Mean Yield Response Research Articles

A meta‐analysis of the effects of Striga control methods on maize, sorghum, and major millets production in sub‐Saharan Africa

AbstractParasitic Striga weeds severely damage cereal crops in sub‐Saharan Africa (SSA), leading to yield losses in susceptible varieties. A range of Striga control methods are commonly recommended, including cultural practices, chemical herbicides, biological control agents, and host resistance, either as solo treatments or in combinations of these approaches (i.e., integrated Striga management [ISM]). A limited number of studies compared the relative efficacy of the recommended Striga control methods, or their combinations for ISM, in cereal crop production in SSA. The objective of this paper was to undertake a meta‐analysis and provide a detailed comparison of the Striga control methods in the production of maize, sorghum, and the major millets, as a guide to effective Striga management. The study was conducted as a meta‐analysis of 66 research articles that reported on various control measures. The following agronomic data were collected: grain yield (GY) response of the assessed crops and Striga parameters such as damage rating score (SDR) and emergence count (SEC). Maize varieties possessing Striga‐resistant genes displayed high mean yield values at 2053.00 kg ha−1, varying from 281.00 to 6260.00 kg ha−1, and a mean SDR of 4.70, ranging from 2.00 to 7.00. Likewise, sorghum varieties with Striga resistance genes achieved greater GY with a mean yield response of 1738.00 kg ha−1, ranging from 850.00 to 2162.00 kg ha−1. A relatively low GY was achieved in maize and sorghum production when deploying ISM (e.g., cultural control + host resistance and host resistance + chemical herbicides) and chemical Striga control. Effective ISM and pre‐ and post‐emergent herbicides have not yet been identified for Striga control and yield gains. Striga damage negatively affected GY in maize, as revealed by the significant correlation (r = −0.36, P < 0.001) between GY and SDR. A relatively weak correlation was detected in maize between GY and SEC (r = 0.003, P = 0.96). Sorghum GY was negatively correlated with SEC, although nonsignificantly (r = −0.30, P = 0.36). Few studies have evaluated Striga control methods in pearl millet and finger millet, limiting the opportunity for an effective comparison. The study recommends SDR as the best selection criterion for improving GY performance in maize, while SEC and SDR are the parameters of choice in sorghum selection programs for better GY under Striga infestation. Overall, the meta‐analysis indicates that host resistance is the most effective method for controlling Striga infestation and boosting GY in maize and sorghum. There is an ongoing need for research into the best combinations of the reported control methods as a sound basis for the recommendation of an ISM package across target production environments of common cereals in Africa.

A yield comparison between small-plot and on-farm foliar fungicide trials in soybean and maize

Agronomic research provides management recommendations based on small-plot trials (SPTs) and on-farm trials (OFTs) with very different characteristics. SPTs are traditionally conducted at agricultural experiment stations by research institutes or universities, while OFTs are conducted under commercial-scale conditions and managed by farmers using their own equipment. Several researchers claimed that discrepancies could occur between these two types of trials, which can make the extrapolation of results from SPTs to the farm level difficult. In our study, we conducted an extensive comparison of small-plot and on-farm trials to analyze the effect of foliar fungicide application on maize and soybean yields. We collected data on maize and soybean from five US states. Analysis of the soybean data showed similar mean yield responses and within-trial standard deviation to fungicide application between 479 OFTs and 83 SPTs. For maize, our comparison of 300 OFTs and 114 SPTs showed similar mean yield response in both. Nevertheless, the within-trial standard deviation was three times smaller in on-farm compared to small-plot trials. On the other hand, the between-trial standard deviation (measuring the variability of the effects of fungicide application across different environments) was almost twice as large in SPTs than in OFTs for both crops. Hence, the differences in the effects of fungicide on yield were similar whether they were estimated using OFTs or SPTs for both crops. This implies that OFTs can potentially detect significant yield differences with fewer replicates and thus reduce the cost of data generation. We argue that SPTs can be seen as a preliminary step before scaling up to OFTs to facilitate technology transfer and extrapolate the results in real farming conditions.

Effect of Organic and Inorganic Fertilization on Soil Enzyme Activities in Relation to Yield and Proximate Composition of Colocasia ( Colocasia esculenta L) in Alfisols of Eastern India

ABSTRACT Field experiments were conducted for two kharif seasons during 2018–2020 to study the effect of inorganic fertilizers and organic manure on soil quality, yield, and proximate composition of colocasia (Colocasia esculenta L) in an Alfisol. Significantly highest mean cormel yield of colocasia (24.27 t ha−1) was recorded due to integrated application of 10.0 t ha−1 farmyard manure (FYM) combined with ½ NPK fertilizers. Application of 40, 80, and 120 kg ha−1 of potash (K2O) registered a mean yield response of 41, 56, and 63% over control, respectively. The biochemical constituents (starch, total sugars, and dry matter) in the cormels of colocasia were found to be higher (12.92, 1.26 and 24.67%, respectively) due to balanced application of 80–30-80 kg ha−1 of N, P2O5, and K2O at par with FYM+½NPK. Integrated application of FYM+½NPK has recorded higher total counts of fungi (57x104 cells g−1) and actinomycetes (53x105 cells g−1) and showed a significant relationship with yield (r = 0.90** and 0.87**, respectively). Higher soil enzyme activities with respect to dehydrogenase (DHA) (1.582 µg TPF hr−1 g−1), fluorescein diacetate hydrolysis assay (FDA) (2.356 µg g−1 hr−1), urease (303.2 µg NH4-N g−1 hr−1), acid phosphatase (69.36 µg PNP g−1 h−1), and alkaline phosphatase (49.76 µg PNP g−1 h−1) were observed due to conjoint application of FYM+½NPK. Fungi had a highly significant relationship with DHA, FDA, and urease activities (r = 0.87**, 0.85**, and 0.84**, respectively), whereas actinomycetes showed a significant relationship with acid and alkaline phosphatase activities (r = 0.88** and 0.93**, respectively). Applications of organic manure combined with ½NPK not only sustained the soil quality but also enhanced the productivity of colocasia.

Are soybean models ready for climate change food impact assessments?

An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO2 concentration [CO2] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO2]. For example, under a + 3 °C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 °C temperature rise to no effect on yield. Similarly, when increasing [CO2] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble mean yield responses to temperature and [CO2] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO2] and temperature is needed to reduce the uncertainty from the individual models.

Meta-Analysis of Yield Response to Applications of Fungicides Made at Different Crop Growth Stages in Michigan Winter Wheat

Various foliar fungal diseases, as well as Fusarium head blight, are a reoccurring threat to Michigan wheat production. Fungicides are a key management tool for these diseases, and many products are efficacious. However, the timing of application is thought to be a key determinant of yield response and disease prevention. A meta-analysis of data from previously conducted small-plot fungicide trials from Michigan was performed to evaluate which fungicide application timings individually or in combination resulted in the greatest yield response across multiple years. Data from 46 trials (2007 to 2020) were utilized to examine six fungicide regimes. All regimes analyzed resulted in a statistically significant yield response, and at least an 87% probability of a positive response in future applications. The combination treatment of a Feekes 5 to 7 plus a Feekes 10.5.1 application had the highest mean yield response of 10.5 bu./ac. increase relative to the mean of the nontreated control, and the Feekes 10.5.1 application alone resulted in 7.4 bu./ac. yield response. Early season applications (Feekes 5 to 7) resulted in the lowest response, with a mean yield increase of 4.1 bu./ac. Probabilities of positive yield response and prediction intervals were calculated for all regimes to aid growers in making future fungicide application decisions.

Meta-Analysis of Soybean Yield Response to Foliar Fungicides Evaluated from 2005 to 2018 in the United States and Canada

Random-effect meta-analyses were performed on data from 240 field trials conducted between 2005 and 2018 across nine U.S. states and Ontario, Canada, to quantify the yield response of soybean after application of foliar fungicides at beginning pod (R3) stage. Meta-analysis showed that the overall mean yield response when fungicide was used compared with not applying a fungicide was 2.7% (110 kg/ha). Moderator variables were also investigated and included fungicide group, growing season, planting date, and base yield, which all significantly influenced the yield response. There was also evidence that precipitation from the time of planting to the R3 growth stage influenced yield when fungicide was used (P = 0.059). Fungicides containing a premix of active ingredients from multiple groups (either two or three ingredients) increased the yield by 3.0% over not applying a fungicide. The highest and lowest yield responses were observed in 2005 and 2007, respectively. Better yield response to fungicides (a 3.0% increase) occurred when soybean crops were planted not later than 21 May and when total precipitation between planting and the R3 application date was above historic averages. Temperatures during the season did not influence the yield response. Yield response to fungicide was higher (a 4.7% increase) in average yield category (no spray control yield 2,878 to 3,758 kg/ha) and then gradually decreased with increasing base yield. Partial economic analyses indicated that use of foliar fungicides is less likely to be profitable when foliar diseases are absent or at low levels.

Going beyond mean effect size: Presenting prediction intervals for on-farm network trial analyses

Abstract The aim of on-farm research is to identify and test a new technology, product or management practice (e.g. more efficient seeding rate, enhanced row spacing, better disease management treatment, etc.) suited to local conditions by comparing it to a standard farmer practice across several farmers’ fields. Typically, each trial includes two treatments (new practice vs. standard control practice) replicated at least three times in each field. The statistical analysis of yield data collected in such trials provides growers with useful information about the effectiveness of the tested farming practice on crop productivity and its uncertainty. We used a random-effects model to i) estimate the performance of a treatment compared to a control in individual trials, ii) estimate the overall mean yield response across all trials, iii) compute prediction intervals describing a range of plausible yield response for a new (out-of-sample) field at the trial level, and iv) compute the probability that the tested management practice will be ineffective in a new field. We used frequentist (classical) and Bayesian approaches for data collected in 26 on-farm trial categories managed by the Iowa Soybean Association. Depending on the level of between-trial variability, we found that prediction intervals were 2.2–12.1 times larger than confidence intervals for the estimated mean yield responses for all tested management practices. We conclude that prediction intervals should be systematically reported to provide additional information about future trials or experiments with associated uncertainties. Nevertheless, prediction intervals should be interpreted with caution when the between-trial variance is small. Using prediction intervals and, when appropriate, the probability of ineffective treatment will prevent farmers from overoptimistic expectations that a significant effect at the overall population level will lead with high certainty to a yield gain on their own farms.

A Quantitative Synthesis of the Efficacy and Profitability of Conventional and Biological Fungicides for Botrytis Fruit Rot Management on Strawberry in Florida.

Botrytis fruit rot (BFR) is a major disease that affects strawberry production in Florida and worldwide. BFR management relies on frequent fungicide applications. A meta-analysis was conducted on the outcomes from nine field trials to evaluate the efficacy and profitability of conventional and biological fungicides compared with a nontreated control (NTC). All trials were conducted in Florida between the 2005/06 and 2016/17 growing seasons. Fungicide treatments were applied weekly, and plots were harvested twice a week for yield and BFR incidence quantification. Treatments were grouped into four categories: NTC, multisite only (Thiram), Standard (captan alternated with fludioxonil + cyprodinil), and Bacillus. Following primary analyses, a random effects network meta-analytical model was fitted to estimate the mean yield and BFR incidence responses for each treatment group and to compare means between pairs of groups. The Thiram and the Standard treatment groups increased yield by 378.8 and 502.2 kg/ha/week, respectively, compared with the NTC. The yield difference between Bacillus and NTC was not statistically significant. Besides increasing yield, Thiram and Standard also reduced BFR incidence by approximately 10% compared with the NTC. The mean yield responses and among-study variability from the meta-analysis were used to estimate the probability of a given yield response in a new future trial. The Standard and Thiram treatment groups showed higher estimated probabilities of increasing yield and resulting in a profitable return on application investments than the Bacillus group of treatments. The results from this study provide growers with information that will aid their decision-making process regarding BFR management.

Performance and Profitability of Fungicides for Managing Soybean White Mold: A 10-Year Summary of Cooperative Trials.

White mold, caused by Sclerotinia sclerotiorum, is a yield-limiting disease of soybean in Brazil. Uniform fungicide trials have been conducted annually since 2009. Data from 74 cooperative field trials conducted over a 10-year period were assembled. We selected five fungicides applied two times around flowering: dimoxystrobin plus boscalid (DIMO+BOSC), carbendazim plus procymidone (CARB+PROC), fluazinam (FLUZ), fluopyram (FLUO), and procymidone (PROC). For comparison, thiophanate-methyl (TMET) applied four times was also included as a low-cost treatment. Network models were fitted to the log of white mold incidence (percentages) and log of sclerotia mass data (grams/hectare) and to the nontransformed yield data (kilograms/hectare) for each treatment, including the untreated check. Back-transformation of the meta-analytic estimates indicated that the lowest and highest mean (95% confidence interval [CI]) percent reductions in incidence and sclerotia mass were 54.2 (49.3 to 58.7) and 51.6% (43.7 to 58.3) for TMET and 83.8 (79.1 to 87.5) and 87% (81.9 to 91.6) for CARB+PROC, respectively. The overall mean (95% CI) yield responses ranged from 323 kg/ha (247.4 to 400.3) for TMET to 626 kg/ha (521.7 to 731.7) for DIMO+BOSC, but the variance was significantly reduced by a binary variable (30% threshold) describing disease incidence in the untreated check. On average, an increment of 352 kg/ha was estimated for trials where the incidence was >30% compared with the low-disease scenario. Hence, the probability of breaking even on fungicide costs for the high-disease scenario was >65% for the more effective, but more expensive fungicide (FLUZ) than TMET. For the low-disease scenario, profitability was less likely and depended more on variations in fungicide cost and soybean price.

Yield Stability in Bread Wheat Germplasm across Drought Stress and Non‐Stress Conditions

Core Ideas Discriminant function analysis was suitable to select the most desirable drought tolerance indices. Residual maximum likelihood and additive main effects and multiplicative techniques were efficient methods in exploring the genotype × environment interaction. Promising lines were recommended for stress‐prone environments. Suitable screening techniques are required to select wheat (Triticum aestivum L.) lines exhibiting both high and stable yields in environments where drought stress is commonplace. Two recombinant inbred line (RIL) populations bred from crosses between a drought‐tolerant landrace Roshan and the cultivars Sabalan and Falat were evaluated in the field under both well‐watered and water‐stressed conditions. The drought stress was imposed by stopping irrigation at the flowering stage. Discriminant function analysis, based on a set of stress tolerance/sensitivity indices, was applied to identify the most desirable drought tolerance criterion. To quantify the size of the variation due to genotype × environment interaction (GEI), the grain yield over field trials was analyzed using the residual maximum likelihood (REML). The additive main effects and multiplicative interaction (AMMI) model was employed to determine the yield stability of the RILs. Finally, we screened a set of 10 drought‐tolerant lines with consistent performance across the test environments. The results of the AMMI and REMEL analysis showed that environment was the major source of variability (69.98%) followed by GEI (12%). The two AMMI biplots revealed that a set of three RILs yielded stably in all environments with the high mean yield response. These promising lines should be valuable as donors of favorable alleles to wheat improvement programs targeting stress‐prone environments.

Meta‐analysis of fungicide efficacy on soybean target spot and cost–benefit assessment

Target spot of soybean has spread in Brazil, the southeastern United States and Argentina in the last decade. A collaborative network of field Uniform Fungicide Trials (UFT) in Brazil was created in 2011 to study the target spot control efficacy of fungicides, including azoxystrobin + benzovindiflupyr (AZ_BF), carbendazim (CZM), fluxapyroxad + pyraclostrobin (FLUX_PYRA), epoxiconazole + FLUX_PYRA (EPO_FLUX_PYRA), mancozeb (MZB) and prothioconazole + trifloxystrobin (PROT_TRIF). Network meta‐analysis was used to conduct a quantitative synthesis of UFT data collected from 2012 to 2016 and to evaluate the effects of disease pressure (DP, low ≤ 35% target spot severity in the nontreated control < high) and year of experiment on the overall mean efficacy and yield response to each of the tested fungicides. Based on mean percentage control of target spot severity, the tested fungicides fall into three efficacy groups (EG): high EG, FLUX_PYRA (76.2% control relative to the nontreated control) and EPO_FLUX_PYRA (75.7% control); intermediate EG, PROT_TRIF (66.5% control) and low EG, MZB (49.6% control), AZ_BF (46.7% control) and CZM (32.4% control). DP had a significant effect on yield response. At DPLow, the highest response was due to PROT_TRIF (+342 kg ha−1, +12.8%) and EPO_FLUX_PYRA (+295.5 kg ha−1, +11.2%), whereas at DPHigh, EPO_FLUX_PYRA and FLUX_PYRA outperformed the other treatments, with yield responses of 503 kg ha−1 (+20.2%) and 469 kg ha−1 (+19.1%), respectively. The probability of a positive return on fungicide investment ranged from 0.26 to 0.56 at DPLow and from 0.34 to 0.66 at DPHigh.

Effect of Mycorrhiza, Organic Sources, Lime, Secondary and Micro-nutrients on Soil Microbial Activities and Yield Performance of Yam Bean (Pachyrhizus erosus L.) in Alfisols

ABSTRACTEffect of integrated use of mycorrhiza, lime, inorganic fertilizers, and organic manures on microbial activities and yield performance of yam bean (Pachyrhizus erosus L.) was studied for two consecutive kharif (rainy) seasons during 2013–14 and 2014–15 in an acid Alfisol. The experiment was laid out with 16 treatments consisting of graded doses of soil test–based nitrogen, phosphors, and potassium (NPK); lime; mycorrhiza; organic sources, that is, farmyard manure (FYM), vermicompost, and green manure; secondary magnesium sulfate (MgSO4) and micronutrients zinc sulfate (ZnSO4 and borax). Significantly highest mean tuber yield (29.61 t ha−1) was recorded due to integrated application of lime + FYM + NPK + ZnSO4. Graded doses of NPK showed a mean yield response of 65%, 134%, and 191% due to addition of 50%, 100%, and 150% of NPK over control, respectively. Inoculation of vesicular–arbuscular mycorrhiza (VAM) combined with NPK and FYM recorded a mean tuber yield of 25.14 t ha−1. Highest mean dry matter (18.85%) was recorded due to application of 150% NPK, whereas highest starch content on fresh weight basis was recorded due to integrated use of lime + FYM + NPK + MgSO4 (11.11%). Application of 150% NPK has recorded highest dehydrogenase activity (2.018 µg TPF h−1 g−1) and fluorescein diacetate hydrolysis assay (2.012 µg g−1 h−1). Fungal inoculation of VAM in combination with lime + FYM + NPK recorded highest acid and alkaline phosphatase activities (82.20 and 67.02 µg PNP g−1 soil h−1, respectively). Soil biological activities and phosphatase activities had highly significant relationship with tuber yield and biochemical constituents of yam bean. The study emphasized the conjunctive use of soil test–based inorganic fertilizers, lime, and organic manures to enhance the enzymatic activities and to realize higher crop yields of yam bean in acid Alfisols.

Revisiting Fungicide-Based Management Guidelines for Leaf Blotch Diseases in Soft Red Winter Wheat

Standard foliar fungicide applications in wheat are usually made between flag leaf emergence (Feekes [FK] 8) and heading (FK10.5) to minimize damage to the flag leaf. However, over the last few years, new fungicide programs such as applications prior to FK8 and split half-rate applications have been implemented, although there are few data pertaining to the efficacy of these programs. Eight experiments were conducted in Illinois, Indiana, Ohio, and Wisconsin from 2010 to 2012 to compare new programs to standard FK8 and FK10 programs in terms of disease control and yield response. The programs evaluated consisted of single full-rate applications of 19% tebuconazole + 19% prothioconazole (Prosaro) or 23.6% pyraclostrobin (Headline) at FK5 (pseudostem strongly erected), FK8, or FK10, or split half rates at FK5 and 8 (FK5+8), plus an untreated check (CK). Leaf blotch (LB) severity and yield data were collected and random effects meta-analytical models fitted to estimate the overall log odds ratio of disease reaching the flag leaf ( ) and mean yield increase ( ) for each fungicide program relative to CK. For all programs, was significantly different from zero (P < 0.05). Based on estimated odds ratios (OR = exp[ ]), the two FK8 programs reduced the risk of LB reaching the flag leaf by 55 and 75%, compared with 62 and 69% and 67 and 70% for the two FK10 and FK5+8 programs, respectively, and only 32 and 37% for the two FK5 programs. was significantly different from zero (P ≤ 0.003) for all FK8, FK10, and FK5+8 programs, with values of 233 and 245, 175 and 220, and 175 and 187 kg ha-1 for the FK10, FK5+8, and FK8 programs, respectively. Differences in mean yield response between Headline and Prosaro were not statistically significant (P > 0.05). The probability of profitability was estimated for each program for a range of grain prices and fungicide application costs. All FK8, FK10, and FK5+8 programs had more than an 80% chance of resulting in a positive yield response, compared with 63 and 67% for the two FK5 programs. The chance of obtaining a yield increase of 200 kg ha-1, required to offset an application cost of $36 ha-1 at a grain price of $0.18 kg-1, ranged from 44 to 60% for FK8, FK10 and FK5+8 programs compared with 22 and 25% for the two FK5 programs. These findings could be used to help inform fungicide application decisions for LB diseases in soft red winter wheat.

Timing of Nitrogen Fertilizer Application for Annual Ryegrass Overseeded into Unimproved Perennial Warm‐season Pasture

Successful production of herbage by cool‐season forage grasses in the southern Plains is heavily dependent on a sufficient supply of available nitrogen (N), and appropriate scheduling of N application is an important component of cost‐effective fertilizer use. The effects of different combinations of fall, early‐spring, and late‐spring increments of 22.3 lb N/acre on herbage dry matter (DM) and N yields of annual (Italian) ryegrass (Lolium multiflorum Lam.) were tested over 3 yr. Fall N application had minimal effect on ryegrass establishment, measured in plant or tiller counts in the following spring. Application of N in fall did not produce harvestable herbage DM in fall and provided significant increase in DM yield in the following spring in only one year out of three. Early‐spring N application produced significant yield increase in two out of three years and provided a mean yield response of 17.2 lb DM/lb N applied. Application of N for regrowth after initial ryegrass harvest in early May produced a mean yield increment of 6.7 lb DM/lb N applied. There was no residual effect of fall or spring N application on warm‐season grass production. Limited N supply (&lt;89 lb N/acre) is likely to be most efficiently used when applied at the beginning of the spring growing season.

Break crops and rotations for wheat

Wheat crops usually yield more when grown after another species than when grown after wheat. Quantifying the yield increase and explaining the factors that affect the increase will assist farmers to decide on crop sequences. This review quantifies the yield increase, based on &gt;900 comparisons of wheat growing after a break crop with wheat after wheat. The mean increase in wheat yield varied with species of break crop, ranging from 0.5 t ha–1 after oats to 1.2 t ha–1 after grain legumes. Based on overlapping experiments, the observed ranking of break-crop species in terms of mean yield response of the following wheat crop was: oats &lt; canola ≈ mustard ≈ flax &lt; field peas ≈ faba beans ≈ chickpeas ≈ lentils ≈ lupins. The mean additional wheat yield after oats or oilseed break crops was independent of the yield level of the following wheat crop. The wheat yield response to legume break crops was not clearly independent of yield level and was relatively greater at high yields. The yield of wheat after two successive break crops was 0.1–0.3 t ha–1 greater than after a single break crop. The additional yield of a second wheat crop after a single break crop ranged from 20% of the effect on a first wheat crop after canola, to 60% after legumes. The mean yield effect on a third wheat crop was negligible, except in persistently dry conditions. The variability of the break-crop effect on the yield of a second wheat crop was larger than of a first wheat crop, particularly following canola. We discuss the responses in relation to mechanisms by which break crops affect soil and following crops. By quantifying the magnitude and persistence of break-crop effects, we aim to provide a basis for the decision to grow continuous cereal crops, strategic rotations or tactically selected break crops. In many wheat-growing areas, the large potential yield increases due to break crops are not fully exploited. Research into quantifying the net benefits of break crops, determining the situations where the benefits are greatest, and improving the benefits of break crops promises to improve the efficiency of wheat-based cropping systems.

Developing and normalizing average corn crop water production functions across years and locations using a system model

Crop water production functions (CWPFs) are often expressed as crop yield vs. consumptive water use or irrigation water applied. CWPFs are helpful for optimizing management of limited water resources, but are site-specific and vary from year to year, especially when yield is expressed as a function of irrigation water applied. Designing limited irrigation practices requires deriving CWPFs from long-term field data to account for variation in precipitation and other climatic variables at a location. However, long-term field experimental data are seldom available. We developed location-specific (soil and climate) long-term averaged CWPFs for corn (Zea mays L.) using the Root Zone Water Quality Model (RZWQM2) and 20 years (1992–2011) of historical weather data from three counties of Colorado. Mean CWPFs as functions of crop evapotranspiration (ET), ET due to irrigation (ETa–d), irrigation (I), and plant water supply (PWS=effective rainfall+plant available water in the soil profile at planting+applied irrigation) were developed for three soil types at each location. Normalization of the developed CWPF across soils and climates was also developed. A Cobb–Douglas type response function was used to explain the mean yield responses to applied irrigations and extend the CWPFs for drip, sprinkler and surface irrigation methods, respectively, assuming irrigation application efficiencies of 95, 85 and 55%, respectively. The CWPFs developed for corn, and other crops, are being used in an optimizer program for decision support in limited irrigation water management in Colorado.

Determination of plant traits that affect genotype × location (G×L) interaction in peanut using the CSM-CROPGRO-Peanut model

Genotype × environment (G×E) interaction complicates the identification of superior genotypes. An understanding its causes is needed for a more effective breeding strategy. The objective of this study was to determine the plant traits that cause genotype × location (G×L) interaction for pod yield in peanut using a modeling approach. The CSM-CROPGRO-Peanut model was used to simulate pod yield for 17 peanut genotypes for 14 locations representative of all peanut production areas in Thailand using 30 years of historical weather data. Sensitivity analysis was used to determine the effects of individual and combinations of plant traits on pod yield and yield response to environments by varying the value of one or more cultivar coefficients and then evaluating their effects. The results showed that the cultivar coefficients that showed major effects were the duration from first seed to physiological maturity (SDPM), maximum leaf photosynthesis rate (LFMAX), the maximum fraction of daily growth that is partitioned to seed and shell (XFRT), single seed filling duration (SFDUR) and the duration of pod addition (PODUR). Those having minor effects were the duration from emergence to first flower (EMFL), maximum leaf size (SIZLF) and maximum seed weight (WTPSD). The cultivar coefficients that caused the differences in both mean yield and yield response to locations between peanut genotypes in different pairs included LFMAX, XFRT, SDPM, SFDUR and PODUR, but the causal characters differed among pairs of genotypes. It was concluded that changing the degree of genotypic response to environments is possible through selection for a combination of some of these traits, and that model simulation could be used to identify those traits.

Establishing a scientific basis for fertilizer recommendations for wheat in China: Yield response and agronomic efficiency

The inappropriate application of fertilizer has become a common phenomenon in wheat production systems in China and has led to nutrient imbalances, inefficient use and large losses to the environment. However, defining an appropriate fertilization rate remains the foundation to science-based nutrient management. This paper described a new fertilizer recommendation method for wheat in China based on yield response and agronomic efficiency using datasets from 2000 to 2011. The results showed that the mean yield responses of wheat to N, P and K were 1.7, 1.0 and 0.8t/ha, respectively. Nitrogen was the nutrient most limiting yield, followed by P and then K. The soil indigenous nutrient supplies were 122.6kg N/ha, 38.0kg P/ha, and 120.2kg K/ha. The mean agronomic efficiencies were 9.4, 10.2 and 6.5kg/kg for N, P and K, respectively. There was a significant negative exponential relationship between yield response and indigenous nutrient supply, and a significant negative linear correlation between yield response and relative yield. It was also demonstrated a quadratic equation between yield response (x) and agronomic efficiency (y) (P<0.05). The relationship between yield response (x) and agronomic efficiency (y) for N was yN=0.3729xN2+6.1333xN+0.1438 (R2=0.76, n=601), for P was yP=0.5013xP2+8.3209xP+2.3907 (R2=0.65, n=288), and for K was yK=1.6581xK2+9.099xK+0.7668 (R2=0.58, n=379). These equations were all incorporated as part of the Nutrient Expert for Wheat fertilizer recommendation decision support system. The results of multiple field experiments helped to validate the feasibility of the recommendation model and concluded that Nutrient Expert for Wheat could be used as an alternative method to make fertilizer recommendations in China.

Meta-Analysis of Yield Response of Hybrid Field Corn to Foliar Fungicides in the U.S. Corn Belt

The use of foliar fungicides on field corn has increased greatly over the past 5 years in the United States in an attempt to increase yields, despite limited evidence that use of the fungicides is consistently profitable. To assess the value of using fungicides in grain corn production, random-effects meta-analyses were performed on results from foliar fungicide experiments conducted during 2002 to 2009 in 14 states across the United States to determine the mean yield response to the fungicides azoxystrobin, pyraclostrobin, propiconazole + trifloxystrobin, and propiconazole + azoxystrobin. For all fungicides, the yield difference between treated and nontreated plots was highly variable among studies. All four fungicides resulted in a significant mean yield increase relative to the nontreated plots (P < 0.05). Mean yield difference was highest for propiconazole + trifloxystrobin (390 kg/ha), followed by propiconazole + azoxystrobin (331 kg/ha) and pyraclostrobin (256 kg/ha), and lowest for azoxystrobin (230 kg/ha). Baseline yield (mean yield in the nontreated plots) had a significant effect on yield for propiconazole + azoxystrobin (P < 0.05), whereas baseline foliar disease severity (mean severity in the nontreated plots) significantly affected the yield response to pyraclostrobin, propiconazole + trifloxystrobin, and propiconazole + azoxystrobin but not to azoxystrobin. Mean yield difference was generally higher in the lowest yield and higher disease severity categories than in the highest yield and lower disease categories. The probability of failing to recover the fungicide application cost (p(loss)) also was estimated for a range of grain corn prices and application costs. At the 10-year average corn grain price of $0.12/kg ($2.97/bushel) and application costs of $40 to 95/ha, p(loss) for disease severity <5% was 0.55 to 0.98 for pyraclostrobin, 0.62 to 0.93 for propiconazole + trifloxystrobin, 0.58 to 0.89 for propiconazole + azoxystrobin, and 0.91 to 0.99 for azoxystrobin. When disease severity was >5%, the corresponding probabilities were 0.36 to 95, 0.25 to 0.69, 0.25 to 0.64, and 0.37 to 0.98 for the four fungicides. In conclusion, the high p(loss) values found in most scenarios suggest that the use of these foliar fungicides is unlikely to be profitable when foliar disease severity is low and yield expectation is high.

Meta-Analysis of the Effects of Triazole-Based Fungicides on Wheat Yield and Test Weight as Influenced by Fusarium Head Blight Intensity

ABSTRACT Multivariate random-effects meta-analyses were conducted on 12 years of data from 14 U.S. states to determine the mean yield and test-weight responses of wheat to treatment with propiconazole, prothioconazole, tebuconazole, metconazole, and prothioconazole+tebuconazole. All fungicides led to a significant increase in mean yield and test weight relative to the check (D; P < 0.001). Metconazole resulted in the highest overall yield increase, with a D of 450 kg/ha, followed by prothioconazole+ tebuconazole (444.5 kg/ha), prothioconazole (419.1 kg/ha), tebuconazole (272.6 kg/ha), and propiconazole (199.6 kg/ha). Metconazole, prothioconazole+tebuconazole, and prothioconazole also resulted in the highest increases in test weight, with D values of 17.4 to 19.4 kg/m(3), respectively. On a relative scale, the best three fungicides resulted in an overall 13.8 to 15.0% increase in yield but only a 2.5 to 2.8% increase in test weight. Except for prothioconazole+tebuconazole, wheat type significantly affected the yield response to treatment; depending on the fungicide, D was 110.0 to 163.7 kg/ha higher in spring than in soft-red winter wheat. Fusarium head blight (FHB) disease index (field or plot-level severity) in the untreated check plots, a measure of the risk of disease development in a study, had a significant effect on the yield response to treatment, in that D increased with increasing FHB index. The probability was estimated that fungicide treatment in a randomly selected study will result in a positive yield increase (p(+)) and increases of at least 250 and 500 kg/ha (p(250) and p(500), respectively). For the three most effective fungicide treatments (metconazole, prothioconazole+tebuconazole, and prothioconazole) at the higher selected FHB index, p(+) was very large (e.g., >/=0.99 for both wheat types) but p(500) was considerably lower (e.g., 0.78 to 0.92 for spring and 0.54 to 0.68 for soft-red winter wheat); at the lower FHB index, p(500) for the same three fungicides was 0.34 to 0.36 for spring and only 0.09 to 0.23 for soft-red winter wheat.