Maize Yield Response Research Articles

Simulating Maize Yield Response to Depletion of Available Soil Water and Nitrogen Management under Drip Irrigation with the FAO AquaCrop Model

Crop simulation models of different complexity have been developed for predicting the effects of soil, water and nutrients on growth and water productivity of different crops. These models are calibrated and validated for a given region using the data generated from field experiments. Therefore, AquaCrop model was calibrated and validated for grain maize (Single Cross 260) under varying irrigation and nitrogen levels. The experiment was conducted at the research farm of the agricultural college, Islamic Azad University, Shiraz during summer season 2011 and 2012. Irrigation treatments consisted of different levels of depletion of available soil water. The four levels of moisture depletions considered in the study were 20, 40, 60 and 80 percent. Nitrogen application levels were 150 (N1), 200 (N2), 250 (N3) and 300 kg ha–1. Root Mean Square error (RMSE), Prediction error (Pe), coefficient of determination (R2) and normalized root mean square error (RMSEn) were used to test the model performance. The model was calibrated for simulating maize grain and biomass yield for all treatment levels with the prediction error 4 < Pe < 5 per cent, 0.64 < R2 < 0.81 and 469 < RMSE < 786 t ha–1. Upon validation, Pe between 10 and 6; R2 between 0.65 and 0.76 and RMSE between 1062 and 1293 for grain and biomass yield, respectively. The highest and the lowest accuracy to predict yield and biomass under all nitrogen levels was obtained at I1 (MAD: 20%) and I4 (MAD: 80%) treatments, respectively. The results of the present study show that the AquaCrop model simulates aboveground biomass more accurately than grain yield. Also, model cannot provide satisfactory results under severe water stress conditions.

Analyzing adaptation strategies for maize production under future climate change in Guanzhong Plain, China

Agricultural adaptation is crucial for sustainable farming amid global climate change. By harnessing projected climate data and using crop modeling techniques, the future trends of food production can be predicted and better adaptation strategies can be assessed. The main objective of this study is to analyze the maize yield response to future climate projections in the Guanzhong Plain, China, by employing multiple crop models and determining the effects of irrigation and planting date adaptations. Five crop models (APSIM, AquaCrop, DSSAT, EPIC, and STICS) were used to simulate maize (Zea mays L.) yield under projected climate conditions during the 2030s, 2050s, and 2070s, based on the combination of 17 General Circulation Models (GCMs) and two Representative Concentration Pathways (RCPs 6.0 and 8.5). Simulated scenarios included elevated and constant CO2 levels under current adaptation (no change from current irrigation amount, planting date, and fertilizer rate), irrigation adaptation, planting date adaptation, and irrigation-planting date adaptations. Results from both maize-producing districts showed that current adaptation practices led to a decrease in the average yield of 19%, 27%, and 33% (relative to baseline yield) during the 2030s, 2050s, and 2070s, respectively. The future yield was projected to increase by 1.1–23.2%, 1.0–22.3%, and 2–31% under irrigation, delayed planting date, and double adaptation strategies, respectively. Adaptation strategies were found effective for increasing the future average yield. We conclude that maize yield in the Guanzhong Plain can be improved under future climate change conditions if irrigation and planting adaptation strategies are used in conjunction.

Determining Mehlich‐3 and DTPA extractable soil zinc optimum economic threshold for maize

AbstractMaize (Zea mays L.) is one of the most susceptible crops to zinc (Zn) deficiency. However, in much of the world, soil Zn tests are poorly calibrated, and thus determining a critical soil test, level for Zn is challenging. The objectives of this study were to: (i) produce a field calibration of the Zn Mehlich‐3 (M3‐Zn) method for predicting maize grain yield response to Zn fertilizer application, (ii) compare the capacity of DTPA extractable Zn (DTPA‐Zn) with M3‐Zn for predicting the response of maize yield to Zn fertilizer, (iii) determine whether inclusion of soil pH, organic matter (SOM) and extractable phosphorus in a M3‐Zn model improves its predictive capacity and (iv) evaluate an economic approach for determining soil Zn thresholds. We conducted 55 field experiments covering a wide range of edaphic and climatic conditions. Maize responded to Zn fertilizer in 29% of the trials. The capacity of M3‐Zn and DTPA‐Zn to predict relative yield of maize was similar. Inclusion of other soil variables (pH, extractable phosphorus and SOM) did not or only slightly improve the prediction of M3‐Zn. Based on the relationship between M3‐Zn and DTPA‐Zn (R2 = 0.89), one test can be predicted from the other without affecting the calibration. The M3‐Zn 'economic threshold' ranged from 0.98 to 2.79 mg kg−1, while for DTPA‐Zn it varied from 0.41 to 1.61 mg kg−1. The broad range of economic thresholds shows that differences in maize productivity and grain price between regions and seasons make establishing a single Zn threshold for all economic‐productive situations inappropriate.

An in‐depth examination of maize yield response to fertilizer in Central Malawi reveals low profits and too many weeds

AbstractWe examine the productivity of fertilizer used on maize in Central Malawi using field‐level panel data from over 1,200 observations on more than 500 fields over 4 harvest years. There are several novel aspects of this analysis compared to other on‐farm fertilizer efficiency studies, including (a) precise and accurate yield measurement using crop cuts, (b) estimating the impact of timely weeding, (c) the use of data on multiple soil characteristics from a panel of soil samples, and (d) the ability to control for field‐level fixed effects by tracking the same land over time. We find critical ecological and management threshold effects on fertilizer effectiveness at 0.94% soil carbon content, 57–58% sandiness, and weeding within 4 weeks of planting. Overall, we estimate lower yield response to fertilizer than reported in earlier studies: 2.6 maize kg/N kg under ideal circumstances, and statistically nil under many other conditions. We discuss the implications of our findings for farmers, policymakers, and researchers.

Bayesian approach for maize yield response to plant density from both agronomic and economic viewpoints in North America

Targeting the right agronomic optimum plant density (AOPD) for maize (Zea mays L.) is a critical management decision, but even more when the seed cost and grain selling price are accounted for, i.e. economic OPD (EOPD). From the perspective of improving those estimates, past studies have focused on utilizing a Frequentist (classical) approach for obtaining single-point estimates for the yield-density models. Alternative analysis models such as Bayesian computational methods can provide more reliable estimation for AOPD, EOPD and yield at those optimal densities and better quantify the scope of uncertainty and variability that may be in the data. Thus, the aims of this research were to (i) quantify AOPD, EOPD and yield at those plant densities, (ii) obtain and compare clusters of yield-density for different attainable yields and latitudes, and (iii) characterize their influence on EOPD variability under different economic scenarios, i.e. seed cost to corn price ratios. Maize hybrid by seeding rate trials were conducted in 24 US states from 2010 to 2019, in at least one county per state. This study identified common yield-density response curves as well as plant density and yield optimums for 460 site-years. Locations below 40.5 N latitude showed a positive relationship between AOPD and maximum yield, in parallel to the high potential level of productivity. At these latitudes, EOPD depended mostly on the maximum attainable yield. For the northern latitudes, EOPD was not only dependent on the attainable yield but on the cost:price ratio, with high ratios favoring reductions in EOPD at similar yields. A significant contribution from the Bayesian method was realizing that the variability of the estimators for AOPD is sometimes greater than the adjustment accounting for seed cost. Our results point at the differential response across latitudes and commercial relative maturity, as well as the significant uncertainty in the prediction of AOPD, relative to the economic value of the crop and the seed cost adjustments.

Response of maize and black gram yield and water productivity to variation in canopy temperature and crop water stress index

Response of maize and black gram yield and water productivity to variation in canopy temperature and crop water stress index

Deficit irrigation improves maize yield and water use efficiency in a semi-arid environment

Uncertainty in the availability of water supply pose challenges to traditional irrigation approaches. Regulating the amount and time of irrigation at different crop growth stages could provide a solution to optimize the irrigation water amid drought periods. This study evaluated the effect of different deficit irrigation levels on maize (Zea mays L.) at several growth phases over two growing seasons (2012 and 2013) in Yangling, Shaanxi province of China. Total nine irrigation treatments incorporated three irrigation amount ratios, i.e., control irrigation (CK, 100 % of crop evapotranspiration), and 80 % and 60 % of control irrigation; named as T2–T9. Among the irrigation treatments, grain yield ranged from 6392 to 9362 kg ha–1 and seasonal water use efficiency (WUE) varied from 20.3 to 34.9 kg ha–1 mm–1, whereas the irrigation water use efficiency (IWUE) ranged between 32.0 and 58.1 kg ha–1 mm–1. T2 that received 80 % irrigation between V8 and R6 growth stage had overall higher yield than CK, and this was closely followed by T4 that received 80 % irrigation at growth phase V3-V8 and V11-Tasseling, full irrigation at V8-V11, and 60 % irrigation between Tasseling and Maturity. Due to near optimum growing season temperature in 2013, larger WUE was noted in comparison to 2012, that resulted 16 % larger yield with 10 % lesser ETc, on an average, whereas 2012 growing season had better IWUE because of 37.5 % smaller irrigation consumption. Maize grain yield in response to water stress (Ky, the yield response factor) was 0.66, suggesting that the environmental conditions of the study area favor the application of deficit irrigation. The maize yield response to reduced irrigation supply in this experiment indicated that regulated deficit irrigation might help growers to cope with decline in water availability during growing season.

The response of maize, sorghum, and soybean yield to growing-phase climate revealed with machine learning

Accurate representation of crop responses to climate is critically important to understand impacts of climate change and variability in food systems. We use Random Forest (RF), a diagnostic machine learning tool, to explore the dependence of yield on climate and technology for maize, sorghum and soybean in the US plains. We analyze the period from 1980 to 2016 and use a panel of county yields and climate variables for the crop-specific developmental phases: establishment, critical window (yield potential definition) and grain filling. The RF models accounted for between 71% to 86% of the yield variance. Technology, evaluated through the time variable, accounted for approximately 20% of the yield variance and indicates that yields have steadily increased. Responses to climate confirm prior findings revealing threshold-like responses to high temperature (yield decrease sharply when maximum temperature exceed 29 °C and 30 °C for maize and soybean), and reveal a higher temperature tolerance for sorghum, whose yield decreases gradually as maximum temperature exceeds 32.5 °C. We found that sorghum and soybean responded positively to increases in cool minimum temperatures. Maize yield exhibited a unique and negative response to low atmospheric humidity during the critical phase that encompasses flowering, as well as a strong sensitivity to extreme temperature exposure. Using maize as a benchmark, we estimate that if warming continues unabated through the first half of the 21st century, the best climatic conditions for rainfed maize and soybean production may shift from Iowa and Illinois to Minnesota and the Dakotas with possible modulation by soil productivity.

Elevated atmospheric carbon dioxide effects on maize and alfalfa in the Northeast US: A comparison of model predictions and observed data

The main feed crops on dairy farms in the northeastern U.S. are alfalfa (Medicago sativa L.) and maize (Zea mays L.). Projected changes in climate along with increased atmospheric carbon dioxide (CO2) concentration will affect future production of these crops. We evaluated maize and alfalfa yield and evapotranspiration response to CO2 enrichment predicted by three process-based cropping system models, CropSyst, DSSAT and IFSM, in six counties of Pennsylvania and New York, using 25 years of daily weather and two concentrations of atmospheric CO2 (350 and 550 ppm). Positive effects of CO2 enrichment on crop growth have been documented using plants grown under controlled CO2 conditions; of particular relevance are results obtained in fields with free-air CO2 enrichment (FACE) technology. For evaluation, simulation results were compared among models and to FACE experimental data. The three models simulated similar maize response to increasing CO2 for grain yield, total biomass yield and harvest index, with predicted responses within the ranges reported in FACE experiments. The models may have slightly overestimated the yield response of maize during wet years compared with FACE experiments. The models also simulated increased productivity of alfalfa within the bounds observed in FACE experiments. While DSSAT and IFSM predicted yield increases of about 20%, Cropsyst predicted an increase of 32%, which is in the upper bound of FACE experimental data. For both crops, models simulated a lesser reduction in evapotranspiration under increasing CO2 than that measured in FACE experiments. The larger response of alfalfa may either shift rotations towards more alfalfa, or allow for a shorter alfalfa phase in a rotation depending on market and other production constraints. While there were overestimations in simulating maize productivity under no water stress and uncertainties in simulating evapotranspiration that are relevant for local and regional hydrology, these models adequately represented crop yield response to changes in atmospheric CO2 concentration.

Seasonal responses of maize growth and water use to elevated CO2 based on a coupled device with climate chamber and weighing lysimeters

The increase in atmosphere carbon dioxide (CO2) concentrations has been the most important environmental change experienced by agricultural systems. It is still uncertain whether grain yield of the global food crop of maize will remain unchanged under a future elevated CO2 (eCO2) environment. A coupled device with climate chamber and weighing lysimeters was developed to explore the water-related yield responses of maize to eCO2. Two experiments were conducted via this device under eCO2 (700 ppm) and current CO2 (400 ppm) concentrations. Seasonal changes in multiple growth indicators and related hydrological processes were compared between these two experiments. The results showed that the eCO2 effects were not significant on several indicators, i.e., the leaf carbon (C) content, nitrogen (N) content, chlorophyll content, C/N ratio, net photosynthesis rate, and leaf area index over the entire growing season (p > 0.05). Nevertheless, the transpiration rate (Tr) significantly reduced during the seedling to filling stages but notably increased at the maturity stage due to eCO2 (p < 0.05). Significant reduction in crop height (mean of 15.9%, p < 0.05) associated with notable increases in stem diameter (mean of 14.9%, p < 0.05) were found throughout the growing season. Dry matter per corncob at the final harvest decreased slightly under eCO2 (mean of 7.7 g, p > 0.05). Soil water storage was not significantly conserved by the decline of Tr except during the filling stage. Soil evaporation was likely promoted by eCO2 that the total evapotranspiration changed little (1.2%) over the entire growing season. Although the leaf water use efficiency increased significantly at every growth stage (mean of 27.3%, p < 0.05), the grain yield and water productivity were not improved noticeably by eCO2. This study could provide significant insight into predicting future crop yield and hydrological changes under climate change.

Response of Maize to Secondary Nutrients and Micronutrients in the Guinea Savanna of Nigeria

Background and Objective: In order to address the inadequacy of the current blanket fertilizer recommendation in the Guinea Savanna of Nigeria, the effect of other nutrients in addition to N, P and K in limiting maize productivity must be assessed. This study aimed to quantify the effects of the addition of secondary macronutrients and micronutrients on maize grain yield, nutrient uptake and N, P and K use efficiencies in the Guinea Savannah of Nigeria. Materials and Methods: The experiment consisted of 12 treatments: A control, an NPK treatment and 10 other treatments in which secondary macronutrient (Mg, S) and/or micronutrients (B, Zn) (SMNs) were added to the NPK. These were set across 4 locations with 3 replications. Results: Maize yield response to the addition of SMNs showed wide variation. The highest yield advantage over recommended NPK fertilizers was highest with the addition of Mg in Lere (2.4 t ha–1), S+B+Zn in Faskari (2.8 t ha–1), S+B in Doguwa (1.5 t ha–1) and S+Zn in Toro (2.4 t ha–1). The uptake, agronomic use efficiency, internal utilization efficiency and apparent recovery efficiency of N, P and K were significantly increased with the addition of SMNs but were not improved with Zn application beyond NPK alone. Conclusion: These results indicated that nutrient limitations to maize in the Guinea Savannah go beyond N, P and K. Therefore, S, Mg and B are needed to improve maize productivity and engender improved use efficiencies of NPK fertilizers.

Agronomic optimal plant density for yield improvement in the major maize regions of China

AbstractIdentifying the agronomic optimal plant density (AOPD) for maximum grain yield is essential to develop enhanced management strategies for maize (Zea mays L.) production. In this study, we used a boundary‐line method to examine the yield response of maize to plant density in China, based on the metadata (2783 data points) gathered from 157 recent studies during 2000–2017. We found the AOPD was 8.5 × 104 plants ha−1 in Northeast China, 9.3 × 104 plants ha−1 on the North China Plain, 10.5 × 104 plants ha−1 in Northwest China, and 8.0 × 104 plants ha−1 in Southwest China. Compared with current practices, potential grain yield with these AOPDs was improved by 13–20% for these major maize regions of China. Across regions, AOPDs were similar for spring‐planted (9.2 × 104 plants ha−1) and summer‐planted maize (9.3 × 104 plants ha−1) systems, although grain yield at the AOPD was 7% greater for the spring‐planted maize system. For different productivity environments, we found that high‐yielding plots obtained a yield of 18.2 Mg ha−1 at an AOPD of 11.6 × 104 plants ha−1, whereas in general productivity plots, 39% lower grain yield was achieved with a 19% lower AOPD (9.4 × 104 plants ha−1). In summary, this study illustrated AOPDs with modern maize hybrids for different productivity environments, cropping systems, and regions. Further study is needed to investigate the possible influence of agronomic factors such as lodging risks by increasing plant density.

Maize production and environmental costs: Resource evaluation and strategic land use planning for food security in northern Ghana by means of coupled emergy and data envelopment analysis

This paper applies an integrated methodology which is constituted of the following: (i) the Emergy-Data Envelopment Analysis (EM-DEA), (ii) environmental Cost-Benefit Analysis (CBA), (iii) Value Chain Analysis (VCA), and (iv) Sustainability Balanced Scorecard (SBSC) approaches, -to support multicriteria decision analysis (MCDA) for strategic agricultural land use planning, which could contribute to improve food security in northern Ghana. Five scenarios of land use and resource management practices for maize production were modelled. The business-as-usual scenario was based on primary data, which were collected using semi-structured questionnaires administered to 56 small-scale maize farmers through personal interviews. The dominant land use was characterised by an external input ≤12 kg/ha/yr inorganic fertilizer with/without the addition of manure in rainfed maize systems. The project scenarios were based on APSIM simulations of maize yield response to 0, 20, 50 and 100 kg/ha/yr urea dosages, with/without supplemental irrigation. The scenarios were dubbed as follows: (1) no/low input systems were denoted by Extensive0, Extensive12, and Intercrop20, and (2) moderate/high input systems were denoted by Intensive50, and Intensive100. The EM-DEA approach was used to assess the resource use efficiency (RUE) and sustainability in maize production systems, Ghana. The measured RUE and sustainability were used as a proxy for further analyses by applying the environmental CBA and VCA approaches to calculate: (a) the environmental costs of producing maize, i.e. resource use measured as total emergy (U), and (b) benefits from the yielded maize, i.e. (b i) food provision from grain measured in kcal/yr, and (b ii) potential electricity (bioenergy) which could be generated from residue measured in MWh/yr. The information which was derived from the applications of the EM-DEA, CBA and VCA approaches was aggregated by applying the SBSC approach to do a sustainability appraisal of the scenarios. The results show that, when labour and services are included in the assessment of RUE and sustainability, Intercrop20 and Intensive50 achieved greater marginal yield, better RUE, sustainability and appraisal score. The same scenarios caused lesser impacts in terms of expansion of area cultivated compared to Extensive0 and Extensive12. Meanwhile the impacts of Intercrop20 and Intensive50 in terms of ecotoxicity, emissions, and demand for resources (energy, materials, labour and services) were lesser compared to Intensive100. The implications of the various scenarios are discussed. The environmental performance of the scenarios are compared to maize production systems in other developing regions in order to put this study within a broader context. We conclude that, the EM-DEA approach is useful for assessing RUE and sustainability of agricultural production systems at farm and regional scales, as well as in connecting the management planning level and regional development considerations.

Maize (Zea mays L.) Growth and Yield Response to Tillage Methods and Fertilizer Combinations in the Midland Agro-ecological Zones of Kenya

The trials were set up in Busia, Embu and Kirinyaga Counties to assess the effect of tillage methods and application of different fertilizer combinations on maize productivity in the lower and upper midland agro-ecological zones of Kenya. Tillage methods were no-tillage (NT) with crop residue retention as mulch (+CR) (NT+CR) and conventional tillage (CT) without crop residue retention on farm surface (-CR) (CT-CR) while fertilizer combinations were NK, NP, PK, NPK, and NPK+CaMgZnBS. The N, P, K, Ca, Mg, Zn, B and S nutrients were applied at the rates of 120, 40, 40, 10, 10, 5 and 26.3 kg ha-1, respectively. The trials were laid in a randomized complete block design with a split-plot arrangement and replicated three times. The results showed that maize leaf area index, plant height, aboveground biomass, crop growth rate, and grain yield were significantly higher under CT-CR than under NT+CR in most of the sites. The CT-CR system out-yielded NT+CR system by 0.3 t ha-1 and 0.6 t ha-1 maize grain in Alupe and Kirinyaga, respectively. However, NT+CR system out-yielded CT-CR system by 0.4 t ha -1 maize grain at Embu. Across all the sites, application of PK and NPK+ZnBMgCaS fertilizer combinations resulted, respectively, in lowest and highest maize shoot biomass, leaf area indices, crop growth rates, plant heights, and grain yields. Based on this result alone, the potential of conservation agriculture in improving yields compared to conventional tillage could not be conclusive despite consistently recording higher soil moisture content across all sites and better yields in Embu. Again, application of a wide range of nutrients may be beneficial to maize production in the study areas as evidenced in the study. Therefore, we recommend multi-season and multi-location trials to comprehensively assess the impact of tillage methods and fertilizer management, particularly in relation to micronutrients.

A regional synthesis of seven-year maize yield responses to conservation agriculture technologies in Eastern and Southern Africa

In an effort to address the challenges of low crop productivity and food insecurity among Africa’s smallholder farmers, Conservation Agriculture (CA) is considered a promising strategy. This study synthesized and analyzed the maize yield merits of CA systems relative to conventional farmer practices and the contribution of agro-ecological zones, seasonal rainfall regimes and soil characteristics in five Eastern and Southern African (ESA) countries, namely Ethiopia, Kenya, Malawi, Mozambique and Tanzania, over a seven-year period. Agronomic data generated from 287 smallholder on-farm trials testing the productivity of CA cropping systems with a total of 4976 observations, were compiled and collated. Across the five countries, the factors country, cropping system, rainfall regime, agro-ecological zones and soil texture, accounted for 51, 20, 16, 10 and 3% of the yield variation, respectively. Furthermore, CA reduced yield variability by 11% (from 67% under conventional tillage to 56%). Across all the ESA environments and relative to conventional tillage with a mean of 2401 kg ha−1, CA-rotation had the highest maize yield increase (35%) compared with CA-sole (29%) and CA-intercrops (15 %) systems and thus offered the best opportunity for improved maize productivity compared with intercrops, CA-sole or conventional tilled systems. Furthermore, the highest returns (90–95%) to CA investments by smallholder farmers were realized under low rainfall conditions (<700 mm), thereby providing clear evidence of the climate smartness of CA systems under soil moisture stressed conditions. These CA yields relative to conventional till however were subdued to 26, 19 and 15% in medium-wet (700−1300 mm) and depressed even further to +1, -18 and +9% in wet (>1300 mm) environments, for CA-rotations, CA-sole and CA-intercrops, respectively. The study also showed that the most beneficial effects of CA occurred under loam textured soils. In addition, CA systems responded best (16%) on well drained soils yet depressed yields by 33% on poorly drained soils. We conclude that the prime conditions for CA performance relative to conventional tillage, involve use of CA-rotations, low rainfall (<700 mm) conditions, medium textured loam and well drained soils.

Growth and yield response of streak virus resistant maize (TZESR-W) to different nitrogen rates on a sandy loam soil of semi-arid region of Nigeria

Field experiments were conducted at Abubakar Tafawa Balewa University Research Farm, Bauchi during the 1992 and 1993 cropping seasons to investigate the “Growth and yield response of streak virus resistant maize (TZESR-W) to different N rates on a sandy loam soil of semi-arid region of Nigeria”. Experimental treatments consisted of seven rates of N (0, 50,100, 150, 200, 250 and 300 KgN/ha) and optimum levels of P (17.5 Kg/ha) and K (50 Kg/ha) applied as base rates. These were tested in a randomized complete block design with 3 replications. N was split applied by side dressing in two equal does: half at two weeks after sowing and the other half at 5 weeks after sowing. Experimental results revealed that the application of N fertilizer up to 100 KgN/ha significantly increased the maize plant height, number of cobs, cob length, cob girth, cob weight, grain yield, stover yield and percent N in grain and stover over the absolute control in both 1992 and 1993 cropping seasons. In other words, the application of N fertilizer beyond 100 KgN/ha was not significant when compared with the absolute control. On the other hand, shelling percentage, 1000-grain weight and N uptake by stover were not significantly affected by N fertilizer application. This study therefore suggested that maize (TZESR-W) could be grown on a sandy loam soil of semi-arid region of Nigeria with the application of 100 KgN/ha, 17.5 KgP/ha and 50 KgK/ha. Application of N fertilizer beyond 100 KgN/ha on TZESR-W is a waste of resources.

Growth and yield response of streak virus resistant maize (TZESR-W) to different nitrogen rates on a sandy loam soil of semi-arid region of Nigeria

Growth and yield response of streak virus resistant maize (TZESR-W) to different nitrogen rates on a sandy loam soil of semi-arid region of Nigeria

Maize yield in smallholder agriculture system-An approach integrating socio-economic and crop management factors.

Yield gaps of maize (Zea mays L.) in the smallholder farms of eastern India are outcomes of a complex interplay of climatic variations, soil fertility gradients, socio-economic factors, and differential management intensities. Several machine learning approaches were used in this study to investigate the relative influences of multiple biophysical, socio-economic, and crop management features in determining maize yield variability using several machine learning approaches. Soil fertility status was assessed in 180 farms and paired with the surveyed data on maize yield, socio-economic conditions, and agronomic management. The C&RT relative variable importance plot identified farm size, total labor, soil factors, seed rate, fertilizer, and organic manure as influential factors. Among the three approaches compared for classifying maize yield, the artificial neural network (ANN) yielded the least (25%) misclassification on validation samples. The random forest partial dependence plots revealed a positive association between farm size and maize productivity. Nonlinear support vector machine boundary analysis for the eight top important variables revealed complex interactions underpinning maize yield response. Notably, farm size and total labor synergistically increased maize yield. Future research integrating these algorithms with empirical crop growth models and crop simulation models for ex-ante yield estimations could result in further improvement.

Science-based decision support for formulating crop fertilizer recommendations in sub-Saharan Africa

In sub-Saharan Africa, there is considerable spatial and temporal variability in relations between nutrient application and crop yield, due to varying inherent soil nutrients supply, soil moisture, crop management and germplasm. This variability affects fertilizer use efficiency and crop productivity. Therefore, development of decision systems that support formulation and delivery of site-specific fertilizer recommendations is important for increased crop yield and environmental protection. Nutrient Expert (NE) is a computer-based decision support system, which enables extension advisers to generate field- or area-specific fertilizer recommendations based on yield response to fertilizer and nutrient use efficiency. We calibrated NE for major maize agroecological zones in Nigeria, Ethiopia and Tanzania, with data generated from 735 on-farm nutrient omission trials conducted between 2015 and 2017. Between 2016 and 2018, 368 NE performance trials were conducted across the three countries in which recommendations generated with NE were evaluated relative to soil-test based recommendations, the current blanket fertilizer recommendations and a control with no fertilizer applied. Although maize yield response to fertilizer differed with geographic location; on average, maize yield response to nitrogen (N), phosphorus (P) and potassium (K) were respectively 2.4, 1.6 and 0.2 t ha−1 in Nigeria, 2.3, 0.9 and 0.2 t ha−1 in Ethiopia, and 1.5, 0.8 and 0.2 t ha−1 in Tanzania. Secondary and micronutrients increased maize yield only in specific areas in each country. Agronomic use efficiencies of N were 18, 22 and 13 kg grain kg−1 N, on average, in Nigeria, Ethiopia and Tanzania, respectively. In Nigeria, NE recommended lower amounts of P by 9 and 11 kg ha−1 and K by 24 and 38 kg ha−1 than soil-test based and regional fertilizer recommendations, respectively. Yet maize yield (4 t ha−1) was similar among the three methods. Agronomic use efficiencies of P and K (300 and 250 kg kg−1, respectively) were higher with NE than with the blanket recommendation (150 and 70 kg kg−1). In Ethiopia, NE and soil-test based respectively recommended lower amounts of P by 8 and 19 kg ha−1 than the blanket recommendations, but maize yield (6 t ha−1) was similar among the three methods. Overall, fertilizer recommendations generated with NE maintained high maize yield, but at a lower fertilizer input cost than conventional methods. NE was effective as a simple and cost-effective decision support tool for fine-tuning fertilizer recommendations to farm-specific conditions and offers an alternative to soil testing, which is hardly available to most smallholder farmers.

Ứng dụng mô hình CERES-Maize mô phỏng năng suất bắp lai trồng trên đất phù sa ở Đồng bằng sông Cửu Long

The objectives of this study were to calibrate and validate the CERES-Maize model and simulate the response of maize growth and yield to different doses of nitrogen and organic fertilizers in alluvial soil. Field experiments were carried out from August 2015 to April 2016 at the Soil Science Department, Can Tho University to collect data for model calibration and validation. The calibrated and validated results showed that there were good agreements between simulated and observed data, indicating through the statistical parameters, i.e. yield (EF:0.85 – 0.99; RMSE:181.49 – 669.71 kg/ha; nRMSE: 3.87 – 12.13 %); above-ground biomass (EF:0.97 – 0.98; RMSE:672.91 – 942.80 kg/ha;, nRMSE: 8.01 – 10.39 %); leaf number (EF: 0.90 – 0.95; nRMSE: 7.81 – 12.04 %. Meanwhile, the simulated leaf area index was rather good agreement to the observed data, i.e. EF:0.69 – 0.82 và nRMSE: 15.65 – 20.47%. Maximum temperature and rainfall were the parameters having the highest sensitivity among the others.