Explain Yield Variation Research Articles

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

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

Using copula graphical models to detect the impact of drought stress on maize and wheat yield

Abstract Improving crop yields is one of the main goals of agronomy. However, yield is determined by a complex interplay between Genotypic, Environmental and Management factors (G$\times$E$\times$M), which varies across time and space. Therefore, identifying the fundamental relations underlying yield variation is a principal aim of agricultural research. A narrow, and not necessarily appropriate, set of statistical methods tends to be used in the study of such relations, which is why we aim to introduce a diverse audience of agronomists, production ecologists, plant breeders and others interested in explaining yield variation to the use of graphical models. More specifically, we wish to demonstrate the usefulness of copula graphical models for heterogeneous mixed data. This new statistical learning technique provides a graphical representation of conditional independence relationships within data that is not necessarily normally distributed and consists of multiple groups for environments, management decisions, genotypes or abiotic stresses such as drought. This article introduces some basic graphical model terminology and theory, followed by an application on Ethiopian maize and wheat yield undergoing drought stress. The proposed method is accompanied with the R package heteromixgm https://CRAN.R-project.org/package=heteromixgm.

Yield response of iceberg lettuce (Lactuca sativa Capitata group) to phosphorus fertilisation in a boreal soil

Cold soils and a short boreal growing season are assumed to necessitate high soil phosphorus (P) status and ample P applications to vegetable crops. Yet, a previous Finnish study indicated lower than anticipated yield responses of onion and cabbage. Here we report 2-year P trials with iceberg lettuce (cv. Skindel) transplanted in an open field in spring and midsummer on a clay soil with moderately low P status. During the spring plantings, P concentration in soil solution was followed using Plant Root Simulator (PRS) probes in P0 (0 kg P ha-1) and P60 (60 kg P ha-1) treatments. The PRS probes indicated initially 5 to 7-fold higher soil solution P concentration in the P60 treatment compared to P0 due to fertilisation, but thereafter P concentrations equalised. For spring plantings, P applications did not explain yield variation, giving statistically non-significant, maximum 14% higher yields over P0. In summer plantings, about 30% of the yield variation was explained by P applications, and the P60 rate gave 20–35% higher yields over P0. Mitscherlich type model integrating all data predicted a maximum 20% yield, 10% of the variation being accounted for. The model suggested that 32 kg P ha-1 brings 97% of the maximum yield, whilst the Finnish P fertilisation regulation allows 60 kg ha-1 for the given soil. No correlation between P applications and P concentrations of lettuce leaves was found. The results stress the need for empirical evaluation of P requirements of vegetable crops to avoid unnecessary P applications.

Can we harness digital technologies and physiology to hasten genetic gain in US maize breeding?

Plant physiology can offer invaluable insights to accelerate genetic gain. However, translating physiological understanding into breeding decisions has been an ongoing and complex endeavor. Here we demonstrate an approach to leverage physiology and genomics to hasten crop improvement. A half-diallel maize (Zea mays) experiment resulting from crossing 9 elite inbreds was conducted at 17 locations in the USA corn belt and 6 locations at managed stress environments between 2017 and 2019 covering a range of water environments from 377 to 760 mm of evapotranspiration and family mean yields from 542 to 1,874 g m-2. Results from analyses of 35 families and 2,367 hybrids using crop growth models linked to whole-genome prediction (CGM-WGP) demonstrated that CGM-WGP offered a predictive accuracy advantage compared to BayesA for untested genotypes evaluated in untested environments (r = 0.43 versus r = 0.27). In contrast to WGP, CGMs can deal effectively with time-dependent interactions between a physiological process and the environment. To facilitate the selection/identification of traits for modeling yield, an algorithmic approach was introduced. The method was able to identify 4 out of 12 candidate traits known to explain yield variation in maize. The estimation of allelic and physiological values for each genotype using the CGM created in silico phenotypes (e.g. root elongation) and physiological hypotheses that could be tested within the breeding program in an iterative manner. Overall, the approach and results suggest a promising future to fully harness digital technologies, gap analysis, and physiological knowledge to hasten genetic gain by improving predictive skill and definition of breeding goals.

Within-Field Yield Prediction in Cereal Crops Using LiDAR-Derived Topographic Attributes with Geographically Weighted Regression Models

Accurate yield estimation and optimized agricultural management is a key goal in precision agriculture, while depending on many different production attributes, such as soil properties, fertilizer and irrigation management, the weather, and topography.The need for timely and accurate sensing of these inputs at the within field-scale has led to increased adoption of very high-resolution remote and proximal sensing technologies. With regard to topography attributes, greater attention is currently being devoted to LiDAR datasets (Light Detection and Ranging), mainly because numerous topographic variables can be derived at very high spatial resolution from these datasets. The current study uses LiDAR elevation data from agricultural land in southern Ontario, Canada to derive several topographic attributes such as slope, and topographic wetness index, which were then correlated to seven years of crop yield data. The effectiveness of each topographic derivative was independently tested using a moving-window correlation technique. Finally, the correlated derivatives were selected as explanatory variables for geographically weighted regression (GWR) models. The global coefficient of determination values (determined from an average of all the local relationships) were found to be R2 = 0.80 for corn, R2 = 0.73 for wheat, R2 = 0.71 for soybeans and R2 = 0.75 for the average of all crops. These results indicate that GWR models using topographic variables derived from LiDAR can effectively explain yield variation of several crop types on an entire-field scale.

Assessing approaches for stratifying producer fields based on biophysical attributes for regional yield-gap analysis

Large databases containing producer field-level yield and management records can be used to identify causes of yield gaps. A relevant question is how to account for the diverse biophysical background (i.e., climate and soil) across fields and years, which can confound the effect of a given management practice on yield. Here we evaluated two approaches to group producer fields based on biophysical attributes: (i) a technology extrapolation domain spatial framework (‘TEDs’) that delineates regions with similar (long-term average) annual weather and soil water storage capacity and (ii) clusters based on field-specific soil properties and weather during each crop phase in each year. As a case study, we used yield and management data collected from 3462 rainfed fields sown with soybean across the North Central US (NC-US) during four growing seasons (2014–2017). Following the TED approach, fields were grouped into 18 TEDs based on the TED that corresponded to the geographic location of each field. In the cluster approach, fields were grouped into clusters based on similarity of in-season weather and soil. To evaluate how the number of clusters would affect the results, fields were grouped separately into 5, 10, 18, and 30 clusters. The two stratification approaches (TEDs and clusters) were compared on their ability to explain the observed yield variation and yield response to key management factors (sowing date and foliar fungicide and/or insecticide). Lack of stratification of producer fields based on their biophysical background ignored management by environment (M × E) interactions, leading to spurious relationships and results that are not relevant at local level. In the case of the cluster approach, a fine stratification (18 and 30 clusters) explained a larger portion of the yield variance compared with a coarse stratification (5 and 10 clusters). However, for our case study in the NC-US region, we did not find strong evidence that the data-rich clustering approach outperformed the TEDs on the ability to explain yield variation and identify M × E interactions. Only the stratification into 30 clusters exhibited a small improved ability at explaining yield variation compared with the TEDs. However, the use of the clustering approach had important trade-offs, including large amount of data requirements and difficulties to scale results to different regions and over time. The choice of the stratification method should be based on objectives, data availability, and expected variation in yield due to erratic weather across regions and years.

Not so robust: Robusta coffee production is highly sensitive to temperature.

Coffea canephora (robusta coffee) is the most heat-tolerant and 'robust' coffee species and therefore considered more resistant to climate change than other types of coffee production. However, the optimum production range of robusta has never been quantified, with current estimates of its optimal mean annual temperature range (22-30°C) based solely on the climatic conditions of its native range in the Congo basin, Central Africa. Using 10years of yield observations from 798 farms across South East Asia coupled with high-resolution precipitation and temperature data, we used hierarchical Bayesian modeling to quantify robusta's optimal temperature range for production. Our climate-based models explained yield variation well across the study area with a cross-validated mean R2 =.51. We demonstrate that robusta has an optimal temperature below 20.5°C (or a mean minimum/maximum of ≤16.2/24.1°C), which is markedly lower, by 1.5-9°C than current estimates. In the middle of robusta's currently assumed optimal range (mean annual temperatures over 25.1°C), coffee yields are 50% lower compared to the optimal mean of ≤20.5°C found here. During the growing season, every 1°C increase in mean minimum/maximum temperatures above 16.2/24.1°C corresponded to yield declines of ~14% or 350-460kg/ha (95% credible interval). Our results suggest that robusta coffee is far more sensitive to temperature than previously thought. Current assessments, based on robusta having an optimal temperature range over 22°C, are likely overestimating its suitable production range and its ability to contribute to coffee production as temperatures increase under climate change. Robusta supplies 40% of the world's coffee, but its production potential could decline considerably as temperatures increase under climate change, jeopardizing a multi-billion dollar coffee industry and the livelihoods of millions of farmers.

Agroclimatology and Wheat Production: Coping with Climate Change.

Cereal production around the world is critical to the food supply for the human population. Crop productivity is primarily determined by a combination of temperature and precipitation because temperatures have to be in the range for plant growth and precipitation has to supply crop water requirements for a given environment. The question is often asked about the changes in productivity and what we can expect in the future and we evaluated the causes for variation in historical annual statewide wheat grain yields in Oklahoma, Kansas, and North Dakota across the Great Plains of United States. Wheat (Triticum aestivum L.) is adapted to this area and we focused on production in these states from 1950 to 2016. This analysis used a framework for annual yields using yield gaps between attainable and actual yields and found the primary cause of the variation among years were attributable to inadequate precipitation during the grain-filling period. In Oklahoma, wheat yields were reduced when April and May precipitation was limited (r2 = 0.70), while in Kansas, May precipitation was the dominant factor (r2 = 0.78), and in North Dakota June–July precipitation was the factor explaining yield variation (r2 = 0.65). Temperature varied among seasons and at the statewide level did not explain a significant portion of the yield variation. The pattern of increased variation in precipitation will cause further variation in wheat production across the Great Plains. Reducing yield variation among years will require adaptation practices that increase water availability to the crop coupled with the positive impact derived from other management practices, e.g., cultivars, fertilizer management, etc.

Genetic control of plasticity of oil yield for combined abiotic stresses using a joint approach of crop modelling and genome-wide association.

Understanding the genetic basis of phenotypic plasticity is crucial for predicting and managing climate change effects on wild plants and crops. Here, we combined crop modelling and quantitative genetics to study the genetic control of oil yield plasticity for multiple abiotic stresses in sunflower. First, we developed stress indicators to characterize 14 environments for three abiotic stresses (cold, drought and nitrogen) using the SUNFLO crop model and phenotypic variations of three commercial varieties. The computed plant stress indicators better explain yield variation than descriptors at the climatic or crop levels. In those environments, we observed oil yield of 317 sunflower hybrids and regressed it with three selected stress indicators. The slopes of cold stress norm reaction were used as plasticity phenotypes in the following genome-wide association study. Among the 65534 tested Single Nucleotide Polymorphisms (SNPs), we identified nine quantitative trait loci controlling oil yield plasticity to cold stress. Associated single nucleotide polymorphisms are localized in genes previously shown to be involved in cold stress responses: oligopeptide transporters, lipid transfer protein, cystatin, alternative oxidase or root development. This novel approach opens new perspectives to identify genomic regions involved in genotype-by-environment interaction of a complex traits to multiple stresses in realistic natural or agronomical conditions.

Dissecting heat stress tolerance in tropical maize (Zea mays L.)

The negative effects of heat stress on maize growth and reproduction is often expressed in terms of significant yield penalty. Germplasm selection based on yield potential along with heat stress adaptive secondary traits, and establishing their interaction under stress helps in identifying suitable heat tolerant genotypes. To dissect intricate nature of heat stress tolerance, two field trials were conducted during spring 2014 and 2015 under natural heat stress conditions. Planting was adjusted to expose reproductive and grain filling period to high temperature regimes during May month in Hyderabad, India. Correlation analysis between grain yield under stress and secondary traits observed in two year experiments indicated that the traits such as - leaf firing (LF) (r=−0.34** and 0.06), tassel blast (TB) (r=−0.18 and −0.25), tassel sterility (TS) (r=−0.08 and −0.38*), anthesis-silking interval (ASI) (r=−0.24* and −0.02) and senescence (SEN) (r=−0.27** and −0.34*) were negatively correlated, while pollen shedding duration (PSD) (r=0.33** and 0.4*), seed set percentage (SSP_OP) (r=0.76** and 0.58**) and chlorophyll content (CHL) (r=0.55** and 0.41*) were positively associated with grain yield under stress in 2014 and 2015, respectively. An ASI of 2–4days and PSD of 2–4days were found advantageous to grain yield under heat stress. Stigma receptivity was less affected under heat stress when compared to pollen viability, yet stigma initiation was delayed under heat stress, which resulted in prolonged ASI. Overall effect of heat stress was expressed in terms of SSP_OP, which explained yield variation by 78.5 and 57.8% for experiment 1 and 2, respectively. Thus, traits that are indicative of reproductive success under heat stress (ASI, TB, TS, pollen viability, stigma receptivity and SSP_OP) and other morpho-physiological traits (LF, SEN and CHL) may be used along with grain yield in index selection of suitable germplasm for heat stress tolerance.

Learning from commercial crop performance: Oil palm yield response to management under well-defined growing conditions

Farmers learn from their own experiences. However, they are rarely sure if an exceptionally good or bad outcome is due to chance effects or whether it is due to a given combination of management practices and environmental conditions. We surmised that, if each harvest event is adequately characterized and a large number of these events are analyzed together, it should be possible to associate crop response to management within a particular set of growth conditions. We tested this hypothesis using the perennial crop, oil palm.The characterization of the harvest events can be divided into factors the grower can control (CFs), and non-controllable factors (NCFs). Expert opinion, coupled with literature reviews, indicated that the most important NCFs for oil palm in the humid tropics were surplus water and water deficits in the three-year period before and including the year of harvest. Water deficit was assessed using a simplified water balance model with inputs on inherent soil characteristics and rainfall. Surplus water was evaluated from the rainfall, inherent soil characteristics and the topographical position of the block in question. Homologous events (HEs) with similar NCFs were determined from weather and soil data for 141 blocks covering >6000ha over the period 2007–2013 on a commercial plantation.The yield of fresh fruit bunches (FFB) of 262 blocks over the period 2009–2013 was analyzed on the conceptual basis that if HEs can be defined in terms of growing conditions and used to account for part of the yield variation (NCFs), then the remaining variation within these events can be attributable to controllable factors (CFs) or management practices. Inclusion of HEs for the three years before the harvest year improved models used to explain yield variation. The variations in yield were in accordance with the expected effects of the distinct HEs confirming their validity as an analytical tool with normal conditions giving the highest yields, either deficit or surplus water giving intermediate yields, and a combination of both deficit and excess water the lowest yields.We chose the CF of fertilizer response to associate variation of management practices and yield within and across HEs. The overall response to fertilizer at 12.8kgFFB·kg−1, without including HEs in the model, was much greater than that obtained when HEs were included (5.9kgFFB·kg−1). As most data were from blocks with sup-optimal growing conditions, the response to fertilizer over the ranges used was small and under the poorest HEs was not detectable.

Initial growth and yield structure of selected cultivars of cranberry (Vaccinium macrocarpon Ait.) cultivated on mineral soils

Abstract A study was conducted to evaluate the possibility of cranberry cultivation on mineral soils and to assess the influence of vegetative biomass development, generative growth and yield components on the yielding of three cranberry cultivars originating in the USA (Stevens, Pilgrim and Ben Lear) at two locations in Poland. The key biometrical traits involved in yield formation were taken into account, and the soil and plant chemical conditions were evaluated. All of the measured biometrical characteristics were strongly influenced by the location and the year of cultivation, and varietal differences were also noted. The most important determinants that explained yield variation were: the number of uprights per square meter, floral induction and berry set. However, the participation of each component in yield variation was strongly affected by the location, age of plantation and to a minor extent by the cultivar. The study confirmed the possibility of cranberry cultivation on mineral soils with a low pH. The biggest average yield of the three years was collected from cv. Stevens as cultivated on sandy soil in contrast to the same cultivar grown on sandy loam soil. In the case of sandy loam soil after acidification, cv. Pilgrim appeared to be a relatively better yielding cultivar.

An approach to understanding persistent yield variation—A case study in North China Plain

Abstract Large gaps between maize yields on average farmers’ fields and the highest yields achieved by either experiment or farmers are typical throughout the developing world, including in the North China Plain (NCP). Understanding the underlying causes to this yield gap is important for prioritizing strategies for shrinking this gap and improving food security. Quzhou county in Hebei province is typical of the winter-wheat summer-maize system in NCP where the average plot size is only 0.25 ha. To analyze this cropping system amidst the challenge of substantial heterogeneity, we identified fields that were either persistently higher or lower yielding according to remote sensing yield estimates, and then conducted detailed field surveys. We found irrigation facility to be a major constraint to yield both in terms of irrigation water quality and farmers’ access to wells. In total, improving the access to unsalty water would be associated with a 0.32 t/ha (4.2%) increase in multi-year average yield. In addition, farmers’ method of choosing cultivar, which likely relates to their overall knowledge level, significantly explained yield variation. In particular, those choosing cultivars according to technician advice, personal experiences and high yielding neighbors’ advice had on average higher yield than farmers that either followed seed sellers’ advice or collectively purchased seeds.

An Observatory Plot System for Grain Production in the Neotropical Savannas

The neotropical savanna is the second largest biome in South America, with significant potential for agricultural development. In Colombia, this biome is experiencing rapid land-use change leading to the conversion of seminatural landscapes into to intensive agricultural systems. Our Dataset Paper documents the emerging intensive grain production systems. Between 2011 and 2013, we established 336 observatory plots within farmer’s maize, rice, and soybean fields along a 200 Km transect from Puerto Lopez (Meta) to Viento (Vichada). From each of these plots, we submit 184 descriptors or variables capturing their location, rotation history, management, and environment. Our specific objective in collecting the data was to identify key factors explaining yield variation, with emphasis on interactions between management and environmental factors potentially informing the development of site-specific management protocols. Beyond this objective, the dataset submitted here is intended to support additional inquiries contributing to the sustainable development of agriculture in the neotropical savannas.

Efficiency of weather derivatives for Chinese agriculture industry

Purpose – The purpose of this paper is to analyze the efficiency of temperature-based weather derivatives (WD) in reducing risk exposure for Chinese agriculture industry. Therefore, a put option with cumulated growing degree days as its underlying index is assumed to be bought by farmers as a risk management instrument to prevent income fluctuations from adverse temperature conditions. Design/methodology/approach – The objective of this paper is to analyze the efficiency of temperature-based WD in reducing risk exposure for Chinese agriculture industry. Therefore, a put option with cumulated growing degree days as its underlying index is assumed to be bought by farmers as a risk management instrument to prevent income fluctuations from adverse temperature conditions. Findings – The results of the efficiency tests show that temperature-based put options are efficient in offsetting yield shortfalls for rice and wheat in China. The weather-yield models have a high prediction power in explaining yield variation by temperature. Research limitations/implications – The de-trending procedure for the weather-yield model should be improved to distinguish better between technology progress, human activities and influence of weather. Further, more advanced models could be used for the pricing. Practical implications – The findings of the paper support the launch of WD as an efficient risk management tool for agriculture in China. Compared with traditional damage-based insurance, WD are more flexible, have lower transactions costs and avoid moral hazard or adverse selection. Originality/value – The efficiency problem of WD has not been analyzed sufficiently worldwide and especially not for developing countries like China where a large proportion of the population works as farmers. This paper supports to fill this gap.

Fields under Semi-arid Alfisols in South India

Based on a study conducted in 120 farmers’ fields in Manchala and Chevella mandals in Ranga Reddy district in Telangana during 2006 to 2008, the effects of socio-economic variables on productivity of different rainfed crops have been assessed in this paper. The relationships of socio-economic variables and their effects on yield of crops attained by farmers were modeled based on multivariate regression analysis. The regression models gave a significant predictability of yield through socio-economic variables in both individual years and also when pooled over years. In Manchala, land holding, price situation and livestock possession contributed significantly to yield in sorghum. In pigeon pea, extension agency contact explained variation in yield significantly. In castor, land holding and price situation contributed to yield. In kharif rice, livestock possession was significant. In rabi rice, farm power, was significant. In Chevella, in maize, risk orientation, management orientation, mass media exposure, livestock possession, education, production orientation and planning orientation were found to be significant factors. In cotton, age, farming experience, credit orientation, extension agency contact and livestock possession explained yield variation significantly. In kharif rice, farm power and planning orientation, management orientation, education, extension agency contact and mass media exposure were emerged as significant factors. In tomato, price situation, market facility, land holding and credit orientation were found to be significant. In carrot, farm power, credit orientation, marketing orientation, labour availability and land holding were emerged as good predictors of yield. In beetroot, farming experience, education, risk orientation and price situation were found to be important factors. The models could be used to predict the yields of crops through the identified significant socio-economic variables under similar farming conditions in the region. Based on the findings of the study appropriate extension strategies are suggested.

Yield Potential of Hulless Versus Hulled Full‐Sib Genotypes in Four Winter Barley Populations

ABSTRACTDetermining what factors contribute to the yield difference that exists between hulled and hulless winter barley (Hordeum vulgare L.) is necessary for continued yield improvement in the hulless barley germplasm pool. This yield difference is a major factor limiting the acceptance and production of hulless barley as an alternative to traditional hulled barley. Experiments were conducted in Warsaw, VA, during 2010/2011 and 2011/2012 and in Blacksburg, VA, during 2011/2012. Seedling emergence, plants per square meter, normalized difference vegetative index, heading date, spikes per square meter, plant height, lodging, yield, grain volume weight, 1000 kernel weight, spikelets per spike, seeds per spike, seed weight per spike, ash, crude fiber, fat, protein, and starch were measured. Grain volume weight and protein concentration were significantly (p ≤ 0.05) higher for hulless genotypes while seedling emergence and grain ash concentration were significantly (p ≤ 0.05) higher for hulled genotypes. Other traits measured in the study varied by population and environment. On the basis of linear regression analysis, none of the traits explained yield variation in all populations and environments. Before adjustment for hull weight, hulless genotypes yielded significantly (p ≤ 0.05) less than hulled genotypes on average in all populations at Warsaw and for population 1 at Blacksburg during the 2011/2012 growing season. After adjustment for hull weight, yield potential of select hulless genotypes was statistically similar to that of hulled genotypes. Therefore, it is possible to identify hulless genotypes having yield potentials equal to those of their hulled sibs.

Relationships between Sunflower Plant Spacing and Yield: Importance of Uniformity in Spacing

ABSTRACTSunflower (Helianthus annuus L.) production systems in semiarid regions often have poor seedling emergence and patchy stands leading to lower yield. We hypothesized plant spacing was positively related to individual plant yield and this relationship would strengthen as water deficits increased. In a 2‐yr study in Colorado, individual plant spacing and associated seed was measured for different irrigation treatments. Highly significant positive linear and second‐order polynomial regressions of individual plant spacing and yield (p < 0.001) were observed for all irrigation treatments in both years. However, given highly variable individual plant yield with spacing, regression explained no more than 16% of the variation. No consistent pattern in linear regression slope, y‐intercept, or R2 values among irrigation treatments were noted, thereby not supporting the expectation that the relationship would be greater as water deficits increased, although rainfall events may have confounded the results. Leverage plots showed similar results as the regression equations but indicated plant spacing explained yield variation more than irrigation treatment. Regression tree analysis further confirmed that plant spacing was more important than irrigation treatment in explaining final plant yield. These results suggest there is a cost to final yield per unit area where large spacing results in lower yields even though these plants have the highest yields per plant of any spacing classes.

Impact of subsoil physicochemical constraints on crops grown in the Wimmera and Mallee is reduced during dry seasonal conditions

Subsoil physicochemical constraints can limit crop production on alkaline soils of south-eastern Australia. Fifteen farmer paddocks sown to a range of crops including canola, lentil, wheat, and barley in the Wimmera and Mallee of Victoria and the mid-north and Eyre Peninsula of South Australia were monitored from 2003 to 2006 to define the relationship between key abiotic/edaphic factors and crop growth. The soils were a combination of Calcarosol and Vertosol profiles, most of which had saline and sodic subsoils. There were significant correlations between ECe and Cl– (r = 0.90), ESP and B (r = 0.82), ESP and ECe (r = 0.79), and ESP and Cl– (r = 0.73). The seasons monitored had dry pre-cropping conditions and large variations in spring rainfall in the period around flowering. At sowing, the available soil water to a depth of 1.2 m (θa) averaged 3 mm for paddocks sown to lentils, 28 mm for barley, 44 mm for wheat, and 92 mm for canola. Subsoil constraints affected canola and lentil crops but not wheat or barley. For lentil crops, yield variation was largely explained by growing season rainfall (GSR) and θa in the shallow subsoil (0.10–0.60 m). Salinity in this soil layer affected lentil crops through reduced water extraction and decreased yields where ECe exceeded 2.2 dS/m. For canola crops, GSR and θa in the shallow (0.10–0.60 m) and deep (0.60–1.20 m) layers were important factors explaining yield variation. Sodicity (measured as ESP) in the deep subsoil (0.80–1.00 m) reduced canola growth where ESP exceeded 16%, corresponding to a 500 kg/ha yield penalty. For cereal crops, rainfall in the month around anthesis was the most important factor explaining grain yield, due to the large variation in rainfall during October combined with the determinant nature of these crops. For wheat, θa in the shallow subsoil (0.10–0.60 m) at sowing was also an important factor explaining yield variation. Subsoil constraints had no impact on cereal yield in this study, which is attributed to the lack of available soil water at depth, and the crops’ tolerance of the physicochemical conditions encountered in the shallow subsoil, where plant-available water was more likely to occur. Continuing dry seasonal conditions may mean that the opportunity to recharge soil water in the deeper subsoil, under continuous cropping systems, is increasingly remote. Constraints in the deep subsoil are therefore likely to have reduced impact on cereals under these conditions, and it is the management of water supply, from GSR and accrued soil water, in the shallow subsoil that will be increasingly critical in determining crop yields in the future.

Site‐Specific Analysis of a Droughted Corn Crop: I. Growth and Grain Yield

Soil in the southeastern USA Coastal Plain exhibits marked variation, especially near shallow depressions called Carolina Bays. This variation causes correspondingly severe variation in yield, particularly for corn (Zea mays L.) during drought. Though important to precision farming, these features often are overlooked in 1:20000 scale county soil surveys. They are visible in 1:1200 scale soil surveys, but the ability to explain yield variation using soil map units at this scale must be unequivocally demonstrated before committing resources to such a detailed survey. Our objectives were (i) to compare paired samples of four soil map units to determine if grain yield variation were sufficiently explained to be of practical value, and (ii) to extend this evaluation to include data with greater spatial coverage. Corn grain yields were measured at 209 sites in an 8‐ha field, including two Carolina Bays near Florence, SC. Site‐specific effects of soil variation on crop phenology, biomass, and yield components were measured at 11 sites during a drought. Variations in yield components were large and sometimes compensatory (e.g., kernel number and mass), with distinctly different routes to sometimes similar final grain yields. Multiple sites within map units were frequently different at α = 0.05. Analysis of variance for grain yield on soil map unit was statistically significant (P < 0.001) but of limited explanatory value (r2 = 0.16). We conclude that to create soil management zones for precision farming, one must augment even detailed soil map units with additional spatial data, such as yield maps.