Yield Potential Of Maize Research Articles

Multimodal Deep Learning Integration of Image, Weather, and Phenotypic Data Under Temporal Effects for Early Prediction of Maize Yield

Maize (Zea mays L.) has been shown to be sensitive to temperature deviations, influencing its yield potential. The development of new maize hybrids resilient to unfavourable weather is a desirable aim for crop breeders. In this paper, we showcase the development of a multimodal deep learning model using RGB images, phenotypic, and weather data under temporal effects to predict the yield potential of maize before or during anthesis and silking stages. The main objective of this study was to assess if the inclusion of historical weather data, maize growth captured through imagery, and important phenotypic traits would improve the predictive power of an established multimodal deep learning model. Evaluation of the model performance when training from scratch showed its ability to accurately predict ~89% of hybrids with high-yield potential and demonstrated enhanced explanatory power compared with previously published models. Shapley Additive explanations (SHAP) analysis indicated the top influential features include plant density, hybrid placement in the field, date to anthesis, parental line, temperature, humidity, and solar radiation. Including weather historical data was important for model performance, significantly enhancing the predictive and explanatory power of the model. For future research, the use of the model can move beyond maize yield prediction by fine-tuning the model on other crop data, serving as a potential decision-making tool for crop breeders to determine high-performing individuals from diverse crop types.

Comparative stress physiological analysis of aluminium stress tolerance of indigenous maize (Zea mays L.) cultivars of eastern Himalaya

Yield potential of maize having distinct genetic diversity in Eastern Himalayan Region (EHR) hill ecologies is often limited by Al toxicity caused due to soil acidity. Stress physiological analysis of local check exposed to 0–300 μM Al under sand culture revealed that 150 μM Al as critical and 200 μM Al as tolerable limit. Increase in Al from 0 to 300 μM reduced total chlorophyll, carotenoids by 74.8 % and 44.7 % respectively and enhanced anthocyanin by 35.3 % whereas LA, SLW and SL have reduced by 81.3%, 21.3 % and 47.8 % respectively. R/S ratio was 51.0 and 13.7 % higher at lower Al levels (50 μM and 100 μM) and photosynthetic, transpiration rate and TDM were 62.5 %, 42.9 % and 78.6 % lower at higher Al (300 μM) as compared to control. TRL, RSA, RDW and RV at higher Al (300 μM) were 92.6 %, 98.7 %, 78.7 and 97.5 % lower over control respectively. Root and shoot Al and PUpE at higher Al (300 μM) was 194.0, 69.2 and 830 % higher whereas PUE decreased to 88.5 % over control. Evaluation of 31 indigenous maize cultivars at 0, 150, and 250 μM Al in sand culture, alongside tolerance scoring and assessment, revealed that Megha-9, Megha-10, and MZM-19 exhibits high Al tolerance, Megha-1, MZM-22, and MZM-42 demonstrated moderate tolerance, whereas Uruapara, Sublgarh, and BRL Para were identified as Al-sensitive. Stress physiological parameters like SDW, TDM, TRL, SL and LA contributed 46.02 % of variability to PC1, whereas A, RV, RSA, anthocyanin and Chlorophyll_b, contributed 13.56 % of variability to PC2. Highest values of CMS, SL, LP, LA, TRL and anthocyanin were recorded in cluster I having sensitive cultivars while highest CMS, SL, LA, LP, TRL and RSA were found in cluster II having moderately tolerant cultivars and highest mean values for TRL, RSA, LP, LA, CMS and SL were recorded in cluster III having highly Al stress tolerant cultivars. The traits viz., A, RV, RSA, anthocyanin and Chlorophyll_b, total chlorophyll and TDM were emanated as physio-morphological for assessing Al toxicity stress tolerance in Maize with high divergence values. Tolerant cultivars showing 63.4 % and 22.4 % higher anthocyanin at 150 μM Al and 250 μM Al than moderately tolerant one in acid soil experiment with increased root Al, shoot Al, root P and shoot P by 42.6 %, 11 %, 95.1 % and 34 % respectively were emerged as promising for novel maize improvement under acid soils of EHR.

Effects of depth of straw returning on maize yield potential and greenhouse gas emissions.

Appropriate straw incorporation has ample agronomic and environmental benefits, but most studies are limited to straw mulching or application on the soil surface. To determine the effect of depth of straw incorporation on the crop yield, soil organic carbon (SOC), total nitrogen (TN) and greenhouse gas emission, a total of 4 treatments were set up in this study, which comprised no straw returning (CK), straw returning at 15cm (S15), straw returning at 25cm (S25) and straw returning at 40cm (S40). The results showed that straw incorporation significantly increased SOC, TN and C:N ratio. Compared with CK treatments, substantial increases in the grain yield (by 4.17~5.49% for S15 and 6.64~10.06% for S25) were observed under S15 and S25 treatments. S15 and S25 could significantly improve the carbon and nitrogen status of the 0-40cm soil layer, thereby increased maize yield. The results showed that the maize yield was closely related to the soil carbon and nitrogen index of the 0-40cm soil layer. In order to further evaluate the environmental benefits of straw returning, this study measured the global warming potential (GWP) and greenhouse gas emission intensity (GHGI). Compared with CK treatments, the GWP of S15, S25 and S40 treatments was increased by 9.35~20.37%, 4.27~7.67% and 0.72~6.14%, respectively, among which the S15 treatment contributed the most to the GWP of farmland. GHGI is an evaluation index of low-carbon agriculture at this stage, which takes into account both crop yield and global warming potential. In this study, GHGI showed a different trend from GWP. Compared with CK treatments, the S25 treatments had no significant difference in 2020, and decreased significantly in 2021 and 2022. This is due to the combined effect of maize yield and cumulative greenhouse gas emissions, indicating that the appropriate straw returning method can not only reduce the intensity of greenhouse gas emissions but also improve soil productivity and enhance the carbon sequestration effect of farmland soil, which is an ideal soil improvement and fertilization measure.

Genetic diversity analysis and heterotic grouping of Indian white maize inbred lines using combining ability and SNP markers

AbstractMaize (Zea mays L.) is a versatile crop with high genetic yield potential and various uses. While yellow maize is primarily used for animal feed, white maize is preferred as a food by rural and tribal populations in India. However, white maize landraces suffer immensely in terms of low yield. Heterotic grouping is an excellent method to develop hybrids with increased yield potential in maize. The heterotic grouping of 78 white maize inbred lines was performed using combining ability and molecular diversity analysis. The lines were crossed with two testers, and the resulting hybrids were evaluated for grain yield at three locations representing different agroecological zones in India. The combining ability analysis based on specific combining ability effects on mean grain yield was used to classify the inbred lines into heterotic groups (HG‐A: 18 inbreds; HG‐B: 33 inbreds). Additionally, marker‐based clustering using 62,470 single nucleotide polymorphisms (SNPs) generated through genotyping by sequencing was also employed to determine the genetic relationships among these lines. The combining ability information was superimposed on marker‐based clustering data to obtain the heterotic grouping information, and two distinct HGs with 7 (HG‐1) and 18 inbred lines (HG‐2) were developed. The results of this study will contribute to the development of high‐performing white maize hybrids through the efficient utilization of clear‐cut and well‐defined HGs. Hence, the use of SNP markers along with the combining ability of inbred lines is a useful method for heterotic grouping and understanding of genetic relationships of white maize.

Yield performance and agronomic characteristics of several candidate hybrid maize varieties on uncultivated land

Corn is currently a strategic and dominant national commodity used as raw material for animal feed. The increase in productivity was made possible by the continued assembly of hybrid corn which has a higher production per hectare with a wide or site-specific adaptation rate. Due to the wide utilization of maize, the main objective of all commercial maize breeding programs is to obtain new inbred and hybrids which will outperform the hybrids mainly on grain yield characteristics. This research was conducted in Bone Regency to know the yield potential of several hybrid maize on uncultivated land, using a randomized block design, 4 replications, a plot area of 3 x 5 m, and spacing of 70 x 20 cm. The material used consisted of 4 hybrids and 2 comparison varieties. The results showed that the hybrid G1603 (11.61 t/ha) showed the highest grain yield and was not significantly different from the comparison varieties. This result was supported by the character of the number of rows of seeds per ear, the length of the ear, and the diameter. These characteristics need to be considered to get hybrid corn with a high grain yield.

Agronomic Performance of IPB Maize Hybrids and Combining Ability of the Lines

Maize is a significant food crop consumed in numerous countries worldwide. Increasing the yield potential of maize can be achieved through the breeding of high-yielding hybrid varieties. The objectives of this study were to assess the performance of hybrid means and parent combining ability for grain yield and other agronomic traits using a line x tester mating design. The study took place at the Gunung Gede Experimental Station of IPB University, Bogor, from June to October 2022. The experiment was conducted using a randomized complete block design with a single genotype factor and three replications. Twenty-two hybrids were utilized as genetic materials, including 20 hybrid candidates and two check varieties. Six maize lines (H1, L26, Nei, P2B, and P42) were employed as line parents, while L15 and P10 were used as tester parents in the line x tester analysis. The neighbor-joining tree analysis indicated that the female parent has a significant influence on the agronomic and yield performance of the hybrids. Correlation analysis revealed that the yield is more strongly influenced by ear diameter rather than ear length, and it is more affected by the number of kernel rows than the number of kernels per row. Three selected hybrids with favorable traits were Nei x B1, L15 x L26, and L26 x P10. The parent L26 demonstrated relatively good general combining ability for yield and may be considered for future maize breeding program.

Predicting zinc‐enhanced maize hybrid performance under stress conditions

AbstractThe low yield potential of most biofortified maize is a barrier to its full adoption and reduces its potential to curb various macro‐ and micronutrient deficiencies highly prevalent in low‐income regions of the world, such as sub‐Saharan Africa (SSA). By crossing biofortified inbred lines with different nutritional attributes such as zinc (Zn), provitamin A and protein quality, breeders are attempting to develop agronomically superior and stable multi‐nutrient maize of different genetic backgrounds. A key question, however, is the relationship between the biofortified inbred lines per se and hybrid performance under stress and non‐stress conditions. In this study, inbred line per se and testcross performance were evaluated for grain yield and secondary traits of Zn‐enhanced normal, provitamin A and quality protein maize (QPM) hybrids and estimated heterosis under combined heat and drought (HMDS) and well‐watered (WW) conditions. Responses of all secondary traits, except for the number of days to mid‐anthesis, significantly differed for HMDS and WW conditions. The contribution of heterosis to grain yield was highly significant under both management levels, although higher mid and high‐parent heterosis was observed under WW than HMDS conditions. However, the findings suggest that inbred line performance was the best determinant of hybrid performance under HMDS. Strong correlations were observed between grain yield and secondary traits for both parents and hybrids, and between secondary traits of inbred lines and hybrids under both management levels, indicating that hybrid performance can be predicted based on intrinsic inbred line performance. Phenotypic correlation between grain yield of inbred lines and hybrids was higher under HMDS than WW conditions. This study demonstrated that under HMDS conditions, performance of Zn‐enhanced hybrids could be predicted based on the performance of their corresponding inbred lines. However, the parental inbred lines should be systematically selected for desirable secondary traits correlated with HMDS tolerance during inbred line development.

Analysing Maize Plant Height Using Unmanned Aerial Vehicle (UAV) RGB based on Digital Surface Models (DSM)

Maize (Zea mays L.) is one of the essential agricultural products in Papua Province of Indonesia, specifically in the three largest maize producing regions, namely Nabire Regency, Biak Numfor Regency and Merauke Regency, with the number of productions of 991 tons, 764 tons, and 751 tons respectively in 2015. Unfortunately, since 2016 the secondary data on food crops productivity, including maize, has not been provided yet in the provinces statistical report, due to manual estimation methods, i.e., visual estimation. On the other side, the number of populations in this Province has a slight increase, from 2.97 million people in 2012 to 3.38 million in 2019. Further, approximately 1.20 million people are employed in the agricultural sector. Considerable population growth will intensify the demand for food stock and other utilization of food crops in this region; hence, relevant research in food crops needs to be considered. One of the dominant factors in the yield potential of maize is plant height, since it is associated with fertilizer, seed, and soil treatment and predicts yield area. Therefore, this study aims to analyse the plant height, particularly maize plant based on a digital surface model (DSM) derived from Unmanned Aerial Vehicle (UAV) Red Green Blue (RGB) images. The crop was monitored during the second and third week of January 2022 and then, processed using pix4d Mapper software to produce the DSM, Digital Terrain Model (DTM), and orthomosaic. Then, the Geographical Information System (GIS) software, and an open-source software, namely Python were used to estimate the plant height. Next, the results were assessed statistically to examine the validation, the strong correlation coefficient of the estimation to the actual height that obtained from UAV and ground-based plant height data. The findings will help to support the prior decision support on estimation of maize production in Papua Province.

Yield potential and selection of off-season maize for silage and grain using GT Biplot under limited water and frost

Maize silage has been used as a forage reserve strategy for critical periods or continuous use in animal feed. However, new genotypes and their potential must be identified. Thus, this study aimed to evaluate the potential of maize genotypes for silage and grain in one off-season in the midwest region of Brazil, under limited water and frost, and select them for this dual purpose (silage and grain) using the GT Biplot tool. The experiment was performed at Embrapa Western Agriculture in the autumn-winter season of 2021 in Dourados, Mato Grosso do Sul, Brazil. The experimental design consisted of randomized blocks of six maize genotypes (BRS1010, KWS9606, 1P2224, 1Q2383, BRS3046, and CAPO) with five replications under no-tillage. Silage points were evaluated at harvest when the grain milk line was at ¾ and maize grains at the maturation stage (dry plant). The 1P2224 and 1Q2383 maize genotypes present silage (high green and dry biomass) and grain yield potential. The GT Biplot tool identified the 1P2224 genotype as superior and suitable for cultivation or as a parent in a breeding program in the midwest region of Brazil for silage and grain yield evaluations of one off-season under limited water and frost.

Data-Driven Artificial Intelligence (AI) Algorithms for Modelling Potential Maize Yield under Maize–Legume Farming Systems in East Africa

Agroecological farming systems such as maize–legume intercropping (MLI) and push-pull technology (PPT) have been introduced to mitigate losses from pests. Nevertheless, the regionwide maize yield gained from practicing such farming systems remains largely unknown. This study compares the performance of two uncomplex and interpretable models, namely the hybrid fuzzy-logic combined with the genetic algorithm and symbolic regression, to predict maize yield. Specifically, the study adopted the best-fitting model to map the potential maize yield under MLI and PPT compared to the monocropping system in East Africa using climatic and edaphic variables. The best model, i.e., the symbolic regression model, accurately fitted the maize yield data as indicated by the low root mean square error (RMSE < 0.09) and the higher R2 (>0.9). The study estimated that East African farmers would increase their annual maize yield by about 1.01 and 1.96 rates under MLI and PPT, respectively. Furthermore, the results showed a fairly good modelling performance as indicated by low standard deviations (range of 0.70–1.1) and skewness (absolute range of 0.03–0.09) values. The study guides the upscaling of MLI and PPT systems through awareness creation and public-private partnerships to ensure increased adoption of these sustainable farming practices.

Kernel color and fertilization as factors of enhanced maize quality.

Maize is an important staple crop and a significant source of various nutrients. We aimed to determine the macronutrients, antioxidants, and essential elements in maize genotypes (white, yellow, and red kernel) using three different fertilizers, which could be used as a basis to increase the nutrient density of maize. The fertilizer treatments used bio- and organic fertilizers as a sustainable approach, urea, as a commonly used mineral fertilizer, and the control (no fertilization). We evaluated the yield, concentration of macronutrient (protein, oil, and starch), nonenzymatic antioxidants (phenolics, yellow pigment, total glutathione (GSH), and phytic phosphorus), and reduction capacity of the 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical, as well as essential elements that are commonly deficient in the diet (Mg, Ca, Fe, Mn, Zn, Cu, and S) and their relationships with phytic acid. The genotype expressed the strongest effect on the variability of grain yield and the analyzed grain constituents. The red-kernel hybrid showed the greatest accumulation of protein, oil, phenolics, and essential elements (Ca, Fe, Cu, and S) than a yellow and white hybrid, especially in the biofertilizer treatment. The yellow kernel had the highest concentrations of yellow pigment, GSH, phytic phosphorous, Mg, Mn, and Zn (19.61 µg g-1, 1,134 nmol g-1, 2.63 mg g-1, 1,963 µg g-1, 11.7 µg g-1, and 33.9 µg g-1, respectively). The white kernel had a greater starch concentration (2.5% higher than that in the red hybrid) and the potential bioavailability of essential metals, particularly under no fertilization. This supports the significance of white maize as a staple food in many traditional diets across the world. Urea was important for the enhancement of the antioxidant status (with 88.0% reduction capacity for the DPPH radical) and increased potential Zn bioavailability in the maize kernels (13.3% higher than that in the biofertilizer treatment). This study underlines the differences in the yield potential and chemical composition of red, yellow, and white-kernel maize and their importance as a necessary part of a sustainable human diet. This information can help determine the most appropriate genotype based on the antioxidants and/or essential elements targeted for kernel improvement.

POTENTIAL OF MAIZE (Zea mays) YIELD IN THE SAVANNA OF KANO STATE, SEMI-ARID REGION OF NIGERIA

This study examined the Savanna agro-ecologies potential of maize yield in Kano State. Field experiment consisted of five different fertilizer treatments namely were replicated three times in a Randomized Complete Block Design. The trial was established at three different locations namely: Doguwa (Northern Guinea Savanna), Bunkure (Sudan Savanna 2) and Dambatta (Sudan Savanna1) with a total of fifteen treatments(Five fertilization for three locations). White maize hybrid cultivar (SC651) was planted at 25cm and 75cm intra plants and inters ridges respectively. The results showed that highest maize yield potential was in the Northern Guinea Savanna with mean yield of 4.284 t/ha while lowest in the Sudan Savanna 1 with mean yield of 1.927 t/ha. Fertilization using Nutrients Expert (NE) has the highest maize yield while lowest maize yield of was observed in Regional Recommendation (RR) fertilization in all the three locations. It further revealed that there is significant maize yield difference between Doguwa (NGS), Bunkure (SS2) and Dambatta (SS1) with p value <0.001 at 0.05 significant level.

How could observed sowing dates contribute to maize potential yield under climate change in Northeast China based on APSIM model

The Northeast China (NEC) accounts for more than 30% of the national total maize planted area (grain yield). Adjusting the sowing dates has been considered an effective measure to adapt to climate change, but there was little evidence that how well the producers had done. In this study, we used phenology observations at 67 stations from 1981 to 2014 to detect trends in actual sowing dates, then the agricultural production systems simulator, APSIM-Maize model, was used to assess the effects of changes in observed sowing dates on maize phenology and yields. During the past 34-year period, the actual maize sowing dates show a delaying tendency, at a rate of 1–6 days per decade, but there are significant fluctuations among years. For per day delay in the sowing dates, the whole growing season was shortened by 0.1%. Delaying sowing dates reduced the solar radiation interception during the vegetative period as well as the thermal time during the reproductive period. As a result, the overall maize potential yield was negatively affected in NEC; for per day delay in the sowing dates, the potential yield was decreased by 0.6%. By contrast, advancing sowing dates in some years led to increases in both the solar radiation interception during the vegetative period and the thermal time during the reproductive period. However, these increases showed various effects on the maize potential yield across different parts of the study region. We detected a positive effect of advancing sowing dates on maize potential yield in the high latitudes, at a rate of up to 1.6%. By contrast, in the low latitudes, the negative effect of advancing sowing dates on maize potential yield was dominant, at a rate of up to 2.7%.

Climate and agronomy, not genetics, underpin recent maize yield gains in favorable environments

Quantitative understanding of factors driving yield increases of major food crops is essential for effective prioritization of research and development. Yet previous estimates had limitations in distinguishing among contributing factors such as changing climate and new agronomic and genetic technologies. Here, we distinguished the separate contribution of these factors to yield advance using an extensive database collected from the largest irrigated maize-production domain in the world located in Nebraska (United States) during the 2005-to-2018 period. We found that 48% of the yield gain was associated with a decadal climate trend, 39% with agronomic improvements, and, by difference, only 13% with improvement in genetic yield potential. The fact that these findings were so different from most previous studies, which gave much-greater weight to genetic yield potential improvement, gives urgency to the need to reevaluate contributions to yield advances for all major food crops to help guide future investments in research and development to achieve sustainable global food security. If genetic progress in yield potential is also slowing in other environments and crops, future crop-yield gains will increasingly rely on improved agronomic practices.

Yield-limiting plant nutrients for maize production in northwest Ethiopia

Summary The potential yield of improved maize varieties usually cannot be fully realised mainly due to inappropriate soil nutrient management practices in most parts of Ethiopia. Site-specific fertiliser recommendations are rarely used in the farming systems of Ethiopia. There is also a lack of data to develop or validate decision support tools for targeting specific crop production. A study was conducted for three consecutive rainy seasons (2016–2018) in the maize belt of the north-western parts of the Amhara National Regional State of Ethiopia. The objectives were to obtain the maximum achievable yield potential of maize, determine the most yield-limiting nutrients and create a database of maize responses to applied nutrients so that decision support tools could be developed for the study areas. Treatments were individual nutrients (nitrogen (N), phosphorus (P) and potassium (K)) and combinations of the three. In some treatments, NPK was also combined with sulphur, zinc, lime and compost. Two hybrid maize varieties (BH-540 and BH-660) adaptable to the study areas were used. BH-540 was used for the Mecha district, while BH-660 was used for the south Achefer, Jabitahnan–Burrie–Womberma districts. Maize yield increased by more than 50% due to fertiliser applications compared to without fertiliser. The study showed that the possibility of increasing maize productivity to more than 12 t ha-1 for the study sites. The most yield-limiting nutrient in the study sites was N, followed by P; K was not a yield limiting. Without N the yield of both varieties was non-significant from the control (without added nutrients). Maize grain yield did not respond to application of lime, compost, zinc and sulphur. The result also showed very high variability across sites, indicating that it is important for policymakers, farmers and investors to consider site-specific fertiliser recommendations. Finally, a database containing intensive plant response to NPK for maize was generated and could be used as input in site-specific decision support tools development.

Estimating potential yield and change in water budget for wheat and maize across Huang-Huai-Hai Plain in the future

Climate change impacts crop productivity as atmospheric conditions and water supply change, particularly in intensive cropping areas. This study used the validated AquaCrop Model, which was run with downscaled daily climate data produced by SDSM and CanESM2. The changes in the potential grain yield of winter wheat and summer maize and water budget during the cropping seasons were estimated for the Huang-Huai-Hai Plain (3H Plain) under RCP4.5 and RCP8.5 scenarios. The results show that the potential yield of winter wheat is increasing with similar spatial patterns in the 2030s, 2050s, and 2080s, with much of the increase is distributed in Shandong and northeastern parts of Henan. During the winter wheat growth period, the water budget deficit will likely improve from −210 mm in the 2030s to −202 mm in 2080s under RCP4.5 and from −206 mm in the 2030s to −191 mm in 2080s under RCP8.5 across the 3H Plain. The water budget during the winter wheat period will continue to be in deficit in the north 3H Plain and improvements are estimated mostly in the lower southern areas of the Plain. The summer maize potential yield is estimated to increase from the baseline period, but yields will decrease by 0.81%, 1.19%, and 2.10% in the 2030s, 2050s, and 2080s, respectively, under RCP8.5 compared to RCP4.5. During the summer maize growth period, the water budget is also estimated to improve from 109 mm in 2030s to 126 mm in 2080s under RCP4.5 and 107 mm in the 2030s to 163 mm in 2080s under RCP8.5. This increase is mainly estimated in the central and south of the 3H Plain. The estimated ETc of winter wheat shows no significant decrease, while the reduction of 6 mm and 13 mm for summer maize is observed under RCP4.5 and RCP8.5, respectively. The study provides scientific evidence to devise adaptation and mitigation climate change strategies for agricultural productivity and water resource management.

Dryland maize yield potentials and constraints: A case study in western Kansas

AbstractWater‐limited environments account for half of the earth's land surface, and dryland agriculture acreage is projected to expand due to climate changes. Examining typical dryland yield potentials and yield improvement measures is crucial for developing future dryland crop production systems. This case study used crop modeling to analyze dryland maize yield potential (YPd), farmers’ yield potential (YPf), and actual farm yields (Ya) from 1990 to 2015 in three counties in western Kansas (i.e., Thomas, Greeley, and Finney in the U.S. Great Plains region). The calibrated APSIM‐Maize model along with actual yields was used to estimate yield gaps attributed to: (a) agronomic factors (YG1 = YPd − YPf) and (b) socioeconomic constraints (YG2 = YPf − Ya). Observed climate conditions during maize‐growing seasons showed warming, brightening, and drying trends for all three counties in western Kansas from 1990 to 2015. Our results showed that the current actual farm yields (Ya) in western Kansas represented only 34%, 32%, and 28% of YPd in Thomas, Greeley, and Finney counties, respectively, indicating significant exploitable yield gaps. Agronomic factors (YG1) contributed the greatest to the yield gap in Greeley and Finney counties, whereas socioeconomic constraints (YG2) offered the greatest opportunity for improvement in Thomas county. Our analysis suggested that improving agronomic management could be a greater priority for further yield improvement in Greeley county, but selecting an appropriate hybrid was a greater priority in Finney county.

Modelling potential maize yield with climate and crop conditions around flowering

Understanding, and then modelling, the effects of sowing date and cultivar on maize yield is essential to develop appropriate climate change adaptation strategies. Here we test the WOFOST model and a hybrid model, based on physiological crop conditions around flowering, against observed data collected during 4 years of field experiments in a Mediterranean environment under fully irrigated conditions. We simulate sowing date and cultivar responses by using 45-year historical meteorological records from the experimental weather station and future climate conditions till 2060 as projected by a set of regional climate models.

Public–private partnership model for intensive maize production in China: A synergistic strategy for food security and ecosystem economic budget

AbstractSmall farms are the mainstay of maize production in China. Its productivity is relatively low despite large farm inputs and the associated environmental footprints. Here, we studied public–private partnership (PPP) model for sustainable intensification of maize production to achieve co‐benefits of food security and environmental sustainability. The PPP model enabled the development of an effective partnership by bringing complementary skills, knowledge, proprietary products and technologies, and resources of public research community and private enterprises to create a new, operational maize farming system in China. We conducted on‐farm research with farmer participation in four major maize‐growing regions spanning temperate to sub‐tropical zones in China for 2 years. The PPP model achieved 78.7% of maize yield potential compared with 61.8% realized in smallholder farm (SHF) (11.0 Mg ha−1 vs. 8.6 Mg ha−1). Overall, environmental externalities of PPP were up to 32.7% lower than that of SHF, depending on the region studied. PPP significantly reduced reactive nitrogen losses by 31.3%–35.5% compared with SHF in both years. There was no significant difference between PPP and SHF for greenhouse gas emission in 2018, but it was significantly lower in PPP (19%) compared to SHF in 2019. Similarly, PPP significantly reduced soil acidification potential (by 10.1%–42.2%) and eutrophication of waterbodies (21.5%) in comparison to SHF. Overall, the net ecosystem economic budget increased 277 USD ha−1 with PPP. The PPP model provides new insights into improving food security and ecosystem and economic budget. As a logical progression to our research, future work should focus on (a) the reasons for the persistence of inter‐regional yield gap in PPP model and (b) to gain a better understanding of socioeconomic drivers critical for successful PPP in different maize‐growing regions.

Optimizing Sowing Date and Planting Density Can Mitigate the Impacts of Future Climate on Maize Yield: A Case Study in the Guanzhong Plain of China

We used the APSIM-Maize model to simulate maize potential yield (Yp) and rain-fed yield (Yw) when adaptation options of sowing date and planting density were adopted under Representative Concentration Pathway (RCP) 4.5 and 8.5 in the Guanzhong Plain of China. The results showed that Yp would decrease by 10.6–14.9% and 15.0–31.4% under RCP4.5 and RCP8.5 for summer maize, and 13.9–19.7% and 18.5–36.3% for spring maize, respectively. The Yw would decrease by 17.1–19.0% and 23.6–41.1% under RCP4.5 and RCP8.5 for summer maize, and 20.9–24.5% and 27.8–45.5% for spring maize, respectively. The loss of Yp and Yw could be reduced by 2.6–9.7% and 0–9.9%, respectively, under future climate for summer maize through countermeasures. For spring maize, the loss of Yp was mitigated by 14.0–25.0% and 2.0–21.8% for Yw. The contribution of changing sowing date and plant density on spring maize yield was more than summer maize, and the optimal adaptation options were more effective for spring maize. Additionally, the influences of changing sowing date and planting density on yields become weak as climate changes become more severe. Therefore, it is important to investigate the potential of other adaptation measures to cope with climate change in the Guanzhong Plain of China.