Crop Period Research Articles

Root mass may affect soil water infiltration more strongly than the incorporated residue

Crop residue incorporation increases stable soil pores and soil water infiltration, consequently, reduces surface water runoff and soil erosion. However, few studies have examined the relationship between crop residue incorporation and water infiltration. A previous study showed that water infiltration increases depending on the quantity of applied wheat straw. In this study, we examined whether the relationship is applicable to different crop residues in a crop rotation. We grew corn, rose grass, and okra in crop rotation under greenhouses and measured the water infiltration rate at the time of ridge making. A strong correlation was found between the quantity of applied residue and the soil water infiltration rate ( r = 0.953), although there were outliers in the case of no prior crop. By contrast, aboveground biomass of the prior crop showed a stronger correlation with water infiltration rate ( r = 0.965), without outliers. Previous studies have revealed the exponential relation between plant root mass and soil erosion. Our data also show a positive relationship between resistance to erosion and root mass when assuming that aboveground biomass is proportional to the underground biomass. The result also showed that the effect of the prior crop root mass disappears within the next crop period. This suggests that maintaining a large root mass is crucial for reducing soil erosion.

Cover crop rotations affect greenhouse gas emissions and crop production in Illinois, USA

Nitrogen (N) is an essential element and usually a limiting factor in corn (Zea mays, L.) production; yet N build-up in the soil might lead to nitrate (N-NO3) leaching, and release of nitrous oxide (N2O) by denitrification, thus contributing to both water and air pollution. Cover crops (CCs) have been used to scavenge surplus soil N, which might lead to a decrease in the substrate needed for N2O production from the field. The objective of this study was to determine the effect that corn-soybean rotations with different CCs, and tillage methods have on greenhouse gas (GHG) emissions and crop yields in Illinois, USA. The experimental design was a split-block arrangement of tillage (chisel vs. no-till) and CC rotations in a RCBD with 4 replications with the corn and soybean [Glycine max (L.) Merr.] phases present each year. Greenhouse gas emissions - nitrous oxide (N2O), carbon dioxide (CO2) and methane (CH4) – soil available N, (NO3 and NH4) were sampled only during the actively growing cover crop period and from the corn phase of each rotation over a period of 4 years (2013–2017). Cover crop rotations included five corn-soybean rotations with different CCs and one with an unseeded control. Our results suggest that CC efficacy in a humid continental climate, like central Illinois, is associated with fall and winter temperature and precipitation. In two of the years, spring CC growth was poor due to unseasonably cold temperatures; however, in two of the other years, weather was favorable and spring CC biomass ranged from 2-3 Mg ha−1 from three of the species tested. Where spring CC biomass was measurable, a fivefold reduction in N2O emissions was associated to significant reductions in soil N-NO3. Corn yields were not improved with the utilization of CCs and a yield decrease of 12% occurred in the annual ryegrass (Lolium multiflorum Lam.) rotation. The findings from this study are the first from Illinois and one of few from the Midwest showing the potential benefits CCs have in reducing GHG emissions. Moreover, given adequate growing conditions, some CC species can significantly reduce GHG emissions, decrease residual soil N, and have no negative effects on cash crop yields.

VEGETATION CONDITION INDEX: A POTENTIAL YIELD ESTIMATOR

Abstract. Early yield assessment at local, regional and national scales is a major requirement for various users such as agriculture planners, policy makers, crop insurance companies and researchers. Current study explored a remote sensing-based approach of predicting the yield of Wheat, Kharif Rice and Rabi Rice at district level, using Vegetation Condition Index (VCI), under the FASAL programme. In order to make the estimates 14-years’ historical database (2003–2016) of NDVI was used to derive the VCI. The yield estimation was carried out for 335 districts (136 districts of Wheat, 23 districts of Rabi Rice and 159 districts of Kharif Rice) for the period of 2016–17. NDVI products (MOD-13A2) of MODIS instrument on board Terra satellite at 16-day interval from first fortnight of peak growing period of crop were used to calculate the VCI. Stepwise regression technique was used to develop empirical models between VCI and historical yield of crops. Estimated yields are good in agreement with the actual district level yield with the R2 of, 0.78 for Wheat, 0.52 for Rabi Rice and 0.69 for Kharif Rice. For all the districts, the empirical models were found to be statistically significant. A large number of statistical parameters were computed to evaluate the performance of VCI-based models in predicting district-level crop yield. Though there was variation in model performance in different states and crops, overall, the study showed the usefulness of VCI, which can be used as an input for operational crop yield forecasting, at district level.

Women's and Men's Work: The Production and Marketing of Fresh Food and Export Crops in Papua New Guinea

ABSTRACTFresh food markets have been a fixture of the social and economic landscape of urban and rural PNG since colonial times. They were often the first points of engagement with the market economy, especially for women, who as small‐scale producers, sold surplus produce from their food gardens located on communally‐owned land. Although local food markets have remained an important livelihood for women, the later adoption and expansion of perennial export cash crops like coffee and cocoa overshadowed food production for local markets as men dominated export crop production on land alienated from communal ownership for decades or permanently. New forms of social relations of production and more exclusive forms of land tenure emerged to accommodate export crop production that were very different from those governing the production and marketing of fresh food. Market values and a trend towards individualisation of production with less capacity to mobilise labour through reciprocal labour exchange networks have characterised export crop production. With the income benefits captured largely by men, women began redirecting their labour to fresh food production where they were able to exercise more control of production and income while still mobilising labour through indigenous labour exchange arrangements. Attempts by men to appropriate the income of women and sons’ labour in export cropping were greater during flush periods when income levels were high, and they were less likely to attempt to appropriate this income in low crop periods when incomes were lower. However, with the recent emergence of female entrepreneurers earning relatively large sums of money in large‐scale, profit‐driven vegetable production, the moral frameworks governing food production are coming to resemble those governing export crops, and making labour more difficult to mobilise. Despite women being key players in these changes, we argue there is an emerging risk that men will attempt to assert control over this income or move into vegetable production themselves and possibly marginalise women in the process.

Landsat-Based Drought Phenomenon Evaluation of Cassava and Sugar cane in Northeast Thailand

Northeast Thailand is a predominantly agriculture-based region yet it suffers from drought which threatens the people’s livelihood. The region is the largest producer of sugar cane and cassava in the country which are classified as high-value, food and energy crops. Thailand ranks first and third in the world in terms of exporting cassava and sugar cane respectively. Unfortunately, when compared to other regions, the northeast receives the least attention in terms of agricultural research yet it is the most vulnerable part of the country. As such, the goal of this study was to assess agricultural drought in the region pertaining to sugar cane and cassava farming over twelve years (2004 to 2015) relative to the climatic conditions. Normalized Difference Vegetation Index (NDVI)-based Vegetation Condition Index (VCI), derived from Landsat 5 and 8 satellites (30 meters’ resolution) was usedas a determinant of agricultural drought. Precipitation and temperature (0.25-degree resolution) data were sourced from GLDAS-2 Noah model products. Temporal-VCI indicated that the major agricultural drought periods for both crops were 2004, 2005, 2007, 2008, 2009 and 2011. A significant improvement in the crops’ condition was noted in 2014 and 2015. Similarly, spatial-VCI showed an increase in VCI for 2014 and 2015 despite these being major meteorological drought years. This supports the premise that the region has made efforts to curb the effects of drought on agriculture. However, continuous monitoring of drought using different physical indicators is necessary for further development of effective solutions and sharpening of the currently existing measures

Assessment of Weather Parameters for Groundnut (Arachis hypogaea) in Alfisols of Karnataka - a Regression Approach

In field trials of six years (2009-14) the influence of weather parameters on groundnut (The varieties: TMV-2, JL-24, K-134 and C-2) under varied sowing environments (July as normal sowing and August as late sowing months) was studied at Bengaluru, Karnataka in alfisols. Rainy days (RD), bright sun shine hours (BSS), total pan evaporation (EVP. in mm), growing degree days (GDD) and rainfall, the cumulative of all measured during crop period were found to influence significantly the growth and yield of ground nut in alfisols across the genotypes. Whereas, potential evapo-transpiration (PET) and length of growing period (LGP) during the cropping period did not show such significant influence. For achieving maximum yield, the optimum value of annual rainfall from the fitted quadratic curves, was found to be 650 mm. BSS, pan evaporation and GDD during the cropping period showed negative correlation with pod yield. Among the 11 multiple linear regression models established, model III was found to be the most reliable in judging the yield potential of groundnut in alfisols of southern Karnataka (Pod yield in kg/ha = -3058.24**+ 6.55 (RD) -2.01 ** (SSH) + 3.98* (GDD) -6.25 (evaporation in mm) + 8.01** (PET) -21.98** (LGP) + 3.38 ** (LAD) with R2 value of 0.86**. Model IV and XI were effective in predicting the yield (R2 =0.86**& 0.60**, respectively), however of the second order.

Application of Particle Swarm Optimization BP Algorithm in Air Humidity of Greenhouse Crops

The advancement of artificial intelligence, the synonym of precision agriculture has approached the public’s vision, and the different requirements for air humidity in different growth periods of crops are proposed. The BP neural network optimized by particle swarm optimization algorithm is proposed to predict the air humidity of crops. Algorithm, this paper chooses BP algorithm network topology structure is 2-5-1, improves the inertia weight of PSO algorithm, proposes nonlinear inertia weight reduction strategy , trains BP algorithm with improved PSO algorithm, has no gradient information, jumps out local pole Value, reduce the number of iterations of the algorithm, and speed up the training speed of the neural network. According to the experimental results of MATLAB simulation, the air humidity prediction model of particle swarm optimization neural network is constructed, which proves the effectiveness of the improved particle swarm neural network prediction system. It can be shown that the proposed algorithm has a relative error of at least 0.0134 compared with other algorithms.

Precision irrigation perspectives on the sustainable water-saving of field crop production in China: Water demand prediction and irrigation scheme optimization

There is a global shortage of fresh water to meet the clean production of field crops. Water-saving irrigation can increase the production of field crops, but under certain conditions it can be overly water-saving and unsustainable, particularly in complex terrains in China where there is already a shortage of water resources. In light of the insufficient water resources and partial inequality in China, several investigators in China and abroad have researched irrigation nozzles and pumps, but there is little information on the impact of the environment on crop irrigation. The purpose of this study was to provide a fast and effective crop water demand prediction model to estimate the water requirement during the growth period of crops, to achieve the purpose of applying water according to the demand of the crops, and to optimize the irrigation network to determine the optimal network structure in order to reduce irrigation energy consumption in the process. Specifically, this study analyzed the environmental factors, namely, the soil moisture, soil electrical conductivity, air temperature, and light intensity, affecting the physiological water demand of field crops. Relevant sensors were used in the field, and environmental information was collected. This study used the collected environmental data to build a water demand prediction model based on the back propagation (BP) neural network. It also carried out optimized layout and hydrodynamic analyses of the drip irrigation network and identified the best pipe network arrangement. On the basis of the BP neural network, the remarkable R amount of 0.98963 was obtained for the water demand prediction model. The mean square error (MSE) value was 0.00857724. We also found that the drip irrigation pipe network in the H-shaped arrangement was more suitable for field crop irrigation than the comb-shaped and fish bone-shaped arrangements. This study employed the common field crops in China as the research object, used the wireless sensing technology to obtain the field environment information quickly, and constructed a water demand prediction model with high prediction accuracy. Finally, this study realized the precise control of field crop irrigation, reduced the waste of agricultural water, and achieved sustainable field crop production.

Integrating Growth and Environmental Parameters to Discriminate Powdery Mildew and Aphid of Winter Wheat Using Bi-Temporal Landsat-8 Imagery

Monitoring and discriminating co-epidemic diseases and pests at regional scales are of practical importance in guiding differential treatment. A combination of vegetation and environmental parameters could improve the accuracy for discriminating crop diseases and pests. Different diseases and pests could cause similar stresses and symptoms during the same crop growth period, so combining growth period information can be useful for discerning different changes in crop diseases and pests. Additionally, problems associated with imbalanced data often have detrimental effects on the performance of image classification. In this study, we developed an approach for discriminating crop diseases and pests based on bi-temporal Landsat-8 satellite imagery integrating both crop growth and environmental parameters. As a case study, the approach was applied to data during a period of typical co-epidemic outbreak of winter wheat powdery mildew and aphids in the Shijiazhuang area of Hebei Province, China. Firstly, bi-temporal remotely sensed features characterizing growth indices and environmental factors were calculated based on two Landsat-8 images. The synthetic minority oversampling technique (SMOTE) algorithm was used to resample the imbalanced training data set before model construction. Then, a back propagation neural network (BPNN) based on a new training data set balanced by the SMOTE approach (SMOTE-BPNN) was developed to generate the regional wheat disease and pest distribution maps. The original training data set-based BPNN and support vector machine (SVM) methods were used for comparison and testing of the initial results. Our findings suggest that the proposed approach incorporating both growth and environmental parameters of different crop periods could distinguish wheat powdery mildew and aphids at the regional scale. The bi-temporal growth indices and environmental factors-based SMOTE-BPNN, BPNN, and SVM models all had an overall accuracy high than 80%. Meanwhile, the SMOTE-BPNN method had the highest G-means among the three methods. These results revealed that the combination of bi-temporal crop growth and environmental parameters is essential for improving the accuracy of the crop disease and pest discriminating models. The combination of SMOTE and BPNN could effectively improve the discrimination accuracy of the minor disease or pest.

Root mass may affect soil water infiltration more strongly than the incorporated residue

Crop residue incorporation increases stable soil pores and soil water infiltration and reduces surface water runoff and soil erosion. However, few studies have examined the relationship between crop residue incorporation and water infiltration. A previous study showed that water infiltration increases depending on the quantity of applied wheat straw. In this study, we examined whether the relationship is applicable to different crop residues in a crop rotation. We grew corn, rose grass, and okra in crop rotation under plastic film houses and measured the water infiltration rate at the time of ridge making. A strong correlation was found between the quantity of applied residue and the soil water infiltration rate (r = 0.953), although there are outliers in the case of no prior crop. However, aboveground biomass of the prior crop showed a stronger correlation with water infiltration rate (r = 0.965), without outliers. Previous studies have revealed the exponential relation between plant root mass and soil erosion. Our data also show a positive relationship between resistance to erosion and root mass when assuming that aboveground biomass is proportional to the underground biomass. The result also showed that the effect of the prior crop root mass disappears within the next crop period. Our results indicate that maintaining a large root biomass is crucial for reducing soil erosion.

Managing Ecosystem Services with Cover Crop Mixtures on Organic Farms

Core Ideas Mixtures provided BNF while maintaining non‐BNF services equivalent to cereal rye. Winter pea in mixture with a brassica provided the same N input as sole‐planted pea. Crimson clover fixed more N than red clover in the overwintering cover crop period. Brassica mixtures matched or exceeded rye N retention and weed suppression in fall. Participants supported use of cover crop mixtures on diversified vegetable farms. Cover crops are central to an ecological approach to nutrient management that could reduce N losses from agroecosystems. Mixed‐species cover crops that include multiple functional groups, such as legumes and non‐legumes, may improve sustainability by supplying an organic N source to fields while providing other ecosystem services, such as N retention and weed suppression. This on‐farm experiment determined the contributions of two‐ and three‐species cover crop mixtures, which combined legumes, brassicas, and cool‐season grasses, to management‐relevant ecosystem services. In August 2015, we planted mixture treatments of interest with participating farmers on nine Michigan vegetable farms. We also included a cereal rye (Secale cereale L.) monoculture treatment, which is the most common overwintering cover crop in the region. In fall 2015 and spring 2016, we measured aboveground biomass production, biological N fixation (BNF), N assimilation and N retention in aboveground biomass, and weed suppression and compared the results with a no–cover crop control. Overall, we did not find consistent benefits of cover crop mixtures compared with cereal rye for individual services other than BNF. However, several mixtures that included legume species provided BNF while maintaining the same level of weed suppression and N retention as sole‐planted cereal rye. Farmers indicated support for the use of cover crop mixtures on diversified vegetable farms, although some expressed concerns about legume seed costs and disease risk with brassica cover crops. On‐farm experimentation allowed us to address ecological questions of direct relevance to farmers within actual social and environmental contexts.

To Stop Nitrogen Overdose in Soilless Tomato Crop: A Way to Promote Fruit Quality without Affecting Fruit Yield

Precision horticulture is fundamental to ensure high quality production with a minimal environmental footprint. It offers the possibility to manage climatic and fertilization inputs closer to the plant needs. In practice, there is a tendency to over-fertilize, as nitrogen limitation can decrease photosynthesis and consequently fruit yield, but also because nutrient recycling does not lead to any substantial costs increase, thus ignoring the influence of nitrogen input on the balance between growth and metabolism. Nitrogen recommendation for tomato greenhouse production on rockwool is 16mM, even it is well established that only 50% of nitrogen amount is really absorbed by plants. This study compares the usual practice (16 mM) to a nitrogen supply to meet plant’s needs (5 mM). We analyzed plant growth and development, yield, leaf photosynthetic activity and fruit quality (sugars, acids, vitamin C,) over the entire crop period (December to October). Over-fertilization favoured the accumulation of nitrogen in leaves and stem but yield, leaf photosynthetic activity and plant architecture were not significantly improved. In addition, it decreased the quality of the tomatoes as the sugar:acid ratio decreased dramatically in the pericarp, whereas the locular gel composition remained similar. A reduction of the nitrogen supply is one solution to improve tomato quality without any reduction of yield in greenhouse. These data have to be incorporated in tomato fertigation management to define a new standard based on overall quality of tomato fruit and low environmental footprint.

Evaluation of Effect of Different Crop Types On Soil Water Erosion: Case Study of the Myjava Hill Land, Slovakia

The main objective of the study is to model and evaluate the effect of different types of crops on soil water erosion by using the physically-based EROSION-3D model. The EROSION-3D model is based on physical principles and simulates surface runoff, erosion, the deposition of material, and the detachment of soil particles for a single rainfall event. The erosion model was applied to a small catchment of Svacenicky Creek (6.86 km2), which is situated in the Myjava Hill Land in the western part of Slovakia; it is known for its quick runoff responses and related erosion processes. The parts most prone to erosion processes are located in the northern part of the Svacenicky Creek catchment on slopes with a more than 20% angle. Three crop types (winter wheat, silage corn and sugar beets) were chosen in consideration of their growing periods in order to assess the dynamic character of the erosion processes, especially during the growing period of each single crop. The modelling was performed for design rainfall events that were derived separately for each month and which were inputs to the model according to the growing period of the selected crops; six scenarios of the initial soil moisture in a range of 10-40% were designed. The results demonstrated the differences between the erosive effects of the crops selected during their growing periods. Finally, the results were statistically evaluated to determine the differences between the various scenarios of the initial moisture, different rainfall events, and selected crop types.

Evaluation of Fengyun-3C Soil Moisture Products Using In-Situ Data from the Chinese Automatic Soil Moisture Observation Stations: A Case Study in Henan Province, China

Soil moisture (SM) products derived from passive satellite missions are playing an increasingly important role in agricultural applications, especially crop monitoring and disaster warning. Evaluating the dependability of satellite-derived soil moisture products on a large scale is crucial. In this study, we assessed the level 2 (L2) SM product from the Chinese Fengyun-3C (FY-3C) radiometer against in-situ measurements collected from the Chinese Automatic Soil Moisture Observation Stations (CASMOS) during a one-year period from 1 January 2016 to 31 December 2016 across Henan in China. In contrast, we also investigated the skill of the Advanced Microwave Scanning Radiometer 2 (AMSR2) and Soil Moisture Active/Passive (SMAP) SM products simultaneously. Four statistical parameters were used to evaluate these products’ reliability: mean difference, root-mean-square error (RMSE), unbiased RMSE (ubRMSE), and the correlation coefficient. Our assessment results revealed that the FY-3C L2 SM product generally showed a poor correlation with the in-situ SM data from CASMOS on both temporal and spatial scales. The AMSR2 L3 SM product of JAXA (Japan Aerospace Exploration Agency) algorithm had a similar level of skill as FY-3C in the study area. The SMAP L3 SM product outperformed the FY-3C temporally but showed lower performance in capturing the SM spatial variation. A time-series analysis indicated that the correlations and estimated error varied systematically through the growing periods of the key crops in our study area. FY-3C L2 SM data tended to overestimate soil moisture during May, August, and September when the crops reached maximum vegetation density and tended to underestimate the soil moisture content during the rest of the year. The comparison between the statistical parameters and the ground vegetation water content (VWC) further showed that the FY-3C SM product performed much better under a low VWC condition (<0.3 kg/m2) than a high VWC condition (>0.3 kg/m2), and the performance generally decreased with increased VWC. To improve the accuracy of the FY-3C SM product, an improved algorithm that can better characterize the variations of the ground VWC should be applied in the future.

Drought priming during the vegetative stage can enhance post-anthesis drought tolerance by improving photosynthetic capacity in winter wheat

Drought stress during the reproductive period of cereal crops leads to significant yield reductions, therefore, exploring effective methods to improve tolerance to post-anthesis drought is necessary. Pot experiment was carried out to investigate the effects of pre-drought priming on physiological characteristics and grain yield with drought stress at post-anthesis. Moderate water deficits (60–65% of the field capacity) were imposed to prime wheat plants during either the tillering or jointing stages, while severe drought stress (40–45% of the field capacity) was applied during the grain filling stage. The priming treatments significantly improved grain yield resulting in higher biomass. Compared to the control, the grain yield and biomass of the non-priming, tillering priming, and jointing priming treatments were reduced by 15.7, 9.1, and 9.3% and by 11.1, 6.1, and 10.5%, respectively. The primed plants exhibited higher adaptability to subsequent severe drought stress during grain filling, showing higher photosynthetic capacities and light use efficiencies with higher leaf water potentials, soluble protein contents, and Rubisco contents and enhanced enzymatic antioxidant systems. The tillering stage is more responsive to drought priming based on the observed grain yield. These results indicate that moderate drought during the vegetative period is conducive to the development of water-saving agriculture to cope drought stress during grain-filling in wheat.

Resistance stability to Septoria tritici blotch and comparison of screening methods for ranking STB disease

Septoria tritici blotch (STB), caused by the ascomycete fungus Zymoseptoria tritici (formerly known as Mycosphaerella graminicola), is a devastating foliar disease causing major yield losses in wheat each year particularly in North-western Europe. Based on varieties ranked for susceptibility to STB in national trials, it was revealed that Danish Z. tritici populations are very dynamic, both over time and geographical distance. The STB ranking of varieties varied significantly between locations and years indicating differences in virulence between local populations. Comparison of results from seedling tests and field trials reveals that these two types of screenings can be used in combination, since a high correlation was observed in variety rankings for STB susceptibility. Inoculation experiments with seedlings show significant differences in the virulence of pathogen populations originating from different locations within Denmark. Significant changes over time were observed in the variety, Mariboss. An increased susceptibility in this variety followed an intensive cropping period, hence supporting a boom and bust scenario. In conclusion, results from this study indicate the importance of cultivating a diverse set of varieties in order to avoid the boom and bust scenario. Furthermore, varieties should be tested for resistance in several locations in order to ensure an effective and durable resistance.

Water Management through Puddling Techniques

Paddy is the major cereal crop grown in India under stagnated water condition in agricultural fields. Puddling operation is carried out to create the stagnated water condition by creating an impervious layer in the sub soil surface. Puddling is a land preparation practice and it will decide the water usage in fields throughout the cropping period. To improve the puddling quality and stagnation of water, the puddling techniques viz., T1 = cage wheel, T2 = rotavator, = cage wheel + rotavator and T4 = power tiller with rotary tool were taken for the study. T3 Since number of passes also influencing the puddling index, each treatment was analyzed for = single pass and P2 = double pass. The implements were tested in silty loam soil and the parameters P1 like puddling index and infiltration rate were measured using standard measurement procedures. Infiltration rate of field was reduced by increasing the puddling index with stagnated water condition. T4P2 technique had the highest puddling index of 70.91 per cent and lowest infiltration rate of 6.5 mm/d whereas T1had the lowest puddling index and highest infiltration rate. In upland area, the power tiller P1 attached with rotary tool was the most suitable method because infiltration rate observed minimum among all the treatments. When the infiltration rate reduces, the frequency of irrigation can be reduced resulting in saving of water during the crop period. In lowland paddy cultivation, usage of cage wheel with two pass allows infiltration of water resulting in proper drainage and reduces water stagnation.

Climatic water balance and length of growing period for efficient crop planning under different agrotopo-climate in Ranchi, Jharkhand

The study was carried out to estimate the water balance and length of growing period under four different types of topographical situation for efficient crop planning in Ranchi region. Length of growing season analysis reveals that it was 154 and 182 days for Upland and Upper medium land (Don III) while 203 days for both lower medium land (Don II) and Low land (Don I), respectively. In view of the different water related conditions in different land situations, crop diversification would be practicable in uplands as well as in medium lands. Direct seeded rice in upland (Tand) and direct as well as transplanted rice in medium lands may be replaced, to some extent, with less water requiring crops like maize, oilseeds and pulses. However, in typical lowland (Don I) farmers do not have much option for crop diversification or replacement except to go for transplanted paddy. Local climate analysis indicated that it varies from semi arid for Tand areas to moist sub humid condition for low-lying areas.

Yield and Water Balance Components in Wheat (Triticum aestivum L.) as Influenced by Management Practices: Field and Simulation Study

This study focuses on (i) computing water balance components and wheat yield under different management interventions under field conditions, and (ii) simulating water balance components for different soil series in Ludhiana district employing past meteorological data. Field results showed that the yield of November 5 (D2) sown wheat was higher than that sown at October 20 (D1) and November 20 (D3). Mean potential yield of PBW 550 was more (9%) than that of PBW 621. Averaged over cultivars and irrigation regimes, evapotranspiration (ET) was 85 mm more in D1 than D2 and D3, but drainage was more in D3 compared to D1 and D2. Water balance components in both cultivars were comparable except drainage, which was higher in PBW 550 due to its shorter crop period. Water use efficiency (WUE) in D2 was more by 40 and 61 per cent than D1 and D3, respectively. Simulation with CERES-Wheat model point out that grain yield, biomass accumulation and ET under different dates of sowing, cultivars and irrigation regimes had similar trends to observe responses, though there were differences in their magnitude. The wheat grain yield, transpiration and ET increased with fineness of soil texture. Similar to the observed results, the yield was higher in D2 followed by D1 and D3 during 1983–2013. Water use efficiency can be enhanced by planting short duration variety of wheat in the first week of November and scheduling irrigation as per evaporative demand.

Modelling cropping periods of grain crops at the global scale

Abstract Crop models require information on both weather and agronomic decisions to simulate crop productivity and to design adaptation strategies. Due to the lack of observational data, previous studies used different approaches to determine sowing dates and cultivar parameters. However, the timing of harvest has not yet been sufficiently analyzed. Here we propose an algorithm to determine location-specific maturity (or harvest) dates for applications in global modelling studies. Given a sowing date and the climatic conditions, the algorithm returns a suitable maturity date, based on crop physiological parameters and agronomic principles. We test the method on a global land area with a spatial resolution of 0.5° against global reported datasets for major grain crops: winter-wheat, spring-wheat, rice, maize, sorghum and soybean. A single set of rules is able to largely reproduce the observed harvest dates of the six grain crops globally, with a mean absolute error of 19 (maize) to 45 (rice) days. In temperate regions, the temperature seasonality is the major driver of cropping calendars. In sub-tropical regions, crops are grown to match water availability. In the case of limiting growing seasons, the crop cycle is shortened or extended to avoid stressful periods. In the case of long-lasting favorable conditions the crop cycle is shorter than what the growing season would allow. We find that cropping periods can be largely defined by climate and crop physiological traits. The timing of the reproductive phase is shown to be a general criterion for selecting grain crops cultivars. This work will allow for dynamically representing adaptation to climate change by adjusting cultivars and represents a first step towards improved crop phenology simulations by global-scale crop models.