships of SHI with agroecosystem outcomes of maize (Zea mays L.) yield and N losses under bounding N‐fertilization rates of 168 and 252 kg/ha. Chemical (n = 19), physical (n = 11), and biological (n = 14) SHI were measured at 24 locations within a 30 ha field at five timepoints (V3, V10, RT, R6, and post‐harvest). Yields did not necessarily reflect N‐fertilization rates, with lowest yields (14.5 Mg/ha) under 224 kg/ha. Flow‐weighted nitrate‐N concentrations were significantly higher under 168 kg N/ha (10.6 mg/L) relative to higher application rates, though cumulative tile nitrate‐N loads were similar. SHI varied more by sampling location and time than by N fertilization rate. Depending on the time of sampling, distinct SHI were related to yield and tile N losses. Total soil carbon and permanganate oxidizable carbon (POXC) best explained yield variation, whereas POXC and sand content best explained variation in nitrate‐N loss. Nematode indices helped explain variability in yield (Simpson and Shannon indices) and nitrate‐N losses (maturity index), supporting recent propositions to integrate nematode measures into soil health assessments. This study provides a basis for expanding to multiyear assessments of SHI linkages with nutrient losses and crop productivity in the North Central United States.

Diverse water management in a preceding wheat crop does not affect maize yield but increases inter-annual variability: A six-year field study

As maize-based crop rotation systems are essential to ensure global food security, understanding the effect of the preceding crops and the associated management on the subsequent maize is crucial. Here we address this view with a 6-year field experiment in the North China Plain's annual winter wheat and summer maize rotation system. Effects of preceding wheat with three water management (conventional irrigation, Con.W/M; optimized irrigation, Opt.W/M; minimum irrigation, Min.W/M) on maize yield and water consumption are investigated. We find that water management in the preceding wheat crop does not affect the subsequent maize yield but increases the interannual yield variability. Over the six years, an average yield of > 10.7 Mg ha-1 is achieved for maize in the three treatments. The highest coefficient of variation (CV) of yield is observed in Min.W/M (12.0%), while there is no significant difference in CV between Con.W/M and Opt.W/M. The annual water consumption follows a decreasing trend in the order of Con.W/M (357 mm), Opt.W/M (332 mm), and Min.W/M (290 mm), which primarily attribute to variations in initial water storage in the deeper soil profile (below 80 cm). The variation in water storage would be eliminated during the following maize growing stages due to ample rainfall in the temperate continental monsoon climate. The findings show that kernel weight (KW) is the primary determinant (accounting for 55–86%) to the observed variations in maize yield. In the Min.W/M treatment, the interactive effect of killing degree days, growing degree days, maximum temperature, and solar radiation accounts for 85% of the variation observed in KW. This study provides insights into the effect of the preceding wheat on the subsequent maize for grain yield and water consumption in the rotation system.

Multivariate Interaction Analysis of Zea mays L. Genotypes Growth Productivity in Different Environmental Conditions.

Evaluating maize genotypes under different conditions is important for identifying which genotypes combine stability with high yield potential. The aim of this study was to assess stability and the effect of the genotype-environment interaction (GEI) on the grain yield traits of four maize genotypes grown in field trials; one control trial without nitrogen, and three applying different levels of nitrogen (0, 70, 140, and 210 kg ha-1, respectively). Across two growing seasons, both the phenotypic variability and GEI for yield traits over four maize genotypes (P0725, P9889, P9757 and P9074) grown in four different fertilization treatments were studied. The additive main effects and multiplicative interaction (AMMI) models were used to estimate the GEI. The results revealed that genotype and environmental effects, such as the GEI effect, significantly influenced yield, as well as revealing that maize genotypes responded differently to different conditions and fertilization measures. An analysis of the GEI using the IPCA (interaction principal components) analysis method showed the statistical significance of the first source of variation, IPCA1. As the main component, IPCA1 explained 74.6% of GEI variation in maize yield. Genotype G3, with a mean grain yield of 10.6 t ha-1, was found to be the most stable and adaptable to all environments in both seasons, while genotype G1 was found to be unstable, following its specific adaptation to the environments.

Relay intercropping of maize with common dry beans to rationalize nitrogen fertilizer

Maize (Zea mays L.) and dry beans (Phaseolus vulgaris L.) are important staple food and cash crops worldwide. Common bean in an intercrop with maize contributes to biological nitrogen fixation, which stabilize productivity of cropping systems and reduce negative environmental impacts and loss of biodiversity for sustainable agriculture. A field experiments was performed during the years of 2020 and 2021 at Sers El-Layian Station, northern Egypt. The current study aiming to study the effect of three sowing dates of maize, represent 3 co-growth duration [T1: at flowering stage (FS) of common beans (60 days co-growth duration), T2: 15 days after FS (45 days co-growth duration), and T3: 30 days after FS (30 days co-growth duration with beans)] and three N fertilizer levels (N1: 190.4, N2: 238.0, and N3: 285.6 kg N/ha of maize) on productivity, profitability and N fertilizer rationalization. The longest co-growth duration of maize intercropping with common beans (T1) significantly (P ≤ 0.05) decreased common beans and maize yields compared with T2 and T3. Performance of common beans did not show (P ≤ 0.05) any variation under different N fertilizer levels of maize. Significant (P ≤ 0.05) increase in maize yield and its components with raising N fertilizer level up to N3. Although there was no significant variation in maize yield when applied N2 and N3, however, nitrogen use efficiency (NUE) was significant (P ≤ 0.05) higher in N2 than N3 by 18.34%. Regardless of planting time and N fertilizer level of maize, combined productivity of common beans and maize increased in the intercropped system as cleared by higher total land equivalent ratios (LER) and area time equivalent ratios (ATER). Highest LER value 1.99 was observed at the shortest co-growth period T3 under N3 followed by 1.97 with N2. Positive values in the actual yield loss index (AYL) indicated intercropping advantage. Different competition indices showed a greater dominance of maize over common beans (aggressivity, Ag; competitive ratio, CR; actual yield losses, AYL). However, the intercropping systems increased the economic advantage (intercropping advantage index, IAI and monetary advantage index MAI) over monoculture. These results imply that shortening the period of co-growth maize with common beans (T3) and applying 238.0 kg N/ha in the relay intercropping system reduced mineral N fertilizer use by 16.67% compared to the advised level 285.6 kg N/ha along with increased productivity per unit area and economic advantages for small-farmer.

Reducing plant pathogens could increase crop yields after plastic film mulching

Soil fungi are closely associated with crop growth in agricultural ecosystems through processes such as nutrient uptake and pathogenesis. Plastic film mulching (PM) plays a dominant role in increasing crop yields in dryland agriculture worldwide. The functional guilds of soil fungi under PM and their effects on crops remain unclear. In this study, we explored the absolute abundance, diversity, community composition, and functional guilds of soil fungi after short-term (2 years) and long-term (10 years) mulching experiments. Short-term mulching caused a 37 %–51 % decrease in absolute fungal abundance owing to abrupt changes in the microenvironment. The response of the fungal community to PM varied with sites, with the effect being more pronounced under poor hydrothermal conditions (314 mm). The abundance of potential fungal pathogens decreased under PM; for example, Gibberella (maize ear rot) abundance was 45 % and 72 % lower under short- and long-term mulching, respectively, when compared with that in control. In contrast, the abundance of plant biocontrol fungi increased under PM; for instance, Glomeromycota abundance increased twofold under long-term mulching. Although PM did not alter the complexity and stability of fungal co-occurrence network, competition among fungi increased in the absence of sufficient carbon (C) sources. Long-term mulching reduced phytopathogen guilds by 12 %–77 % and increased arbuscular mycorrhizal fungi (AMF) guilds by 89 %–94 %. Structural equation modeling suggested that PM altered fungal functional guilds mainly by shaping the structure of the fungal community, and fungal pathogens decreased with increased AMF functional guilds, inducing higher maize yields. These results showed for the first time, from a microbial perspective, that pathogens reduction owing to PM could explain 4.4 % of maize yield variation, providing theoretical guidance to accomplish sustainability of continuous maize mulching.

Improving maize yield prediction at the county level from 2002 to 2015 in China using a novel deep learning approach

Accurate and timely maize yield monitoring from satellite imagery is in great demand in developing countries. The spatial heterogeneity deprived of the large territory of China makes it a challenge. In this article, we developed a novel deep learning model for maize yield prediction at the county level based on multiple satellite data. The two-stage feature learning structure integrated data from disparate sources, enhancing feature representation. The discriminative features were optimized from two levels: the dictionary matrix learned the spatial diversity of the provinces, and the improved optimizing formulation fit the distribution of the unbalanced records. The cross-validation results showed that our approach could explain 82 % of the variation in maize yield, achieving state-of-the-art. The model was robust when predicting the future, with the average root-mean-square error of 1006 kg/ha and the mean-absolute-percentage error of 17.1 %. The ability of early maize yield prediction clarifies the tremendous application value, showing the data from the first two months can already explain 75.6 % of yield variation. It was the first effort to improve county-level maize yield prediction in China, providing a potential framework for advancing the use of multi-source datasets for maize yield estimating.

Yield and yield stability of single cropping maize under different sowing dates and the corresponding changing trends of climatic variables

Maize (Zea mays L.) yield and yield variation are both affected by climate and cultivation management. Changing the sowing date (SD) is one of the most commonly used cultivation managements for achieving high yield of maize in North China Plain (NCP). Climate is one of the most important factors in maize yield variation under different SDs. But the yield variation under different SDs and the contribution of each climatic variable remain unclear. In this study, a 7-year field experiment of SDs was used to assess the changing trends of climatic variables under different SDs, and the relative contribution of climatic variables on yield and yield variation of maize were also evaluated. The experiment was conducted at Wuqiao Experimental Station in NCP, with 35 SDs in 7 years from 2013 to 2018, and 2021, totally. Through analyzing the historical meteorological data, our results showed that more photosynthetically active radiation (PAR) distributed in August and September, minimum temperature was increased from April to September, and high temperature days (HTDs) in a year was significantly advanced from 1990 to 2021. These changes in climate caused the optimum SD with both high yield and yield stability was in early- to mid-June, mainly because of the reduction of HTDs in 5 d pre-silking to 5 d post-silking (SS) and increased PAR in SS and silking to harvest (SH). Through variance partitioning analysis, the climatic variables in SS, SH and the whole growth stage (WS) contributed 56 %, 44 %, and 18 % of maize yield variation, respectively. In SS, 7 %, 28 % and 5 % of maize yield variation were explained by HTDs, PAR, and temperature independently. And this contribution was 18 % and 15 % of the PAR and temperature in SH. While in WS, only temperature explained 20 % of the yield variation. Our results highlight the importance of focusing on the yield stability of maize, and for the first time to clarify the differences in maize yield stability under different SDs in NCP. Meanwhile, the relative contribution of climate on yield variation was quantified. This study also proposed and predicted the optimal SDs for high and stable maize yield in NCP. These results will help to study the regional maize production under climate change in the future.

Effect of Climate, Growth and Decomposition Analysis of Maize in India

Maize is third most important cereal crop in India after rice and wheat. Maize can be grown under various agro-climatic zones.
 Aims: 
 
 To study the effect of temperature, rainfall and crop are on crop yield in maize in India.
 To study Compound Annual Growth Rate of area, production and productivity of maize in India.
 To study percentage contribution of area, yield and their interaction to maize production in India.
 
 Study Design: Only secondary data has been considered for the present study. Maize in India is considered for study purpose.
 Data Considered: All the data required for the study is obtained from Indiastat website. The impact of climatic variables on yield is studied by using data for a period of 28 year i.e., from 1992-93 to 2019-20. For calculating CAGR and for decomposition analysis data from 1950-51 to 2019-20 were considered.
 Methodology: Ordinary Least Squares method, Compound Annual Growth Rate(CAGR) and decomposition analysis were tools employed for the study.
 Results: For every 10c rise in temperature, the yield of maize decreased by 1.6 units. For every 1 mm increase in rainfall, the yield increased by 0.05 units and for every thousand hectares increase in temperature, the crop yield increased by nearly 1.37 units. Temperature, Rainfall, crop area and time explain 89 per cent of variation in maize yield. Highest and significant growth rate of 3.39 per cent per annum was observed in area during the period II i.e., from 1960-61 to 1969-70. Similarly, in the case of production and yield a highest and significant growth rate of 7.32 and 4.03 per cent respectively were observed during the period I i.e., 1950-51 to 1959-60. For the entire study period i.e., from 1950-51 to 2019-20 significant and positive growth rates of 1.26, 3.34, and 2.05 per cent per annum were observed in area, production and yield respectively. For the entire period, the contribution of interaction effect is high (58.30%) followed by yield (28.73%) and area effects(12.97%). The results revealed that area and yield effect contributes more to production than their interaction when data is considered for a shorter period (10 years) whereas the interaction effect contributes more to production if the period considered is of longer duration(70 years).
 Conclusion: Care should be taken by the government in the supply of hybrid maize seeds to farmers. Farmers should be educated about the effect of climatic variables on maize yield and they are also trained in following an improved package of practices.

Microbial keystone taxa drive crop productivity through shifting aboveground-belowground mineral element flows

Unbalanced fertilization of nutritional elements is a potential threat to environmental quality and agricultural productivity in acid soil. Harnessing keystone taxa in soil microbiome represents a promising strategy to enhance crop productivity as well as reducing the adverse environmental effects of fertilizers, with the goal of agricultural sustainability. However, there is a lack of information on which and how soil microbial keystone taxa contribute to sustainable crop productivity in acid soil. Here, we examined soil microbial communities (including bacteria, fungi, and archaea) and soil nutrients, and the mineral nutrition and yield of maize subjected to different inorganic and organic fertilization treatments over 35 years in acid soil. The application of organic fertilizer alone or in combination with inorganic fertilizers sustained high maize yield when compared with the other fertilization treatments. Microbial abundances and community structures rather than their alpha diversities explained the main variation in maize yield among different treatments. Sixteen soil keystone taxa (a fungal operational taxonomic unit and 15 bacterial operational taxonomic units) were identified from the microbial co-occurrence network. Among them, five keystone taxa (in Hypocreales, Bryobacter, Solirubrobacterales, Thermomicrobiales, and Roseiflexaceae) contributed to high maize yield through increasing phosphorus flow and inhibiting toxic aluminum and manganese flow from soils to plants. However, the remaining eleven keystone taxa (in Conexibacter, Acidothermus, Ktedonobacteraceae, Deltaproteobacteria, Actinobacteria, Elsterales, Ktedonobacterales, and WPS-2) exerted the opposite effects. As a result, maize productivity varied among different fertilization treatments because of the altered maize mineral element flows by microbial keystone taxa. We conclude that microbial keystone taxa drive crop productivity through shifting aboveground-belowground mineral element flows in acid soil. This study highlights the importance of microbial keystone taxa for sustainable crop productivity in acid soil and provides deep insights into the relationships between soil microbial keystone taxa, crop mineral nutrition, and productivity.

Climate variability and impacts on maize (Zea mays) yield in Ghana, West Africa

AbstractRain‐fed agriculture is predominantly the primary source of livelihood in sub‐Saharan Africa. In Ghana, it employs over 60% of the population and contributes to about 30% of the Gross Domestic Product (GDP). Despite the relevance of rain‐fed agriculture to the socioe‐conomic development of the country, studies investigating the impacts of climate variability on crop yield in the region are limited. This prevents the growth of relevant policy frameworks to address the threats posed by climate variability on crop production. As a contribution to guide policy planning and food security in Ghana, the present study seeks to analyze the vulnerability of maize yield to rainfall, soil moisture, and minimum and maximum temperature variability in Ghana. Moreover, the study assesses the impact of below‐normal and above‐normal rainfall conditions on maize yield in the country. The results show a decreasing trend in rainfall and soil moisture and an increasing trend in the maize yield and minimum and maximum temperature in Ghana. The individual climate variables (rainfall, soil moisture, minimum and maximum temperature) account for approximately 4.2, 22.5, 39.2, and 23.1%, respectively, of the annual maize yield variations throughout the study period. Multivariate regression analysis revealed that soil moisture and minimum and maximum temperature account for approximately 75% of the variation of maize yield under wetter‐than‐normal conditions in Ghana. This implies that, under wetter‐than‐normal rainfall conditions, 25% of the variation in maize yield could be attributable to non‐climatic conditions or practices. In like manner, climatic parameters account for 40.8% of maize yield under drier‐than‐normal rainfall conditions in the country. This study, therefore, recommends investment in climate‐smart agronomic technologies such as access to improved seed, extension and mechanization services, and other farm management practices to maximize maize yield in the country.

Building a new machine learning-based model to estimate county-level climatic yield variation for maize in Northeast China

China is one of the top maize exporting countries in the world. In China, maize is the most important staple food crop, and Northeast China is one of the main maize-growing regions. Regional maize production in Northeast China is critical for national or even global food security. In order to take preventive measures in combating the potential impacts of climate change on maize yield, it is imperative to evaluate historical climatic maize yield variation at finer scales in Northeast China. Previous scholars have achieved some good results in crop yield projection by using machine learning methods. However, climatic crop yield variation has rarely been addressed by considering the geographical factors (i.e., elevation, latitude, and longitude) of the meteorological stations used. In this study, based on 18 climatic elements during the period of 2003–2016, we compared the performance of three machine leaching methods, including neural network, support vector machine, and random forest, with multiple linear regression in evaluating climatic maize yield variation. Overall, machine learning methods are superior to traditional multilinear regression, particularly the neural network (with an R2 of 0.43 and an annual average MAE of 1.22 ton·ha−1). We selected the best machine learning methods and used the Bayesian method to integrate them as a new model. The Bayesian method reduced the evaluation error (i.e., RMSE and MAE) and improved the estimation accuracy. By addressing the uncertainty in the meteorological stations, our newly built model could be extended to other regions for evaluating climatic crop yield variation.

Contributions of Climate and Soil Properties to Wheat and Maize Yield Based on Long-Term Fertilization Experiments.

Identifying the contributions of climate factors and soil fertility to crop yield is significant for the assessment of climate change impacts on crop production. Three 20-year field experiments were conducted in major Chinese wheat-maize cropping areas. Over the 20-year period, crop yield and soil properties showed significantly dissimilar variation trends under similar climate changes at each experimental site. The correlation between climatic factors and crop yield varied greatly among the fertilization regimes and experimental sites. Across all the fertilization regimes and the experimental sites, the average contribution rates of soil properties to wheat and maize yield were 45.7% and 53.2%, respectively, without considering climate factors, and 40.4% and 36.6%, respectively, when considering climate factors. The contributions of soil properties to wheat and maize yield variation when considering climate factors were significantly lower than those without considering climate factors. Across all experimental sites and all fertilization regimes, the mean contribution rates of climate factors to wheat and maize yield were 29.5% and 33.0%, respectively. The contribution rates of the interaction of climate and soil to wheat and maize yield were 3.7% and −0.9%, respectively. Under balanced fertilization treatments (NPK and NPKM), the change in the contribution rate of soil properties to wheat or maize yield was not obvious, and the average contribution rates of the interaction of climate and soil to wheat and maize yield were positive, at 14.8% and 9.5%, respectively. In contrast, under unbalanced fertilization treatments (CK and N), the contribution rates of soil properties to wheat or maize yield decreased, and the average contribution rates of the interaction of climate and soil were negative, at −7.4% and −11.2%, respectively. The above results indicate that climate and soil synergistically affected crop yields and that, with the optimization of the fertilization regime, positive interactions gradually emerged.

Thermal Time Requirements for Maize Growth in Northeast China and Their Effects on Yield and Water Supply under Climate Change Conditions

Northeast China (NEC) is a region sensitive to climate change. However, the adoption of long-season maize cultivars in NEC has caused a substantial yield increase under climate change conditions. It is important to determine whether such cultivar adoptions are effective throughout the whole NEC to sustainably increase grain yield. In this study, phenological observations and meteorological data at six sites from 1981 to 2018 were used to detect thermal time (TT) trends during the maize growing period. TT, as a parameter for measuring changes in maize cultivars, was used in the crop simulation model CERES-Maize to examine the variations in maize yield produced with different cultivar × climate combinations in different decades. In NEC, both TTs from emergence to anthesis and from anthesis to physiological maturity showed significant increasing trends from 1981 to 2018. Simulation results for humid areas revealed that adopting longer-season cultivars during 2000–2018 caused yield increases, ranging from 6.3% to 13.3%, compared with the 1980s. However, for stations in semi-humid areas, maize grain yield showed a decrease or a small increase (from −12.7% to 8.0%) when longer-season cultivars were adopted during 2000–2018. For semi-humid areas, decreasing trends in the ratios of rainfed yield to no water-stress yield (Yrainfed/Yno water-stress) and lower Yrainfed/Yno water-stress values during 2000–2018 indicated a growing sensitivity of maize production to water, which was attributed to changes in TT and precipitation. Our results indicate that, for the semi-humid area, maize yield was limited by water after introducing cultivars with higher TT requirement under climate change conditions. Therefore, securing food supplies will depend on increases in water-use efficiency levels and other adaptive strategies, such as varietal diversification, drought-resistant varieties, conservation tillage and irrigation.

Modelling global impacts of climate variability and trend on maize yield during 1980–2010

AbstractEnhanced understanding of historical climate impacts on crop yield is critical for adaptation and mitigations within the context of global warming. Previous impact assessments rely on statistical or process‐based models, each with its own strength and weakness. To date, a global‐scale comparison between process‐based and statistical models in assessing climate impacts on yield variability and trends is lacking. Here, we leverage both statistical and process models to estimate global impacts of climate variability and trend on maize yield between 1980 and 2010. To allow for reasonable comparability, we develop a novel emulator to mimic each of eight global process‐based maize models, based on which the common set of climate scenarios are used for driving both statistical and process models. Results show that climate variability controls 42% of global maize yield variations in statistical model, while large discrepancy is found in process‐based models with R2 ranging from 0.22 to 0.61, especially at the country scale. Both statistical and most process models suggest that historical climate trend has led to a yield loss by 1.51–3.80% during the period 1980–1990. As for the period 1991–2000, however, the observed negative climate impacts are only captured by two process models. In contrast to the positive climate impact in statistical model, most of process‐based models simulated negative climate effects for the period 2001–2010, due to large yield sensitivity to maximum temperature in process models. This study highlights the large discrepancies not only among the eight process‐based models but also between statistical and process‐based models in simulating yield response to climate variation and trends, which has great implications for projecting future crop yields under climate change.