Corn Yield Research Articles

Long‐term crop yield benefits of subsurface drainage on poorly drained soils

AbstractSubsurface drainage is an important agricultural practice that has been widely utilized in the US Midwest to improve the productivity of poorly drained soils. Although widely adopted, long‐term yield benefits of drainage, particularly with varying spacings, in an ever‐changing climate are largely unknown. The goals of this study were to assess how various drainage spacings (5, 10, and 20 m) impacted crop yields compared to the undrained control in a long‐term trial (started in 1984) in southeastern Indiana and how these effects were influenced by the amount of rainfall of specific periods of the growing season. Drainage treatments led to an increase in corn (Zea mays) yields (by 12%–17%) but did not significantly affect soybean (Glycine max) yields compared to the control. In the initial 10 years of the experiment, drainage benefits were subtle and corn yields did not vary significantly across spacing treatments, whereas in the most recent 10 corn years, the drainage treatment effects became more pronounced, likely due to the combined effects of long‐term drainage system and conservation practices of no‐till and cover crops. Over 37 years, corn yields remained stagnant in the undrained plots but progressively increased in the drained treatments. Both corn and soybean yields showed a negative correlation with rainfall 14 days post‐planting, while drainage spacing treatments partially mitigated this negative effect. Our findings underscore the importance of effective drainage as a necessary prerequisite for realizing the potential benefits of conservation practices and improved crop genetics for increased crop productivity.

Respuesta del maíz harinoso (zea mays l. Var. Amylacea) a la inoculación de azospirillum y pseudomonas

Plant growth-promoting bacteria (PGPB) that inhabit root rhizosphere of plants are of great agricultural importance due to their ability to produce phytohormones during root colonization. These phytohormones produce physiological changes in the plant that favor a greater absorption of nutrients, promote growth and increase production. This study analyzed the effect of inoculation of two of these bacteria, Azospirillum sp. And Pseudomonas fluorescens, under field conditions representative of the Andean Highlands. The experiment was carried out using flour corn seed of the INIAP-101 variety in a randomized complete block design with six repetitions. The treatments corresponded to: T1 (Azospirillum sp.), T2 (P. fluorescens), T3 (Azospirillum sp. and P. fluorescens), T4 (control, conventional chemical fertilization) and T5 (absolute control). The application of these bacteria significantly increased (p<0.05) the root length, the diameter of the cob, and the yield of fresh corn (“elotes” or “choclos”). The combined inoculation of these bacteria (T3) obtained the highest yield, with 19.70 t ha-1 of fresh corn; while the chemical control and the absolute control got 17.12 and 13.58 t ha-1 of fresh corn, respectively. The economic analysis of T3 reported a benefit/cost of 1.35, which indicates that the synergism of these two bacteria could be a sustainable strategy to improve yields and reduce the use of chemical fertilizers for corn production in the Andean region.

Моніторинг ефективності зернового господарства України: фактори впливу та повоєнні перспективи

A comprehensive analysis of production efficiency was carried out, a model was developed and the degree of influence of factors on the level of profitability of growing grain crops was calculated. Forecasts of the yield of grain crops, wheat, corn, and barley for the nearest future have been built, taking into account the patterns that have developed over the previous period (1950- 2022). Forecast indicators of the profitability of the production of grain crops for the short-term perspective have been determined. The dependence of the efficiency of grain-growing enterprises on the average size of their sown areas was assessed. It was established that a positive return from the effect of scale, which is reflected in the form of an increase in productivity and profitability, is observed in grain farming enterprises when the land bank is increased to 5,000 hectares.

A machine learning models approach and remote sensing to forecast yield in corn with based cumulative growth degree days

A machine learning models approach and remote sensing to forecast yield in corn with based cumulative growth degree days

Kajian Pelaksanaan Gerakan Tanam Jagung Panen Sapi (TJPS) Dalam Meningkatkan Nilai Tukar Petani (NTP) Di Desa Manusak, Kabupaten Kupang, Provinsi Nusa Tenggara Timur

Corn and cattle are the main commodities in dry land farming in East Nusa Tenggara (NTT), with corn as a food source and cattle as a source of income. This province has vast dry land potential, namely 1,734,488 H, but its utilization is still less than optimal. The Cattle Harvest Corn Planting Program (TJPS) was launched to increase corn production, cattle population and farmer welfare.This research analyzes the Farmer Exchange Rate (NTP) in Manusak Village, Kupang Regency, and the factors that influence it, as well as examining government policies in supporting the efficiency of the corn-cattle farming system. Qualitative methods through interviews, observations and text analysis are used to understand farmers' experiences in farming.The results show that the NTP in the Gacinda and Leselik farmer groups is different: Gacinda has a higher NTP than Leselik, which is caused by differences in corn production costs and yields. In conclusion, corn productivity and costs incurred affect farmer welfare, with high expenditures potentially reducing NTP.

Planting time and seeding rate impact insect feeding, ear rots, and forage nutritive value in silage corn

AbstractPlanting date and seeding rate affect the microclimate within a crop field. They can influence lepidopteran insect infestation and fungal infections and are an important part of crop management strategy. Altering planting date and seeding rate can also influence yield and quality of silage corn (Zea mays L.). Field research was conducted to identify optimum planting time and seeding rate to minimize insect feeding, ear rot infections, and mycotoxin accumulation in silage corn without compromising yield and quality. Replicated field trials were conducted across multiple site‐years in Michigan with three planting dates (early: April 25–May 10; mid: May 11–25; and late: May 26–June 10) and/or four seeding rates (ranging from 69,160 to 113,620 seeds ha−1, in increments of 14,800 seeds ha−1). Mid‐planting yielded 12%–15% less forage than early and late plantings, partly due to greater insect feeding injury and fungal infections observed in the former. Neutral detergent fiber digestibility, starch, and crude protein concentration were greatest for early planting. Greater predicted milk per hectare and milk per megagram for early planting also indicated superior silage quality. Increasing seeding rate increased insect feeding and ear rot injury only when severity was >5% and >15%, respectively. The impact of increasing seeding rate on dry forage yield was specific to each site‐year. Overall, results showed that early planting of silage corn helps to escape insect and disease pressure and provides better yield and quality, while seeding rate response is variable and dependent on the field environment.

Weed Biology and Ecology Studies: Diversity, Dominance, Population and Weed Growth and Land Use Efficiency in Intercropping Corn (Zea mays l.) with Leguminous Crops in Dryland

The aim of the research is to examine the effect of intercropping corn with leguminous crops on weeds and land use efficiency. The experimental research method was designed using a randomized complete block design (RCBD) with three blocks. The treatments tested were monocrop planting patterns and intercropping between corn and leguminous crops. The results of the research showed that 16 weed species were found with high species diversity, evenness, dominance, and abundance, so that there were six dominant species that existed during plant growth. Peanuts and cowpeas are suitable for intercropping with corn because they can suppress the population and growth of weeds. Soybeans, green beans, and red beans are not suitable because they are not effective in suppressing the population and growth of weeds and compete with corn, so that corn yield losses due to competition are 38.20%–40.96% and due to weed competition, they reach 62.37%–63.77%. The best ecological and agronomic land use efficiency was obtained by intercropping corn with peanuts and cowpeas, with NKL values based on dry biomass weights of 1.90 and 1.89 (NKL > 1) and dry seed weights of 1.79 and 1.78 (NKL > 1).

Similarities in the US Department of Agriculture international baseline projections

AbstractThe US Department of Agriculture (USDA) international baseline projections are crucial for US agricultural policy. Using a dynamic time warping algorithm, we examine whether corn, soybean, and wheat baselines exhibit a lack of significant distance indicating similarity in projections across countries. We then examine whether projections for countries with more similarity with the United States projections exhibit higher projection errors. We discover evidence of projections being similar to the United States, which is associated with lower errors for corn yield, harvested area, and exports and wheat import projections, but not for other variables. These findings offer valuable insights for USDA and stakeholders.

Spatial and Spectral Dependencies of Maize Yield Estimation Using Remote Sensing.

Corn (Zea mays L.) is the most abundant food/feed crop, making accurate yield estimation a critical data point for monitoring global food production. Sensors with varying spatial/spectral configurations have been used to develop corn yield models from intra-field (0.1 m ground sample distance (GSD)) to regional scales (>250 m GSD). Understanding the spatial and spectral dependencies of these models is imperative to result interpretation, scaling, and deploying models. We leveraged high spatial resolution hyperspectral data collected with an unmanned aerial system mounted sensor (272 spectral bands from 0.4-1 μm at 0.063 m GSD) to estimate silage yield. We subjected our imagery to three band selection algorithms to quantitatively assess spectral reflectance features applicability to yield estimation. We then derived 11 spectral configurations, which were spatially resampled to multiple GSDs, and applied to a support vector regression (SVR) yield estimation model. Results indicate that accuracy degrades above 4 m GSD across all configurations, and a seven-band multispectral sensor which samples the red edge and multiple near-infrared bands resulted in higher accuracy in 90% of regression trials. These results bode well for our quest toward a definitive sensor definition for global corn yield modeling, with only temporal dependencies requiring additional investigation.

On-farm corn stover and cover crop residue recycling with biostimulant Re-Gen increases corn yields and resultant milk yields in multi-year dairy cattle farm trials

On-farm corn stover and cover crop residue recycling with biostimulant Re-Gen increases corn yields and resultant milk yields in multi-year dairy cattle farm trials

Using gross primary production data and deep transfer learning for crop yield prediction in the US Corn Belt

Estimating crop yield prior to harvest plays a crucial role in agricultural decision making. Machine learning models that use remote sensing (RS) data can provide quick and early estimates of crop yield. Previously, timeseries gross primary production (GPP) synthesized from RS data has been employed in agricultural applications. However, relevant research on using GPP data and deep learning models in crop yield prediction is scarce. The aim of this study was to assess the capability of MODIS-derived GPP data and deep transfer learning to predict crop yield. We developed and trained deep neural network (DNN), one-dimensional convolutional neural network (1D-CNN), and gated recurrent unit (GRU) models to predict county-level corn and soybean yields. Then we evaluated the performance of crop-type and location transfer learning strategies in crop yield prediction. The results of this study can be summarized as follows: (1) GPP data and deep transfer learning have the potential to predict crop yield with reasonable prediction accuracy; (2) in the context of transfer learning, crop-type transfer learning produced a coefficient of determination (R2) ranging from 0.521 to 0.784 for soybean and from 0.644 to 0.903 for corn. Meanwhile, location transfer learning yielded R2 values ranging from 0.690 to 0.931 for the Great Plains (GP) climatic zone and 0.480 to 0.801 for the Eastern Temperate Forest (ETF) climatic zone; and (3) For corn and soybean yield predictions, the 1D-CNN model (R2 = 0.864 for corn and 0.750 for soybean) outperformed the DNN and GRU models. Our results reveal that MODIS-derived GPP data and deep transfer learning can be used to effectively predict county-level crop yield.

Nitrogen fertilizer replacement value of stockpiled and rotted dairy cattle manures for corn (Zea mays L.) from a long-term study

Context or problemAnimal manure is an excellent renewable source of plant nutrients, but the correct quantification of nutrient equivalents is affected by manure type and application rate as well as weather conditions that impact crop yields Objective or research questionThe objectives from a 32-year field experiment were to (1) compare the effects of mineral N fertilization and solid dairy cattle manure on corn grain yield; (2) determine the manure N fertilizer replacement value (NFRV), and (3) quantify environmental factors, mainly the impact of total precipitation during the critical period (silking) on corn yield and manure NFRV. MethodsA field experiment under continuous corn with annual manure application was conducted over 32 years in Ottawa, Ontario, Canada. Corn yield data were categorized into low, intermediate, and high N response groups. ResultsOn an equivalent amount of manure organic N basis, NFRVs were, on average, 0.38 (±0.02) for stockpiled manure (SM) and 0.27 (±0.02) for incompletely composted or rotted manure (RM), with a wide range of 0.12 (±0.02), 0.28 (±0.02), and 0.37 (±0.04) for RM, and 0.16 (±0.04), 0.39 (±0.03), and 0.48 (±0.04) for SM under the low, intermediate, and high N response conditions, respectively. Supplementing 100 kg synthetic fertilizer N ha−1 in low RM (50 Mg ha−1 fresh weight; f.w.), and 50 kg fertilizer N ha−1 in high RM (100 Mg ha−1 f.w.) application rates decreased NFRVs under low and intermediate N response conditions, whereas the addition of similar synthetic N in the corresponding SM rates reduced NFRVs only under low N response conditions. ConclusionsBased on the 32-year long-term study, we documented that the maximum economic synthetic N fertilizer rates for continuous corn cropping varied from 117 to 179 kg N ha−1 under the low N response, from 127 to 283 kg N ha−1 under the intermediate N response, and from 227 to 289 kg N ha−1 under the high N response conditions. The NFRVs of solid cattle manures varied according to corn yield-N response levels. Implications or significanceOur findings showed that the response of corn yields to manure NFRV depends on the level of yield-N response and climatic conditions. It also indicated the importance of soil organic amendments for improving the climate resilience of corn production, while simultaneously meeting the demand for more sustainable nutrient management of soil resources.

Insights on agricultural nitrate leaching from soil block mesocosms.

Quantifying nitrate leaching in agricultural fields is often complicated by inability to capture all water draining through a specific area. We designed and tested undisturbed soil monoliths (termed "soil block mesocosms") to achieve complete collection of drainage. Each mesocosm measures 1.5m ×1.5m ×1.2 m and is enclosed by steel on the sides and bottom with a single outlet to collect drainage. We compared measurements from replicate mesocosms planted to corn (Zea mays L.) with a nearby field experiment with tile-drained plots ("drainage plots"), and with drainage from nearby watersheds from 2020 through 2022 under drought conditions. Annual mesocosm drainage volumes were 6.5-24.6cm greater than from the drainage plots, likely because the mesocosms were isolated from the subsoil and could not store groundwater below the drain depth, whereas the drainage plots accumulated infiltration as groundwater. Thus, we obtained consistent nitrate leaching measurements from the mesocosms even when some drainage plots yielded no water. Despite drainage volume differences, mean flow-weighted nitrate concentrations were similar between mesocosms and drainage plots in 2 of 3 years. Mesocosm annual drainage volume was 8.7cm lower to 16.7cm higher than watershed drainage, likely due to lagged influences of groundwater. Corn yields were lower in mesocosms than drainage plots in 2020, but with irrigation, yields were similar in subsequent years. Mean 2020 surface soil moisture and temperature were similar between the mesocosms and nearby fields. Based on these comparisons, the mesocosms provide a robust method to measure nitrate leaching with lower variability than field plots.

Performance of agricultural factors on yield of sweet corn (Zea mays L. Saccharata ) - A review

Sweet corn producers and industries require more reliable cultivars which could be accomplished by hybrid breeding. However, progressive phenological growth may be affected by different factors. In this paper, we analyze the key factors that determine the growth and yield of sweet corn. Environmental factors such as temperature and photoperiod were strong determinants of dates of flowering and harvest which are often crucial to yield in diverse climates and agricultural systems, besides the country's pedological conditions, especially soil fertility, affected phenological development. The effectiveness of fertilization in improving sweet corn growth performance was significantly influenced by the soil characteristics, the water supply, the genotype, and the agrotechnological factors. Therefore, genetic improvement of hybrids should be incorporated into the climate and soil elements to stabilize sweet corn yields in various agroecosystems. Decisions made in the sowing period are very significant, as up to 30% of the obtained yield may depend on making the proper choice. Deviation from the optimum date (either early or late sowing) may decrease yield. When deciding about the sowing date of maize, one needs to consider climate, soil quality, geographical location, temperature, weed infestation, sowing seed quality, and the ripening time of the hybrid to be produced.

On Timor Island, East Nusa Tenggara, nixtamalization and pre-gelatinization were used as Bose Corn instantiation techniques

Abstract The province of East Nusa Tenggara is an archipelago province. Timor Island is one of the islands in the province of East Nusa Tenggara, where most of the population consumes corn as a staple food besides rice. The typical food on Timor Island, known since ancient times, is Bose Corn. The Bose corn cooking procedure is time-consuming and water-intensive. Timor Island is an area with dry land and a dry climate. Therefore, it is necessary to introduce technology that can shorten the cooking time by using less water. The instantiation process of bose corn using nixtamalization and pre-gelatinization methods is used as a technology to overcome this problem. The nixtamalization method using 0.4% (w/v) Ca(OH)2 in the steeping time treatment (0, 2, 4, 6, 8, and 10 hours) showed an increase in the color components (brightness, redness, and yellowness) in a raw and cooked instant bose corn. The grain yield of instant bose corn experienced a significant increase, namely 53.63% in the treatment without steeping time (0 hours) to 72.21% in the treatment of 10 hours steeping time. Water absorption increased to 352.36% at a steeping time of 6 hours but decreased to 320.17% at 10 hours of steeping time. Instantiation of bose corn using the pre-gelatinization method with different treatments of cooking time (60, 90, 120, 150, and 180 minutes) showed almost the same brightness value in raw and cooked instant bose corn. Differences in the color components redness and yellowness are indicated by the color towards yellowish red on raw instant bose corn and the color towards yellowish green on cooked instant bose corn. The grain yield of instant bose corn in large the pre-gelatinization method decreased significantly with the cooking time, namely 91.38% at 60 minutes to 75.23% at 180 minutes. The water absorption capacity of instant bose corn has increased but not significantly. It proves that the discoloration of instant bose corn in raw and cooked conditions using nixtamalization and pre-gelatinization methods was not significantly different. The grain yields produced from these two methods have no significant difference in water absorption. Thus, these two instantiation methods can be applied to the people of the island of Timor to make instant bose corn.

Effect of Adding NPK-Chitosan Fertilizer and Organic Matter on phosphorus and potassium content of Corn (Zea mays L.)

A field experiment was conducted at the College of Agriculture, University of Basrah's research station in Garmat Ali during 2022 agricultural season. The study aimed to investigate the impact of three types of fertilizers (commercial NPK as a control and two types of prepared NPK-Chitosan organic fertilizers) at four addition levels (0, 1, 1.5, and 2 ton ha-1) and two levels of organic matter (0% and 2.5%) on the availability of nitrogen and potassium in loam sandy soil and yield of corn (Zea mays L.). The results indicated a significant effect of fertilizer type on increasing Phosphorus and Potassium Content of corn plant. The highest values of Phosphorus and Potassium Content for corn were 6.15 and 47.07 gm kg-1 respectively for the added fertilizer type. The NPK-Chitosan fertilizer in a 1:3 coating ratio achieved the, 3.62 and 31.01 kg ha-1, respectively, followed by the treatment of NPK-Chitosan fertilizer with a 1:3 mixing ratio and a fertilizer level of 2 tons/ha, which yielded The results showed a significant effect of fertilizer level on increasing Phosphorus and Potassium Content of the corn plants. Phosphorus and Potassium Content increased with higher levels of added fertilizer. The level 2 achieved the highest values of Phosphorus and Potassium Content for the vegetative part corn plants, reaching 2 tons per hectare.4.22 and 37.91 gm kg-1, and with a significant difference from level 1 and 1.5 tons per hectare, level 2 also outperformed level 0 tons per hectare (the control treatment). Similarly, the organic matter showed a significant effect the results indicated a significant interaction between the type and level of fertilizer, as well as the level of added organic matter to the soil, on Phosphorus and Potassium Content of the yellow corn plants. The NPK-Chitosan fertilizer with a 1:3 coating ratio added at the level of 2 tons per hectare outperformed the other treatments, yielding the highest phosphour contentand grain yield.

Effect of irrigation scheduling on growth and yield of Rabi sweet corn (Zea mays saccharata L.)

Effect of irrigation scheduling on growth and yield of Rabi sweet corn (Zea mays saccharata L.)

Weed Biology and Ecology Studies: Diversity, Dominance and Prediction of Yield Loss of Corn (Zea mays L.) Due to Broadleaf Weeds Competition in Dryland

The aim of the research was to determine the diversity, distribution, dominance, and yield loss of corn due to competition from broadleaf weeds. Descriptive research method with direct survey techniques of research objects. Data were collected using 10 sample plots measuring 1 m2, which were placed using random sampling. Observation parameters include the population and dry biomass weight of weeds and corn. The data was analyzed descriptively and comparatively by calculating density, frequency, and dominance to obtain an important value index and summation diminance ratio (SDR). The results of the research showed that 14 species of broad-leaved weeds were found with high species diversity, distribution, dominance, and abundance indices. The competitiveness and relative weighted dominance of six weed species, namely Amaranthus spinusus, Amaranthus gracilis, Synedrella nodiliflora, Acalypha indica, Ageratum conyzoides, and Alternanthera philoxeroides, are higher than those of other weed species, causing corn yield losses of 5.56%–21.90%. Therefore, these six weed species must be controlled.

Effect of cover crop species and termination timing on corn growth and seedling disease

AbstractLater spring termination of fall‐planted cover crops can result in more biomass production, which has the potential to improve environmental benefits. However, later cover crop termination can also have the potential to harm cash crops, such as through increases in corn seedling disease. With the objective of better understanding such potential competing interactions, we evaluated the impact of early and late termination timing of two cover crops—cereal rye (Secale cereale L.) and hairy vetch (Vicia villosa Roth.)—on growth, seedling disease, and grain yield of corn in 2021 and 2022 in Nebraska. The total biomass production varied among treatments. Hairy vetch biomass was lower than cereal rye biomass at both termination times in 2021, but not different from cereal rye terminated early in 2022. Radicle root rot severity, a symptom of corn seedling disease, was not affected by cover crops in 2021. In 2022, however, radicle root rot was most severe in cereal rye terminated late treatments. Hairy vetch did not affect radicle root rot severity, regardless of termination time. Late termination of cereal rye resulted in a reduction in corn yield of 15.3% in 2021 and 76.1% in 2022 compared to no cover crop. Corn (Zea mays L.) grain yield was not affected following hairy vetch at either termination timing. Our results suggest that corn planted following late‐terminated cereal rye can increase the severity of root rot and decrease corn grain yield; however, planting green into hairy vetch can be a successful option to increase biomass without increasing corn seedling disease or decreasing corn yield.

Spectral Index-Based Estimation of Total Nitrogen in Forage Maize: A Comparative Analysis of Machine Learning Algorithms

Nitrogen plays a fundamental role as a nutrient for the growth of leaves and the process of photosynthesis, as it directly influences the quality and yield of corn. The importance of knowing the foliar nitrogen content through Machine Learning algorithms will help determine the efficient use of nitrogen fertilization in a context of sustainable agronomic management by avoiding Nitrogen loss and preventing it from becoming a pollutant for the soil and the atmosphere. The combination of machine learning algorithms with vegetation spectral indices is a new practice that helps estimate parameters of agricultural importance such as nitrogen. The objective of the present study was to compare random forest and neural network algorithms for estimating total plant nitrogen with spectral indices. Five spectral indices were obtained from remotely piloted aircraft systems and analyzed by mean, maximum and minimum from each sample plot to finally obtain 15 indices, and total nitrogen was estimated from the georeferenced points. The most important variables were selected with backward, forward and stepwise methods and total nitrogen estimates by laboratory were compared with random forest models and artificial neural networks. The most important indices were NDREmax and TCARImax. Using 15 spectral indices, total nitrogen with a variance of 79% and 81% with random forest and artificial neural network, respectively, was estimated. And only using NDREmax and TCARmax indices, 73% and 79% were explained by random forest and artificial neural network, respectively. It is concluded that it is possible to estimate nitrogen in forage maize with two indices and it is recommended to analyze by phenological stage and with a greater number of field data.