Spatio-temporal variability of sugarcane yield efficiency in the state of São Paulo, Brazil

The objective of this work was to assess the spatial and temporal variability of sugarcane yield efficiency and yield gap in the state of São Paulo, Brazil, throughout 16 growing seasons, considering climate and soil as main effects, and socioeconomic factors as complementary. An empirical model was used to assess potential and attainable yields, using climate data series from 37 weather stations. Soil effects were analyzed using the concept of production environments associated with a soil aptitude map for sugarcane. Crop yield efficiency increased from 0.42 to 0.58 in the analyzed period (1990/1991 to 2005/2006 crop seasons), and yield gap consequently decreased from 58 to 42%. Climatic factors explained 43% of the variability of sugarcane yield efficiency, in the following order of importance: solar radiation, water deficit, maximum air temperature, precipitation, and minimum air temperature. Soil explained 15% of the variability, considering the average of all seasons. There was a change in the correlation pattern of climate and soil with yield efficiency after the 2001/2002 season, probably due to the crop expansion to the west of the state during the subsequent period. Socioeconomic, biotic and crop management factors together explain 42% of sugarcane yield efficiency in the state of São Paulo.

Rice yield estimation based on weather conditions and on technological level of production systems in Brazil

The objective of this work was to evaluate an estimation system for rice yield in Brazil, based on simple agrometeorological models and on the technological level of production systems. This estimation system incorporates the conceptual basis proposed by Doorenbos & Kassam for potential and attainable yields with empirical adjusts for maximum yield and crop sensitivity to water deficit, considering five categories of rice yield. Rice yield was estimated from 2000/2001 to 2007/2008, and compared to IBGE yield data. Regression analyses between model estimates and data from IBGE surveys resulted in significant coefficients of determination, with less dispersion in the South than in the North and Northeast regions of the country. Index of model efficiency (E1') ranged from 0.01 in the lower yield classes to 0.45 in higher ones, and mean absolute error ranged from 58 to 250 kg ha‑1, respectively.

Effects of crop and weed densities on the interactions between barley and Lolium rigidum in several Mediterranean locations

The effects of both barley and Lolium rigidum densities on weed growth and spike production and on crop yield were examined in five field experiments carried out in the Mediterranean drylands of Spain and Western Australia. The aim was to check the consistency of the competitiveness of the crop in different environmental and management conditions. L. rigidum reduced barley yields in most of the experiments (between 0 and 85%), the number of ears per m2 being the most affected. It was found that increasing the barley seeding rate did not reduce the crop losses but did limit weed biomass (between 5 and 61%) and spike production (between 24 and 85%). The variability observed in crop yield losses between sites and seasons was related to rainfall at the beginning of the season. The most sensitive component of yield to weed competition was the number of ears per plant.

Economic analysis of cashew early dwarf Crop BRS - 189 depending on water levels and doses of potassium fertilization

Water and fertilizer among the production factors are the elements that most restrict the production of cashew. The precise amount of these factors is essential to the success of the crop yield. This research aimed to determine the best factor-product ratio and analyze technical and economic indicators, of productivity of the cashew clone BRS 189 (Anacardium occidentale) to production factors water and potassium. The experiment was conducted from May 2009 to December 2009 in an experimental area of 56.0 m x 112.0 m in the irrigated Curu - Pentecoste, located in the municipality of Pentecoste, Ceará, Brazil. Production factors water (W) and potassium (K) were the independent variables and productivity (Y), the dependent variable. Ten statistical models that have proven satisfactory for obtaining production function were tested. The marginal rate of substitution was obtained through the ratio of the potassium marginal physical product and the water marginal physical product. The most suited model to the conditions of the experiment was the quadratic polynomial without intercept and interaction. Considering that the price of the water was 0.10 R$ mm -1, the price of the potassium 2.19 R$ kg -1 and the price of the cashew 0.60 R$ kg-1, the amounts of water and K2O to obtain the maximum net income were 6,349.1 L plant-1 of water and 128.7 g plant -1year, -1 respectively. Substituting the values obtained in the production function, the maximum net income was achieved with a yield of 7,496.8 kg ha-1 of cashew.

Management zones using fuzzy clustering based on spatial-temporal variability of soil and corn yield

Clustering soil and crop data can be used as a basis for the definition of management zones because the data are grouped into clusters based on the similar interaction of these variables. Therefore, the objective of this study was to identify management zones using fuzzy c-means clustering analysis based on the spatial and temporal variability of soil attributes and corn yield. The study site (18 by 250-m in size) was located in Jaboticabal, São Paulo/Brazil. Corn yield was measured in one hundred 4.5 by 10-m cells along four parallel transects (25 observations per transect) over five growing seasons between 2001 and 2010. Soil chemical and physical attributes were measured. SAS procedure MIXED was used to identify which variable(s) most influenced the spatial variability of corn yield over the five study years. Basis saturation (BS) was the variable that better related to corn yield, thus, semivariograms models were fitted for BS and corn yield and then, data values were krigged. Management Zone Analyst software was used to carry out the fuzzy c-means clustering algorithm. The optimum number of management zones can change over time, as well as the degree of agreement between the BS and corn yield management zone maps. Thus, it is very important take into account the temporal variability of crop yield and soil attributes to delineate management zones accurately.

Implications of weeds of genus euphorbia for crop production: a review

The genus Euphorbia comprises about 2000 species ranging from annuals to trees, including C3, C4, and CAM species. Euphorbia species widely studied in agriculture includes E. antiquorum, E. carollata, E. dentata, E. dracunculoides, E. esula, E. geniculata, E. granulata, E. helioscopia, E. heterophylla, E. hierosolymitana, E. hirta, E. maculata, E. microphylla, E. nerifolia, E. piluifera, E. pulcherrima, E. royleana, E. supine, and E. thiamifolia. These species have been reported mainly in field crops/vegetables, orchards, pastures, and rangelands. Euphorbia plants may present allelopathic effect over desirable cereals, pulses, oilseeds, vegetables, forage plants, and nitrifying bacteria, posing a serious threat to livestock production on open range lands through the release of allelochemicals from roots, stems, leaves, and inflorescence in the rhizosphere. Leaves are reported to be more toxic than other plant parts. Competition of Euphorbia spp. against crop plants is the most important crop yield-limiting factor. The critical period for Euphorbia competition with crops is reported to take place between 17 to 70 days after emergence for most crops, depending on root development during the initial crop growth stage, crop height, tillering or branching capacity, whether weeds emerge at the same time as the crop or later after crop emergence; how quickly crop canopy develops and also on Euphorbia species. A yield reduction of 4-85% has been reported in field crops with different Euphorbia species and distinct occurrence densities. Euphorbia species decrease herbage production by 10 to 100% in pasture and rangelands, with many acting as natural insecticide, fungicide, nematidicide, immunopotentiator, or immunosuppressor.

The Effect of Different Weed Management Strategies on the Growth and Yield of Direct-Seeded Dry Rice (Oryza sativa)

Weed management is a primary concern in direct seeded rice (DSR) cropping because weed growth becomes a major constraint on crop yield. A two year field study was set up to evaluate the effect of various weed control measures on crop growth, grain yield and grain quality of DSR. The experiment involved five different weed control measures: hand weeding, hoeing, inter-row tine cultivation, inter-row spike hoeing and herbicide treatment (Nominee 100 SC). The extent of weed control (compared to a non-weeded control) ranged from 50-95%. The highest crop yield was obtained using hand weeding. Hand weeding, tine cultivation and herbicide treatment raised the number of fertile rice tillers formed per unit area and the thousand grain weight. Tine cultivation provided an effective and economical level of weed control in the DSR crop.

Growth and yield of groundnut, sesame and roselle in an Acacia senegal agroforestry system in North Kordofan, Sudan

A field experiment was conducted under rainfed conditions in western Sudan at El-Obeid Research Farm and Eldemokeya Forest Reserve, North Kordofan State, during the growing seasons 2004/05 and 2005/06. The main objective was to investigate the soil physical and chemical properties and yield of groundnut (Arachis hypogea), sesame (Sesamum indicum) and roselle (Hibiscus sabdariffa) of an Acacia senegal agroforestry system in comparison with the sole cropping system. Data were recorded for soil physical and chemical properties, soil moisture content, number of pods per plant, fresh weight (kg ha^−1) and crop yield (kg ha^−1). The treatments were arranged in Randomized Complete Block Design (RCBD) and replicated four times. Significant differences (P < 0.05) were obtained for sand and silt content on both sites, while clay content was not significantly different on both sites. The nitrogen (N) and organic carbon were significantly (P < 0.05) higher in the intercropping system in Eldemokeya Forest Reserve compared with sole cropping. Soil organic carbon, N and pH were not significant on El-Obeid site. Yet the level of organic carbon, N, P and pH was higher in the intercropping system. Fresh weight was significantly different on both sites. The highest fresh weight was found in the intercropping system. Dry weights were significantly different for sesame and roselle on both sites, while groundnut was not significantly different. On both sites intercropping systems reduced groundnut, sesame and roselle yields by 26.3, 12 and 20.2%, respectively. The reduction in yield in intercropping plots could be attributed to high tree density, which resulted in water and light competition between trees and the associated crops.

Effect of planting methods and cyanobacterial inoculants on yield, water productivity and economics of rice cultivation

The impact of two crop planting methods and of the application of cyanobacterial inoculants on plant growth, yield, water productivity and economics of rice cultivation was evaluated with the help of a split plot designed experiment during the rainy season of 2011 in New Delhi, India. Conventional transplanting and system of rice intensification (SRI) were tested as two different planting methods and seven treatments that considered cyanobacterial inoculants and compost were applied with three repetitions each. Results revealed no significant differences in plant performance and crop yield between both planting methods. However, the application of biofilm based BGA bio-fertiliser + 2/3 N had an overall positive impact on both, plant performance (plant height, number of tillers) and crop yield (number and weight of panicles) as well as on grain and straw yield. Higher net return and a higher benefit-cost ratio were observed in rice fields under SRI planting method, whereas the application of BGA + PGPR + 2/3 N resulted in highest values. Total water productivity and irrigation water productivity was significantly higher under SRI practices (5.95 and 3.67 kg ha^(-1) mm^(-1)) compared to practices of conventional transplanting (3.36 and 2.44), meaning that using SRI method, water saving of about 34 % could be achieved and significantly less water was required to produce one kg of rice. This study could show that a combination of plant growth promoting rhizobacteria (PGPR) in conjunction with BGA and 2/3 dose of mineral N fertiliser can support crop growth performance, crop yields and reduces overall production cost, wherefore this practices should be used in the integrated nutrient management of rice fields in India.

Maize yield and rainfall on different spatial and temporal scales in Southern Brazil

This study aimed to establish relationships between maize yield and rainfall on different temporal and spatial scales, in order to provide a basis for crop monitoring and modelling. A 16-year series of maize yield and daily rainfall from 11 municipalities and micro-regions of Rio Grande do Sul State was used. Correlation and regression analyses were used to determine associations between crop yield and rainfall for the entire crop cycle, from tasseling to 30 days after, and from 5 days before tasseling to 40 days after. Close relationships between maize yield and rainfall were found, particularly during the reproductive period (45-day period comprising the flowering and grain filling). Relationships were closer on a regional scale than at smaller scales. Implications of the crop-rainfall relationships for crop modelling are discussed.

Quantifying uncertainty in the simulation of crop-climate processes

The impacts of climate change on crop productivity are often assessed using simulations from a numerical climate model as an input to a crop simulation model. The precision of these predictions reflects the uncertainty in both models. We examined how uncertainty in a climate (HadAM3) and crop General Large-Area Model (GLAM) for annual crops model affects the mean and standard deviation of crop yield simulations in present and doubled carbon dioxide (CO2) climates by perturbation of parameters in each model. The climate sensitivity parameter (λ, the equilibrium response of global mean surface temperature to doubled CO2) was used to define the control climate. Observed 1966–1989 mean yields of groundnut (Arachis hypogaea L.) in India were simulated well by the crop model using the control climate and climates with values of λ near the control value. The simulations were used to measure the contribution to uncertainty of key crop and climate model parameters. The standard deviation of yield was more affected by perturbation of climate parameters than crop model parameters in both the present-day and doubled CO2 climates. Climate uncertainty was higher in the doubled CO2 climate than in the present-day climate. Crop transpiration efficiency was key to crop model uncertainty in both present-day and doubled CO2 climates. The response of crop development to mean temperature contributed little uncertainty in the present-day simulations but was among the largest contributors under doubled CO2. The ensemble methods used here to quantify physical and biological uncertainty offer a method to improve model estimates of the impacts of climate change.

Development and assessment of a coupled crop-climate model

It is well established that crop production is inherently vulnerable to variations in the weather and climate. More recently the influence of vegetation on the state of the atmosphere has been recognized. The seasonal growth of crops can influence the atmosphere and have local impacts on the weather, which in turn affects the rate of seasonal crop growth and development. Considering the coupled nature of the crop-climate system, and the fact that a significant proportion of land is devoted to the cultivation of crops, important interactions may be missed when studying crops and the climate system in isolation, particularly in the context of land use and climate change. To represent the two-way interactions between seasonal crop growth and atmospheric variability, we integrate a crop model developed specifically to operate at large spatial scales (General Large Area Model for annual crops) into the land surface component of a global climate model (GCM; HadAM3). In the new coupled crop-climate model, the simulated environment (atmosphere and soil states) influences growth and development of the crop, while simultaneously the temporal variations in crop leaf area and height across its growing season alter the characteristics of the land surface that are important determinants of surface fluxes of heat and moisture, as well as other aspects of the land-surface hydrological cycle. The coupled model realistically simulates the seasonal growth of a summer annual crop in response to the GCM's simulated weather and climate. The model also reproduces the observed relationship between seasonal rainfall and crop yield. The integration of a large-scale single crop model into a GCM, as described here, represents a first step towards the development of fully coupled crop and climate models. Future development priorities and challenges related to coupling crop and climate models are discussed.

The role of the pod in seed development: strategies for manipulating yield

Pods play a key role in encapsulating the developing seeds and protecting them from pests and pathogens. In addition to this protective function, it has been shown that the photosynthetically active pod wall contributes assimilates and nutrients to fuel seed growth. Recent work has revealed that signals originating from the pod may also act to coordinate grain filling and regulate the reallocation of reserves from damaged seeds to those that have retained viability. In this review we consider the evidence that pods can regulate seed growth and maturation, particularly in members of the Brassicaceae family, and explore how the timing and duration of pod development might be manipulated to enhance either the quantity of crop yield or its nutritional properties.

Calibration and bias correction of climate projections for crop modelling: an idealised case study over Europe

Producing projections of future crop yields requires careful thought about the appropriate use of atmosphere-ocean global climate model (AOGCM) simulations. Here we describe and demonstrate multiple methods for ‘calibrating’ climate projections using an ensemble of AOGCM simulations in a ‘perfect sibling’ framework. Crucially, this type of analysis assesses the ability of each calibration methodology to produce reliable estimates of future climate, which is not possible just using historical observations. This type of approach could be more widely adopted for assessing calibration methodologies for crop modelling. The calibration methods assessed include the commonly used ‘delta’ (change factor) and ‘nudging’ (bias correction) approaches. We focus on daily maximum temperature in summer over Europe for this idealised case study, but the methods can be generalised to other variables and other regions. The calibration methods, which are relatively easy to implement given appropriate observations, produce more robust projections of future daily maximum temperatures and heat stress than using raw model output. The choice over which calibration method to use will likely depend on the situation, but change factor approaches tend to perform best in our examples. Finally, we demonstrate that the uncertainty due to the choice of calibration methodology is a significant contributor to the total uncertainty in future climate projections for impact studies. We conclude that utilising a variety of calibration methods on output from a wide range of AOGCMs is essential to produce climate data that will ensure robust and reliable crop yield projections.

Climate change impacts on crop productivity in Africa and South Asia

Climate change is a serious threat to crop productivity in regions that are already food insecure. We assessed the projected impacts of climate change on the yield of eight major crops in Africa and South Asia using a systematic review and meta-analysis of data in 52 original publications from an initial screen of 1144 studies. Here we show that the projected mean change in yield of all crops is − 8% by the 2050s in both regions. Across Africa, mean yield changes of − 17% (wheat), − 5% (maize), − 15% (sorghum) and − 10% (millet) and across South Asia of − 16% (maize) and − 11% (sorghum) were estimated. No mean change in yield was detected for rice. The limited number of studies identified for cassava, sugarcane and yams precluded any opportunity to conduct a meta-analysis for these crops. Variation about the projected mean yield change for all crops was smaller in studies that used an ensemble of > 3 climate (GCM) models. Conversely, complex simulation studies that used biophysical crop models showed the greatest variation in mean yield changes. Evidence of crop yield impact in Africa and South Asia is robust for wheat, maize, sorghum and millet, and either inconclusive, absent or contradictory for rice, cassava and sugarcane.