Low Yield Years Research Articles

Land–atmosphere feedbacks contribute to crop failure in global rainfed breadbaskets

Global crop yields are highly dependent on climate variability, with the largest agricultural failures frequently occurring during extremely dry and hot years. Land–atmosphere feedbacks are thought to play a crucial role in agricultural productivity during such events: precipitation deficits cause soil desiccation, which reduces evaporation and enhances sensible heating from the land surface; the amplified local temperatures and moisture deficits can be detrimental to crop yield. While this impact of local land–atmosphere feedbacks on agricultural productivity has recently been reported, the dependency of crop yields on upwind regions remains understudied. Here, we determine the spatio-temporal origins of moisture and heat over the world’s largest 75 rainfed breadbaskets, and illustrate the crop yield dependency on upwind regions. Further, we disentangle the role of local and upwind land–atmosphere interactions on anomalous moisture and heat transport during low-yield years. Our results indicate that crop failure increases on average by around 40% when both upwind and local land–atmosphere feedbacks cause anomalously low moisture and high heat transport into the breadbaskets. The impact of upwind land–atmosphere feedbacks on productivity deficits is the largest in water-limited regions, which show an increased dependency on moisture supply from upwind land areas. Better understanding these upwind–downwind dependencies in agricultural regions can help develop adaptation strategies to prevent food shortage in a changing climate.

Analysis of the relationship between yield in cereals and remotely sensed fAPAR in the framework of monitoring drought impacts in Europe

Abstract. This study focuses on the relationship between satellite-measured fraction of absorbed photosynthetically active radiation (fAPAR) and crop yield cereals in Europe. Different features of the relationship between annual yield and multiple time series of fAPAR, collected during different periods of the year, were investigated. The two key outcomes of the analysis are the identification of the period: (i) from March to October as the one having the highest positive correlation between fAPAR and yield and (ii) from February to May as the period characterised by most of the estimated negative correlation. While both periods align well with the commonly assumed dynamic of the growing season, spatial differences are also observed across Europe. On the one hand, the Mediterranean regions report the highest correlation values (r>0.8) and the longest continuous periods with positive statistically significant results (up to 7 months), covering most of the growing season. On the other hand, the central European region is characterised by the most limited positive correlation values, with only 2 months or less showing statistically significant results. While marked differences in the overall capability to capture the full dynamic of yield are observed across Europe, fAPAR anomalies seem capable of discriminating low-yield years from the rest in most of the cases.

A critical analysis of the effect of projected temperature and rainfall for differential sowing of maize cultivars under RCP 4.5 and RCP 6.0 scenarios for Punjab

A simulation study was conducted for two maize cultivars (PMH1 and PMH2) under two Representative Concentration Pathways (RCPs) in four agroclimatic zones of Punjab state of India where climate change depicts a consistent rise in temperature and variability in rainfall. The temperature and rainfall varied from location to location so agroclimatic zone II (Ballowal Saunkhri), zone III (Ludhiana, Amritsar, and Patiala), zone IV (Bathinda), and zone V (Abohar and Faridkot) were selected for the study. The bias-corrected ensemble model data from seventeen global circulation models (GCMs) for RCP 4.5 and RCP 6.0 was used to simulate maize yield for a period of 70 years (2025–2095) using calibrated and validated CERES-Maize model. The simulated yield trend in Punjab under current dates of sowing indicated a strong negative correlation between the yield and the weather parameters under the two scenarios. Agroclimatic zones II, III, and V (Faridkot) observed an increase in temperature by 1 °C over the 70 years’ time period which led to lowering of the maize yield from high yield category (> 5000 kg/ha) to low (< 3000 kg/ha). The cv PMH1 was able to compensate these effects and performed better in agroclimatic zones II, III, and V (Faridkot). In agroclimatic zones IV (Bathinda) and V (Abohar), an increase in temperature by 2 °C is observed, which led to decline of yield categories from medium yield years to low yield years. Though the rainfall in the region was higher for low yield years, but the rainfall amount was insufficient to mitigate the impact of temperature. Under the future stabilization scenarios, amongst the current sowing dates, mid-June was found suitable in agroclimatic zones II and III and in agroclimatic zone V (Faridkot) both early and mid-June sowing dates performed well. Considering the suitable sowing dates under current farming practices, it would be easy to determine the future sowing window for maize cultivars in Punjab.

Observed Changes in Crop Yield Associated with Droughts Propagation via Natural and Human-Disturbed Agro-Ecological Zones of Pakistan

Pakistan’s agriculture and food production account for 27% of its overall gross domestic product (GDP). Despite ongoing advances in technology and crop varieties, an imbalance between water availability and demand, combined with robust shifts in drought propagation has negatively affected the agro-ecosystem and environmental conditions. In this study, we examined hydro-meteorological drought propagation and its associated impacts on crop yield across natural and human-disturbed agro-ecological zones (AEZs) in Pakistan. Multisource datasets (i.e., ground observations, reanalysis, and satellites) were used to characterize the most extensive, intense drought episodes from 1981 to 2018 based on the standardized precipitation evaporation index (SPEI), standardized streamflow index (SSFI), standardized surface water storage index (SSWSI), and standardized groundwater storage index (SGWI). The most common and intense drought episodes characterized by SPEI, SSFI, SSWSI, and SGWI were observed in years 1981–1983, 2000–2003, 2005, and 2018. SPEI yielded the maximum number of drought months (90) followed by SSFI (85), SSWSI (75), and SGWI (35). Droughts were frequently longer and had a slower termination rate in the human-disturbed AEZs (e.g., North Irrigated Plain and South Irrigated Plain) compared to natural zones (e.g., Wet Mountains and Northern Dry Mountains). The historical droughts are likely caused by the anomalous large-scale patterns of geopotential height, near-surface air temperature, total precipitation, and prevailing soil moisture conditions. The negative values (&lt;−2) of standardized drought severity index (DSI) observed during the drought episodes (1988, 2000, and 2002) indicated a decline in vegetation growth and yield of major crops such as sugarcane, maize, wheat, cotton, and rice. A large number of low-yield years (SYRI ≤ −1.5) were recorded for sugarcane and maize (10 years), followed by rice (9 years), wheat (8 years), and cotton (6 years). Maximum crop yield reductions relative to the historic mean (1981–2017) were recorded in 1983 (38% for cotton), 1985 (51% for maize), 1999 (15% for wheat), 2000 (29% for cotton), 2001 (37% for rice), 2002 (21% for rice), and 2004 (32% for maize). The percentage yield losses associated with shifts in SSFI and SSWSI were greater than those in SPEI, likely due to longer drought termination duration and a slower termination rate in the human-disturbed AEZs. The study’s findings will assist policymakers to adopt sustainable agricultural and water management practices, and make climate change adaptation plans to mitigate drought impacts in the study region.

Yield forecasting with machine learning and small data: What gains for grains?

Forecasting crop yields is important for food security, in particular to predict where crop production is likely to drop. Climate records and remotely-sensed data have become instrumental sources of data for crop yield forecasting systems. Similarly, machine learning methods are increasingly used to process big Earth observation data. However, access to data necessary to train such algorithms is often limited in food-insecure countries. Here, we evaluate the performance of machine learning algorithms and small data to forecast yield on a monthly basis between the start and the end of the growing season. To do so, we developed a robust and automated machine-learning pipeline which selects the best features and model for prediction. Taking Algeria as case study, we predicted national yields for barley, soft wheat and durum wheat with an accuracy of 0.16–0.2 t/ha (13-14% of mean yield) within the season. The best machine-learning models always outperformed simple benchmark models. This was confirmed in low-yielding years, which is particularly relevant for early warning. Nonetheless, the differences in accuracy between machine learning and benchmark models were not always of practical significance. Besides, the benchmark models outperformed up to 60% of the machine learning models that were tested, which stresses the importance of proper model calibration and selection. For crop yield forecasting, like for many application domains, machine learning has delivered significant improvement in predictive power. Nonetheless, superiority over simple benchmarks is fully achieved after extensive calibration, especially when dealing with small data.

Vegetative and Reproductive Response to Fruit Load in Two Jojoba (Simmondsia chinensis) Cultivars

Jojoba (Simmondsia chinensis) is a wax crop cultivated mainly in arid and semi-arid regions. This crop has been described as an alternate-bearing plant, meaning that it has a high-yield year (“on-year”) followed by a low-yield year (“off-year”). We investigated the effect of fruit load on jojoba’s vegetative and reproductive development. For two consecutive years, we experimented with two high-yielding cultivars—Benzioni and Hazerim—which had opposite fruit loads, i.e., one was under an on-year load, while the other was under an off-year load simultaneously. We found that removing the developing fruit from the shoot during an off-year promotes further vegetative growth in the same year, whereas in an on-year, this action has no effect. Moreover, after fruit removal in an on-year, there was a delay in vegetative growth renewal in the consecutive year, suggesting that the beginning of the growing period is dependent on the previous year’s yield load. We found that seed development in the 2018 season started a month earlier than in the 2017 season in both cultivars, regardless of fruit load. This early development was associated with higher wax content in the seeds. Hence, the wax accumulation rate, as a percentage of dry weight, was affected by year and not by fruit load. However, on-year seeds stopped growing earlier than off-year seeds, resulting in smaller seeds and an overall lower amount of wax per seed.

Period-3 dominant phase synchronisation of Zelkova serrata: border-collision bifurcation observed in a plant population

The population synchrony of tree seed production has attracted widespread attention in agriculture, forestry and ecosystem management. Oaks usually show synchronisation of irregular or intermittent sequences of acorn production, which is termed ‘masting’. Tree crops such as citrus and pistachio show a clear two-year cycle (period-2) termed ‘alternate bearing’. We identified period-3 dominant phase synchronisation in a population of Zelkova serrata. As ‘period-3’ is known to provide evidence to imply chaos in nonlinear science, the observed period-3 phase synchronisation of Zelkova serrata is an attractive real-world phenomenon that warrants investigation in terms of nonlinear dynamics. Using the Hilbert transform, we proposed a procedure to determine the fractions of periods underlying the survey data and distinguished the on-year (high yield year) and the off-year (low yield year) of the masting. We quantified the effects of pollen coupling, common environmental noise and individual variability on the phase synchronisation and demonstrated how the period-3 synchronisation emerges through a border-collision bifurcation process. In this paper, we propose a model that can describe diverse behaviours of seed production observed in many different tree species by changing its parameters.

Temperature effect on yield of winter and spring irrigated crops

Extreme weather conditions, such as low and high air temperature changes during the growing season, play a significant role in irrigated crop yield. In this study an iterative chi-square statistical method was used to determine the relationship between air temperature and yield of dominant winter and spring irrigated crops in Shahrekord, Iran. Long term daily weather (1991–2016) and annual crop yield (wheat, barley, potato, alfalfa) data were analyzed and the relationships between low and high crop yield years with maximum and minimum air temperatures was evaluated. Our results indicated that higher temperature during tillering, dormancy breaking and stem elongation (fewer number of days with Tmax ≤ 5 °C and greater number of days with Tmax > 20 °C) and colder temperature in short time before dormancy breaking led to wheat yield loss. Cold weather during germination, seedling growth and tillering (greater number of days with Tmax ≤ 0 °C and excess nights with Tmin ≤ −20 °C) as well as cold temperature during breaking dormancy and kernel dough development (fewer days with Tmax > 25 °C) caused to reduce the barely grain yield. Warmer nights during potato flowering, development of fruit and mid-stage of tuber formation (excess days with Tmin > 10 °C) and cooler days during potato senescence (greater number of days with Tmax ≤ 20 °C) reduced potato tuber yield. Cool weather during emergence of first flower buds and first flowering stages (greater number of days with Tmax ≤ 20 °C) led to forage yield loss of alfalfa. Determination of critical periods when these crops are most sensitive to air temperature extremes provides important information for modifying management practices, such as adjusting planting date in order to reduction the negative effects of temperature extremes on crop yield. This information will also be useful for predicting crop yields in different years.

Canola yield sensitivity to climate indicators and passive microwave-derived soil moisture estimates in Saskatchewan, Canada

Canola production is primarily attributed to variations in precipitation and temperature, however the direct relationship between soil moisture and canola yield has yet to be fully evaluated. Recent advancements in microwave remote sensing offer the potential for timely and accurate acquisitions of large-scale soil moisture data; however, the short temporal record of these data sets presents a challenge for defining extreme soil moisture events, and satellite limitations surrounding the detection of surface conditions prevents a direct agronomic link between satellite soil moisture and plant available water. This study examines the potential of passive microwave surface soil moisture observations from the Soil Moisture Ocean Salinity (SMOS) mission satellite as an indicator of canola yields across Saskatchewan over a 6-year period from 2010–2015. An iterative chi-square analysis was employed to investigate the association between SMOS soil moisture and canola yield data from Saskatchewan’s 20 Census Agricultural Regions (CARs). Associations between canola yield and traditionally used climate indicators (temperature and rainfall) were compared to assess the relative effectiveness of SMOS soil moisture as an indicator of yield. Cooler-than-average temperatures (Tmax ≤ 26 °C and Tmin ≤ 12 °C) at the beginning of August were shown to have a positive impact on canola yield, while high nocturnal temperatures (Tmin ≥ 15 °C) in mid-July had a negative effect. Precipitation was found to have a minimal impact on canola production due to greater-than-average rainfall that was observed during the period of study. The strongest associations were observed between soil moisture and canola yield, particularly in low-yielding years with excess soil moisture exhibiting significant associations (p < 0.01, df = 1) throughout the growing season. The beginning of June, coinciding with the stand establishment stage, was identified as a critical time period in which excess soil moisture (≥26.6%) resulted in yield losses. The findings of this study suggest that soil moisture observations obtained by passive microwave satellites, such as SMOS, may provide an effective indicator of crop yields, particularly in areas experiencing excess soil moisture conditions.

Responses of residual and recommended N, P, K, S nutrients and crops to six annual soil test-based fertilizer rates and seeding systems

Responses of residual and recommended nitrogen (N), phosphorus (P), potassium (K), sulfur (S) nutrient amounts and crops (yield, emergence, and height) to 0, 60, 100, and 140% soil test-based fertilizer rate applications on the same plots for six years under minimum tillage and direct seeding systems were assessed. Higher fertilizer rates increased residual nitrate (NO3)-N, extractable K, and sulfate (SO4)-S amounts after a low crop yield year, particularly NO3-N, without temporal trend. Increase in residual available P level at higher fertilizer rates showed a positive temporal trend. Lower N, P, and S fertilizer rates were recommended to crops after higher residual NO3-N, available P, and SO4-S levels. Crops effectively used the residual nutrients. Compared to 0%, the relative seed yields at 60, 100, and 140% rates increased with years of fertilization. There were no clear responses of measured soil and crop parameters to seeding systems. Testing residual nutrients can optimize fertilizer use and crop yields.

Bird Management in Blueberries and Grapes

Bird damage to fruit is a long-standing challenge for growers that imposes significant costs because of yield losses and grower efforts to manage birds. We measured bird damage in ‘Bluecrop’ blueberry fields and Pinot noir vineyards in 2012–2014 in Michigan to investigate how year, grower, and forest cover influenced the proportions of bird damage. We tested whether inflatable tubemen (2013–2014) and a methyl anthranilate spray (2015) reduced bird damage in blueberries, and tested the deterrent effect of inflatable tubemen in grapes (2014). Years when crop yield was lower tended to have a higher damage percentage; for blueberries, bird damage was highest in 2012, and in grapes, damage was highest in 2012 and 2014. Neither blueberry fields nor vineyards with inflatable tubemen showed significantly reduced bird damage, although the blueberry fields showed a non-significant trend toward lower damage in the tubemen blocks. Blueberry field halves treated with the methyl anthranilate spray had equivalent bird damage to untreated halves. Our results correspond to previous work showing that percent bird damage varies by year, which was likely because bird consumption of fruit is relatively constant over time, while fruit yield varies. Fruit growers should expect a higher proportion of bird damage in low-fruit contexts, such as low-yield years, and prepare to invest more in bird management at those times. Investigating patterns of bird damage and testing deterrent strategies remain challenges. Bird activity is spatially and temporally variable, and birds’ mobility necessitates tests at large scales.

Diverging importance of drought stress for maize and winter wheat in Europe

Understanding the drivers of yield levels under climate change is required to support adaptation planning and respond to changing production risks. This study uses an ensemble of crop models applied on a spatial grid to quantify the contributions of various climatic drivers to past yield variability in grain maize and winter wheat of European cropping systems (1984–2009) and drivers of climate change impacts to 2050. Results reveal that for the current genotypes and mix of irrigated and rainfed production, climate change would lead to yield losses for grain maize and gains for winter wheat. Across Europe, on average heat stress does not increase for either crop in rainfed systems, while drought stress intensifies for maize only. In low-yielding years, drought stress persists as the main driver of losses for both crops, with elevated CO2 offering no yield benefit in these years.

Testing a crop model with extreme low yields from historical district records

Extreme weather events across the world cause large variations in daily seasonal temperature and precipitation, potentially reducing grain yield and negatively affecting global food security. Thus, it is important to assess if crop growth models can simulate the yield losses caused by these extreme weather events. We tested if the DSSAT-NWheat crop simulation model, which has been successfully validated with many controlled field-experimental data across the world, can reproduce historical extreme low-yielding years at three global locations in the USA, France, and Australia. The crop model reproduced extreme low yields in some of the driest and hottest years at all three locations. However, the model failed to simulate some of the observed extreme low-yielding years. Also, the model simulated extreme low yields in some years that were not extreme low yielding in the observed records. Surprisingly, some of the driest and hottest years did not show up as observed extreme low-yielding years in the district records. This discrepancy could be explained by reporting yields, or in this case “not reporting extreme low yields” in district records, as indicated by large drops in harvested areas in extreme dry and hot seasons. Other discrepancies exist because crop models do not often consider many factors under farmer-field conditions that lead to extreme low yields in district records, including frost, hail and lodging, pests and diseases, and excess water. Historical district yield records are also limited for model testing due to unknown and changing aggregation across a district, possible omission of extreme low-yielding fields in some years, and unknown spatial and temporal varying cultivars and crop management, particularly in initial soil water conditions. In conclusion, we do not recommend using historical district yield records for model testing of extreme low yields.

ЧИСЛЕННОСТЬ СЕГОЛЕТОК ВОБЛЫ, ЛЕЩА И СУДАКА В ЗАПАДНОЙ ЧАСТИ СЕВЕРНОГО КАСПИЯ В 2012-2016 ГОДАХ

The article presents the data obtained in the result of scientific research expeditions aiming to determine abundance of roach ( Rutilus caspicus ), bream ( Abramis brama ) and pike-perch ( Sander lucioperca ) in the water area of the Western part of the Northern Caspian Sea in September-October 2012-2016. Roach and bream fingerlings, that are the largest part of anadromous fishes of the Volga-Caspian fishery basin, predominate in catches of minnow trawls. Theirs total share is 85.9-99.6%. Weight density of pike-perch is not so great - 0.2-2.0%. Level of yield of roach, bream, pike-perch fingerlings in the Northern Caspian Sea varies from year to year (roach 4.5-7 mln, bream 4.0-12.9 mln, pike-perch 0.030-0.163 mln specimens) and determined by abiotic and biotic complex of factors, the general of which is the water content of the Volga river in the flood-time period. The number of young fishes increases in the period with high level of flowing, in low flowing times it decreases. The most favorable conditions for fish reproduction were in high-water periods in 2013 and 2016, and least favorable - in extremely law water periods in 2015. The maximum number of bream fingerlings reached in 2013, roach - in 2012 and 2013, bream - in 2016. The minimum number of all three fish species was noted in 2015. By the results of researches of bream breed, years 2012, 2013 and 2016 are considered not so great yielding years; years 2014 and 2015 are low-yield years. The breed of roach and bream from 2012 to 2016 are estimated like low yield years, which was determined not only due to low water level of the Volga river, but because of a decrease of the population number of these fish species and breeding population on breeding grounds.

Proportions of bird damage in tree fruits are higher in low-fruit-abundance contexts

Frugivorous birds impose significant costs on tree fruit growers through direct consumption of fruit and grower efforts to manage birds. We documented factors that influenced tree fruit bird damage from 2012 through 2014 with a coordinated field study in Michigan, New York, and Washington. For sweet cherries, percent bird damage was higher in 2012 compared to 2013 and 2014, in Michigan and New York compared to Washington, and in blocks with more edges adjacent to non-sweet cherry land-cover types. These patterns appeared to be associated with fruit abundance patterns; 2012 was a particularly low-yield year for tree fruits in Michigan and New York and percent bird damage was high. In addition, percent bird damage to sweet and tart cherries in Michigan was higher in landscapes with low to moderate forest cover compared to higher forest cover landscapes. 'Honeycrisp' apple blocks under utility wires were marginally more likely to have greater bird damage compared to blocks without wires. We recommend growers prepare bird management plans that consider the spatial distribution of fruit and non-fruit areas of the farm. Growers should generally expect to invest more in bird management in low-yield years, in blocks isolated from other blocks of the same crop, and in blocks where trees can provide entry to the crop for frugivorous birds.

Statistical modeling implications for coffee progenies selection

A reliable phenotyping and a thorough investigation of the experimental data via accurate statistical methods are key requirements for attaining selection gain. Coffee bean yield data are provided from annual harvests. The data analysis is generally performed based on total phenotypic data of entire period or in biennia using a split-plot-in-time model. An essential aspect of these data is the covariance associated with some random factors of the statistical model. The aim of this work was to evaluate different covariance matrix structures in coffee progenies bean yield modeling and their implications for prediction accuracy of progenies genotypic values and selection under different harvest data grouping strategies. We evaluated 21 S0:1 Coffea arabica L. progenies during eight harvests. The analyses were conducted considering all the harvests (annual or biennia) and focusing only on the high yield or low yield years. In each case, we modeled the residual covariance matrix (R) and the genetic covariance matrix over harvests (G). We noticed that some models are more suitable in explaining the coffee yield pattern. There were alterations in parameter estimates, prediction error variance of genotypic values, rankings and coincidence index in selecting the best progenies. The model involving annual harvests gave more information regarding the coffee progenies yield behavior in comparison to biennia.

How Climate Change Affects Extremes in Maize and Wheat Yield in Two Cropping Regions

Abstract Downscaled climate model projections from phase 5 of the Coupled Model Intercomparison Project (CMIP5) were used to force a dynamic vegetation agricultural model (Agro-IBIS) and simulate yield responses to historical climate and two future emissions scenarios for maize in the U.S. Midwest and wheat in southeastern Australia. In addition to mean changes in yield, the frequency of high- and low-yield years was related to changing local hydroclimatic conditions. Particular emphasis was on the seasonal cycle of climatic variables during extreme-yield years and links to crop growth. While historically high (low) yields in Iowa tend to occur during years with anomalous wet (dry) growing season, this is exacerbated in the future. By the end of the twenty-first century, the multimodel mean (MMM) of growing season temperatures in Iowa is projected to increase by more than 5°C, and maize yield is projected to decrease by 18%. For southeastern Australia, the frequency of low-yield years rises dramatically in the twenty-first century because of significant projected drying during the growing season. By the late twenty-first century, MMM growing season precipitation in southeastern Australia is projected to decrease by 15%, temperatures are projected to increase by 2.8°–4.5°C, and wheat yields are projected to decline by 70%. Results highlight the sensitivity of yield projections to the nature of hydroclimatic changes. Where future changes are uncertain, the sign of the yield change simulated by Agro-IBIS is uncertain as well. In contrast, broad agreement in projected drying over southern Australia across models is reflected in consistent yield decreases for the twenty-first century. Climatic changes of the order projected can be expected to pose serious challenges for continued staple grain production in some current centers of production, especially in marginal areas.

Seasonal changes in nutrient content and concentrations in a mature deciduous tree species: Studies in almond (Prunus dulcis (Mill.) D. A. Webb)

Knowledge of the pattern of nutrient uptake and loss and the within-tree allocation of nutrients in trees is critical to the understanding of ecosystem nutrient fluxes and to the management of applied nutrients in agricultural ecosystems. Fluxes of nutrients in whole trees and determination of total annual uptake, nutrient allocation within organs and loss in annual organs were obtained in a mature commercial almond (Prunus dulcis) orchard managed with non-limiting irrigation under varied nitrogen (N) and potassium (K) fertilization regimes from 2008 through 2012. Whole tree nutrient dynamics were derived from multiple in-season measurements of nutrients in leaves and fruits and biomass in all years. Whole tree nutrient budgets in perennial tree parts were derived from whole tree excavations and tree coring in 2011 and 2012 when the orchard had reached >85% full canopy closure. The annual N increment in perennial tree organs was 21kg, 41kg, 44kg and 45kgha−1 for the four N application rates of 140kg, 224kg, 309kg and 392kgha−1 N, respectively. In this orchard maximum agronomic productivity (yield plus increment in tree size) was recorded with an N application rate of 309kgha−1. Under optimal N application the annual increment in P, K, Ca and Mg was 8kg, 38kg, 3.5kg and 2.8kgha−1. Total N in leaves in midsummer in 2012 (a low yield year) was 37kg, 43kg, 54kg and 58kgha−1 for the four N application rates. In contrast, total N in leaves at mid-summer in 2011 (high yield year) was 21kg, 28kg, 35kg and 39kgha−1 for the four N application rates. In years 2009 through 2011 nitrogen in fruit at harvest represented 90% of whole tree N fluxes. In trees provided just adequate N to achieve maximal yield (309kgha−1 in this experiment in years 2009–11), N, P, K, S, Ca and Mg export in fruit was 212kg–366kgha−1, 26kg–45kgha−1, 265kg–389kgha−1, 7.9kg–14.4kgha−1, 24.7kg–29.6kgha−1 and 15.9kg–22.6kgha−1, respectively. The majority of whole tree macro and micronutrient uptake occurred between anthesis and kernel fill with 35–50% of total N uptake occurring before 40 days after full bloom (DAFB) and 80–90% of the total N being accumulated by 112 DAFB coinciding with kernel filling. Over 70% of the total P and 65–80% of the total K was accumulated in fruits by mid-season. Ninety percent of total S, 80–90% of total Ca and 70–85% of total Mg accumulated in fruit by 112 DAFB. The findings from the study can be used for in-season fertilizer management according to plant demand based on expected yield.

تعیین ارتباط شرایط آب و هوایی با تولید محصول جو دیم (مطالعه موردی: آذربایجان شرقی)

Temperature and precipitation are two climatic variables haveing the most significant impact on the variability of crops yield. In this study, the relationship between rainfed barley yield, temperature and rainfall were assessed with both simple correlation and iterative chi-square analysis for Eastern Azerbaijan in four districts (Tabriz, Maragheh, Sarab and Miyaneh). The iterative chi-square analysis identified relationships of low and high yield years to the maximum and minimum of temperatures and rainfall. The same patterns in all regions found in the relationship between rainfed barley yield and climatic indicators. In all regions,the main climatic factors limiting rainfed barley production were low,high and very high temperatures occurring during February (cardinal value rang, T min ≤ -10 °C to T min ≤ -20°C), before the onset of winter (cardinal value rang,T max≥15 °C to Tmax≥20°C ) and at anthesis stage(early May to early June) (cardinal value ranging, Tmax ≥ 25 °C to T max ≥ 35°C ) respectively. Cold nights (cardinal value rang, T min ≤ 10 °C to T min ≤ 20°C) and enough rainfall (cardinal value ranging, pp ≥ 5.8 mm to pp ≥ 10.6 mm ) led to increasing yield in ripening stage (early June to early August) and in the early planting respectively. In all regions, the anthesis stage was the critical time in which high temperatures and low precipitation led to yield loss. Determine the impact of temperature and rainfall on crops yield provides advantage information for adopting appropriate management practices in order to decrease adverse effects.

Effect of inter and intra seasonal variability in meteorological parameters on rice productivity in central Punjab

A study was conducted to ascertain the role of meteorological parameters on year to year variability in rice yield in Punjab using 12 years (2000-11) data of Ludhiana district. The rice growing season was divided into three stages (entire rice season, vegetative stage and reproductive stage) and the meteorological data during these stages were correlated with the rice yields. Highly significant (p=0.05) positive correlation was noticed between grain yield and sunshine hours received during entire rice season and vegetative stage of the crop. The maximum temperature during entire rice season and vegetative stage was significantly (p=0.10) and positively correlated with grain yield. During all three stages maximum relative humidity had significantly negative correlation with grain yield. Rainfall during vegetative stage and number of rainy days during reproductive stage were significantly and negatively correlated with grain yield. After studying the deviation of meteorological parameters from respective normals during high and low yield years and the correlation of meteorological parameters with rice yield it can be concluded that in the central parts of the Punjab sunshine hours play a major role in determining the productivity of rice.