Sowing Window Research Articles

Assessment of AquaCrop model for simulating Baby corn (Zea mays L.) growth and productivity under different sowing windows and crop geometries

The experiments were conducted at Agro Climate Research Centre, TNAU, Coimbatore. Calibration and validation of AquaCrop model was done using Winter and Kharif, 2022 data. Calibration showed that AquaCrop accurately simulated the canopy cover by low RMSE≤13.1%, good E≤0.76, high d≤0.94 and high R2 values ≥0.98 and biomass development by low RMSE≤13.2%, high E≤0.92, good d≤0.68 and high R2 values ≥0.95. During calibration, model well-simulated the CC under second sowing (D2) and biomass under third sowing (D3). Validation showed almost good fit of CC by low RMSE≤22.0%, good E≤0.68, high d≤0.84 and high R2 values ≥0.97 and biomass development with low RMSE≤7.1%, good E≤0.66, good d≤0.60 and high R2 values ≥0.98. During Validation, model well-simulated the CC and biomass under third sowing (D3). Model showed good fit of yield during first sowing window (D1) with a less deviation for both calibration and validation (15.6% and 5.8% respectively). From the result it could be concluded that sowing windows influence on baby corn production was accurately simulated using AquaCrop during calibration (R2=0.94) and validation (R2=0.98). Hence, AquaCrop proved to be a feasible tool for maximizing the Baby corn yield under different sowing windows.

Impact on Yield Attributes and Yields of Groundnut Varieties under Different Sowing Windows in Western Maharashtra, India

Present experiment was undertaken to study the “Impact of yield attributes and yields of groundnut varieties under different sowing windows in Western Maharashtra Plain Zone” at Department of Agricultural Meteorology Farm, Centre of Advanced Faculty Training (CAFT) in Agricultural Meteorology, College of Agriculture, Pune, Maharashtra State (India). The experiment was laid out in split plot design comprised of four varieties viz., V1: JL-501, V2: RHRG-6083 (Phule Unnati), V3: TAG-24 and V4: JL-776 (Phule Bharati) as main plot and four sowing windows viz., S1: 25rd MW (18th to 24th June), S2: 26th MW (25th June to 01st July), S­3: 27th MW (2nd to 8th July) and S4: 28th MW (09th to 15th July) as sub plot treatments. The number of pods plant-1, weight of pods plant-1, 100 kernel weight (g), shelling (%) and yields as influenced by the different treatments were recorded at harvest. Yield contributing characters viz., number of pods-1 (45.25 and 43.29) and weight of pods-1 (12.99 and 13.82) were found significantly higher in variety JL-776 over RHRG-6083, JL-501 and TAG-24, whereas shelling percentage (75.12 and 76.60) were found significantly higher in variety TAG-24 followed by JL-501, JL-776 and RHRG-6083. Pod yield (26.59 and 28.14 q ha-1) and haulm yield (39.61 and 36.7 q ha-1) were significantly higher in JL-776 followed by RHRG-6083, JL-501 and TAG-24. Amongst all the groundnut varieties, JL-776 (Phule Bharati) is significantly superior under extended sowing windows followed by varieties RHRG-6083, TAG-24 and JL-501. Sowing during 26th MW was observed to be most suitable and optimum for groundnut considering the growth and yield attributes. This sowing window was at par with 27th MW sowing window.

Assessment of Groundnut (Arachis hypogaea L.) Varieties under Various Sowing Windows for Crop Growth, Development and Yield in Western Maharashtra, India

The present experiment was carried out at Department of Agricultural Meteorology Farm, Centre of Advanced Faculty Training, College of Agriculture, Pune, MPKV, Rahuri for assessment of different groundnut (Arachis hypogaea L.) varieties for crop growth, development and yields under various sowing windows in Western Maharashtra Plain Zone during the Kharif season of 2017 and 2018. The experiment was laid out in split plot design with three replications. The treatment comprised of four varieties viz., V1: JL-501, V2: RHRG-6083 (Phule Unnati), V3: TAG-24 and V4: JL-776 (Phule Bharati) as main plot and four sowing windows viz., S1: 25th MW, S2: 26th MW, S­3: 27th MW and S4: 28th MW as sub plot treatments. From the result of the study, all the growth attributes were increased with the advancement in age of the groundnut crop. Plant height 35.27 and 33.34 cm, total number branches 11.34 and 10.55, total dry matter accumulation plant-1 34.36 and 32.11 g during both the years of experiment, which were found significantly higher in variety JL-776 over RHRG-6083, JL-501 and TAG-24. Among all the sowing windows 26th MW (S2) recorded the highest growth attributes viz., plant height (33.67 and 31.94 cm), total number of branches plant-1 (11.34 and 10.58 g) and total dry matter accumulation plant-1 (34.75 and 32.78 g) during both year of experiment. 26th MW sowing window was at par with the 27th MW sowing window with all growth attributes. Pod yield (26.59 and 28.14 q ha-1) and haulm yield (39.61 and 36.7 q ha-1) were significantly higher in variety in JL-776 followed by RHRG-6083, JL-501 and TAG-24. Pod yield (27.25 and 28.84 q ha-1) and haulm yield (40.60 and 37.61 q ha-1) was higher in 26th MW sowing window, which were at par with 27th MW sowing window during both the year.

Thermal Indices Requirement of Kharif Groundnut (Arachis hypogaea L.) Varieties under Different Sowing Window in Western Maharashtra, India

An agro-meteorological investigation was undertaken during the kharif season of 2017 and 2018 at the Department of Agricultural Meteorology Farm, College of Agriculture, Pune, Maharashtra State (India). The experiment was laid out in split plot design with three replications. The treatment comprised of four varieties viz., V1: JL-501, V2: RHRG-6083 (Phule Unnati), V3: TAG-24 and V4: JL-776 (Phule Bharati) and four sowing windows viz., S1: 25rd MW (18th to 24th June), S2: 26th MW (25th June to 01st July), S­3: 27th MW (2nd to 8th July) and S4: 28th MW (09th to 15th July). Results showed that the higher GDD was observed in 26th MW sowing window with variety JL-776 (1826 and 1723) and RHRG-6083 (1763 and 1690) followed by variety JL-501 and TAG-24, whereas, higher heat use efficiency (0.088 and 0.100 g/GDD) at peg formation to rapid kernel growth stage was observed under 26th MW sowing window (S2) with variety JL-776 (V2) during 2017 and 2018, respectively. The highest HTU was observed in 26th MW sowing window in variety JL-776 (8254 and 6924) and RHRG-6083 (8171 and 7074) followed by variety JL-501 and TAG-24, whereas, higher heliothermal use efficiency (0.0200 and 0.0309 g/HTU) at peg formation to rapid kernel growth stage was observed under 26th MW sowing window (S2) in variety JL-776 (V2) during 2017 and 2018, respectively. Higher PTU (4818.42 and 4890.75) was observed under 26th MW sowing window (S2) in variety JL-776 (V2) this was followed by var. RHRG-6083, JL-501 and TAG-24 whereas, higher photothermal use efficiency (0.0284 and 0.293 g/PTU) at physiological maturity was observed under 26th MW sowing window (S2) with variety JL-776 (V2) followed by variety RHRG-6083, JL-501 and TAG-24 during 2017 and 2018, respectively.

Double-cropping effects of Brassica carinata and summer crops: II. Effects of winter cropping history on subsequent summer crop production

There is an opportunity for crop farmers in the Southeast United States (SE US) to increase income by producing carinata during the winter following summer crop production. However, a major limitation to the use of carinata as a winter annual in the SE US is the lack of information on double-cropping with summer commodity crops common to the region. Considering that integrating carinata into cropping systems common to the region could impact productivity of those systems, it is critical to quantify the potential effects of carinata on existing cropping systems. To this end, a study was conducted in Jay, Florida for three years to evaluate the effects of winter cropping history (carinata vs. fallow) on the productivity of subsequent summer crops including cotton, peanut, sorghum, and soybean. Treatments (winter carinata vs. winter fallow) were laid out in a randomized complete block design with eight replications. Data were collected on infiltration; plant population; plant canopy height; aboveground biomass; and yield. Infiltration rates, determined within a week of carinata harvest, were affected by winter cropping history, being greater in former carinata plots than former fallow plots. Carinata harvest coincided with the timely planting of cotton and peanut which resulted in these summer crops being sown three to four weeks later than their typical sowing window for the SE US. Winter cropping history (including the winter fallow control) did not influence commodity crop (cotton, peanut, sorghum, and soybean) yields when all summer crops were planted at the same time, as in a double-crop scenario. Regarding the combined productivity of winter (carinata vs. winter fallow) and summer crops, carinata-cotton, carinata-peanut, carinata-sorghum, and carinata-soybean systems were necessarily more productive than their winter fallow counterparts due to no crop production in the fallow during the winter cropping years. Overall, results from this study showed that carinata can successfully replace winter fallow, and can be double-cropped with summer-planted sorghum and soybean with no adverse effect on yield. Double-cropping carinata caused a delay in timely planting of cotton and peanut, and represents a limitation that may hinder its adoption in the SE US until such time a faster-maturing carinata cultivar can be developed.

Changes in growth and yield of pigeonpea genotypes under different growing environments

A field experiment was conducted at Regional Agricultural Research Station (RARS), Vijayapura, during kharif2021. The experiment was laid out in a split-plot design assigning four sowing windows to main plots viz., 1st fortnight ofJune, 2nd fortnight of June, 1st fortnight of July and 2nd fortnight of July, whereas genotypes to subplot viz., TS-3R, GRG-152 and GRG-811 with three replications. The results revealed that the seed yield progressively decreased with a delay insowing. A significantly higher seed yield was recorded under early sowings in the 1st and 2nd fortnight of June (1539 and1535 kg ha-1, respectively) and were statistically similar for seed yield. Among the genotypes studied, TS-3R recorded asignificantly higher seed yield (1455 kg ha-1) than other genotypes, which was on par with GRG-152 (1424 kg ha-1). Theincrease in the yield was attributed to better growth and yield attributes coupled with more rainfall (521 mm) receivedduring the cropping period. The pigeonpea sown in 1st fortnight of June recorded higher plant height (196.3 cm), primarybranch per plant (14.00), secondary branch per plant (18.21), the total number of pods (117.7), seed weight per plant (53.1)and 100-seed weight (12.5) compared to delayed sowings. The interaction of pigeonpea genotype TS-3R sown in 2ndfortnight of June recorded a significantly higher seed yield (1550 kg ha-1), which was closely followed by and statisticallyon par with the interaction of GRG-152 sown in 1st fortnight of June (1542 kg ha-1). The study concludes that the earlysowing of pigeonpea genotypes TS-3R and GRG-152 is better for realizing maximum productivity and profitability in thenorthern dry zone of Karnataka.

Evaluating the seasonal accumulation of Heat units as an agroclimatic indicator on Baby corn (Zea mays L.) under different sowing windows

Temperature determines the plant's growth and development, which decides the onset of different phenophasic stages of the Baby corn. This study aimed to evaluate the phenological behaviour and yield of Baby corn (Zea mays L.) influenced by sowing windows and heat units with the field investigations carried out during winter (January – April) and kharif (June – September) 2022 at Eastern Block Farm of Tamil Nadu Agricultural University, Coimbatore. Growing Degree Days (GDD), Photo Thermal Units (PTU), Helio Thermal Units (HTU), Relative Temperature Disparity (RTD), Heat Unit Efficiency (HUE) and seasonal efficiency were calculated at different phenological stages. The results revealed that early attainment of phenophases was noticed during winter (62.5 days - January 21st to 27th April) than kharif (77.1 days – 15th June to 4th October). Among seasons, higher accumulation of GDD (1553) and PTU (19099) was observed during kharif 2022, whereas maximum accumulation of HTU (9923) and RTD (2146) was observed in winter 2022. Seasonal efficiency was higher during kharif (118) than during the winter season (81). The sowing windows significantly influenced the higher accumulation of heat units and yield attributes. Hence, higher yield (11922.7 kg ha-1) and HUE (7.3) were obtained during kharif than in winter 2022 (yield – 7849 kg ha-1 and HUE – 5.8). Weather parameters showed a negative correlation except RH-I, WS, SR and HUE during winter 2022 (R2=0.802) and RH-II, WS, RF, Daylength, HUE during kharif 2022, which had a positive correlation (R2=0.795). Baby corn is highly sensitive to increasing temperature. Hence, the study expresses the effect of varying ambient temperature on the duration between the phenological stages and yield.

Performance of Quinoa (Chenopodium quinoa Willd.) under varied sowing windows and planting patterns

The experiment on “Performance of quinoa (Chenopodium quinoa Willd.) under varied sowing windows and planting patterns” was carried out at Main Research Station, Hebbal, UAS, GKVK, Bengaluru, during Kharif-2019. The experiment constituted four sowing dates (D1: July second fortnight, D2: August first fortnight, D3: August second fortnight and D4: September first fortnight) and four planting patterns (S1: 30 × 15 cm, S2: 45 × 15 cm, S3: 60 × 15 cm and S4: 75 × 15 cm) and replicated thrice was laid out in split-plot design. The results revealed that, increase in AGR and CGR with advancement of age of quinoa and was peak at 60 DAS and showed decreasing trend towards harvest. Sowing during July second fortnight showed significantly, higher AGR and CGR between 30-60 DAS (0.367 and 5.11, respectively) and grain and stover yield (2051 and 2439 kg/ha, respectively) as compared to other sowing windows. Similarly, between 60 DAS-harvest, Absolute Growth Rate and Crop Growth Rate (0.195 and 2.65, respectively) were significantly higher under July second fortnight sowing window, yet was found to be on par with sowing on August first fortnight and August second fortnight. In contrary, September first fortnight sown crop reached days to 50 per cent flowering (43.90) and days to maturity (97.36) early, which was significantly lower compared to other sowing windows and found on par with August first fortnight sown crop (41.16 and 95.53, respectively). Among the varied planting patterns, 45 × 15 cm spacing was found to be optimum and recorded significantly higher grain and stover yield (1941 and 2346 kg/ha, respectively) as compared to other spacings.

Establishing Optimal Planting Windows for Contrasting Sorghum Cultivars across Diverse Agro-Ecologies of North-Eastern Nigeria: A Modelling Approach

In the context of climate change, the sowing date and cultivar choice can influence the productivity of sorghum, especially where production is constrained by low soil fertility and early terminal drought across the challenging agro-ecologies of north-eastern Nigeria. Planting within an optimal sowing window to fit the cultivar’s maturity length is critical for maximizing/increasing the crop yield following the appropriate climate-smart management practices. In this study, the APSIM crop model was calibrated and validated to simulate the growth and yield of sorghum cultivars with differing maturing periods sown within varying planting time windows under improved agricultural practices. The model was run to simulate long-term crop performance from 1985 to 2010 to determine the optimal planting windows (PWs) and most suitable cultivars across different agro-ecological zones (AEZs). The performance of the model, validated with the observed farm-level grain yield, was satisfactory across all planting dates and cropping systems. The model predicted a lower mean bias error (MBE), either positive or negative, under the sole cropping system in the July sowing month compared to in the June and August sowing months. The seasonal climate simulations across sites and AEZs suggested increased yields when using adapted sorghum cultivars based on the average grain yield threshold of ≥1500 kgha−1 against the national average of 1160 kgha−1. In the Sudan Savanna (SS), the predicted optimum PWs ranged from 25 May to 30 June for CSR01 and Samsorg-44, while the PWs could be extended to 10 July for ICSV400 and Improved Deko. In the Northern Guinea Savanna (NGS) and Southern Guinea Savanna (SGS), the optimal PWs ranged from 25 May to 10 July for all cultivars except for SK5912, for which predicted optimal PWs ranged from 25 May to 30 June. In the NGS zone, all cultivars were found to be suitable for cultivation with exception of SK5912. Meanwhile, in the SGS zone, the simulated yield below the threshold (1500 kgha−1) could be explained by the sandy soil and the very low soil fertility observed there. It was concluded that farm decisions to plant within the predicted optimal PWs alongside the use of adapted sorghum cultivars would serve as key adaptation strategies for increasing the sorghum productivity in the three AEZs.

A Simulation Study on Optimization of Sowing Time of Maize (Zea mays L.) for Maximization of Growth and Yield in the Present Context of Climate Change under the North China Plain

Adjusting the sowing dates of crops is an effective measure for adapting them to climate change, but very few studies have explained how the optimum sowing dates can be determined. In this study, we used the sowing date field data from 2018 to 2021 from Hebei Gucheng Agricultural Meteorology National Observation and Research Station to analyze the effects of the sowing date on growth, development, and yield of maize, and to quantify the impact of light-temperature potential productivity on different stages of the yield formation. The results showed that delayed sowing decreased the vegetative growth period (VGP) and increased the reproductive growth period (RGP) of maize. The light-temperature potential productivity of the whole growth (WG) period had an exponential relationship with the theoretical yield. At least 14,614.95 kg ha−1 of light-temperature potential productivity was needed to produce grain yield. The maximum theoretical yield was approximately 18,052.56 kg ha−1, as indicated by the curve simulation results. The influence of light-temperature potential productivity on theoretical yield was as follows: VGP > RGP > vegetative and reproductive period (VRP). Accordingly, a method for determining the sowing time window based on VGP was established, and the optimal sowing dates were estimated for 1995–2021 and the SSP2-4.5 scenario in CMIP6 in the middle of this century (2030–2060). The simulation results showed that the optimum sowing date of maize “Lianyu 1” at the study site was 20–25 May in 1995–2021. In the middle of this century, the optimal sowing time of maize was ahead of schedule and the suitable sowing window was increased slightly. We conclude that advancing the sowing date of maize is a practical strategy for enhancing yield in the context of climate warming, and this strategy will provide a meaningful reference for scientific optimization of sowing dates to adapt maize to climate change.

Strategies to cope with warming and reduced rainfall in green gram for northern transition zone of Karnataka, India

South West monsoon has become more erratic and unpredictable in the northern Transition Zone of Karnataka and in the coming decades this will continue further with warming. To cope with change in climate, agronomic adaptation strategies (optimum sowing window and application of irrigation at critical stages) are required to maintain higher yields of greengram. As a result, the DSSAT model was used to investigate the influence of climate change on greengram. The study included a combination of two temperature (+1 and +2 °C) and two reduced rainfall (˗10 and ˗20 %) scenarios in comparison with the baseline scenario (i.e., current climate). These scenarios were built for 32 years using historical weather data from 1985-2016. With regard to adaptation strategies, six dates of sowing; starting from June 1st week to July 2nd week at a weekly interval and four irrigation treatments each of 60 mm; one at pre-flowering stage, one at pod formation stage, one each at pre-flowering and pod formation stages, and no irrigation (rainfed) were included. Between sowing dates, the model's simulation of average grain yield across 32 years revealed that, 3rd (513 kg ha-1) and 4th (508 kg ha-1) week of June were found to be optimum under future climate. Irrigation at any of the critical stages increased the yield, but largest positive yield response was replicated with two irrigations: one at pre-flowering and the other during the pod formation stage (556 kg/ha). This study clearly showed that under future climates of 1 to 2 ℃ warming with reduced rainfall scenarios (-10 % & -20 %), sowing on 3rd and 4th week of June is best one, providing two irrigations (60 mm each) one at pre-flowering and the other at pod formation stage would more than compensate the loss in yield projected under changing climates in coming decades.

Optimal Sowing Windows under Rainfall Variability in Rainfed Agriculture in West Africa

Climate change is exacerbating adverse impacts of water stress in rainfed agriculture. This paper seeks to identify safe sowing windows for smallholder farmers in the Sudanian region of West Africa (WA). We hypothesize that the traditional focus on the onset of the season to start sowing leads to crop losses in years of high rainfall intermittency. AquaCrop, an FAO crop model, is used to simulate the yield response of maize (Zea mays L.) to sowing dates ranging from the 1st of May to the 30th of November at 20 locations in WA. We find that sowing directly after the first rains carries a higher risk of water stress, hampering crop development due to insufficient buildup of soil water storage to overcome dry spells. Based on three years of data per station on average, we identify safe sowing windows across the Sudanian region that secure optimal yield in 97% of all cases. We find that delaying sowing to mid-June (savanna and western part of the region) and to July (semi-arid region) ensures optimal yields. Of the three commonly applied local onset approaches covered in our evaluation, only LO10mm (10 mm/day on four consecutive days) achieves a similar yield result. The advantage of the safe window approach is that it is accessible for smallholders, who in many cases do not have access to local rainfall information.

Influence of sowing time on yield and economics of small millet crops during kharif in YSR kadapa district of Andhra Pradesh

In the changing climate scenario, millet crops are gaining importance due to factors such as low-maintenance, disease and pest resistance, nutritional benefits, market demand, fodder value and ecological benefits. As farmers especially in dry land areas are showing interest in cultivation of millets, finding out an optimum time of sowing for getting higher yield and good returns has become essential. Hence, the experiment was laid out at Agricultural Research Station, Utukur, Kadapa during kharif seasons of 2019 and 2020 to evaluate the performance of small millet crops under different sowing windows. The trial included three sowing windows namely July I F.N, July II F.N, and August I F.N and six millet crops viz., foxtail millet, little millet, brown top millet, proso millet, kodo millet and barnyard millet which were laid out in split plot design with sowing times in main plots and six millet crops in sub plots in three replications. The results indicated that July II F.N is the optimum time of sowing for small millet crops to get higher yield and net returns. Among the different crops sown, kodomillet recorded higher seed yield at different times of sowing followed by brown top millet. Whereas the lowest seed yield was recorded with little millet crop at all the dates of sowing.

Wheat productivity and profitability under different sowing window and irrigation scheduling based on weather model and spatial data in north Western Himalayas

Wheat productivity and profitability under different sowing window and irrigation scheduling based on weather model and spatial data in north Western Himalayas

Optimizing Sowing Window for Sesame

Optimizing Sowing Window for Sesame

Response of Super Early Pigeonpea to Varied Sowing Windows and Integrated Nutrient Management in Southern Telangana Zone

The field experiments were conducted during kharif seasons (July–November) 2018 to determine the physiological, yield parameters and yield of super early pigeonpea under different sowing windows and with integrated nutrient management practices at Regional Agricultural Research Station, Palem, Telangana, India situated at 16˚ 51’ N Latitude and 78º25’ E Longitude with an average Altitude of 478 m above the mean sea level of Southern Telangana Zone of Telangana State, India. The experiment was laid out in strip plot design for pigeonpea with 3 main treatments i.e., (M1)1st July, (M2)20th July and (M3) 10th August sowings and four integrated nutrient management practices as sub treatments viz.S1: 75% RDF, S2:75% RDF+FYM enriched with microbial consortia (1 t ha-1), S3:100% RDFand S4:100% RDF+FYM enriched with microbial consortia (1 t ha-1) and replicated thrice. The findings of two years of field study revealed that, among the three sowing dates in main treatments, 1st July (M1) recorded a greater number of days to 50% flowering and 75% pod maturity of SEPP. Similar trend in the CGR, RGR, yield parameters and yield except 1000 seed weight of SEPP was noticed superior with 10th August sowing (M3) compared to other two sowing dates viz. July 1st and July 20th. The sub treatment containing INM practices revealed higher values of physiological, yield parameters and yield with application 100% RDF+FYM enriched with microbial consortia @ 1 t ha-1 (S4) compared to other INM practices.

Optimizing the sowing window for direct-seeded rice (Oryza sativa L.) considering high yield and methane emissions in Central China

CONTEXTElevated temperatures have had many adverse effects on rice (Oryza sativa L.) production. Methane (CH4) emissions from rice paddy fields can further increase global temperatures and in turn affect rice production. Many adaptations, such as shifting the sowing date, can help rice plants avoid the influences of high temperatures and reduce CH4 emissions by shortening the rice-growth duration. OBJECTIVEThis study explored the optimal sowing window for direct-seeded rice with the highest yield and lowest CH4 emissions. METHODSHere, we used the calibrated Crop Environment Resource Synthesis (CERES)-Rice model and a widely used CH4 estimation equation to determine the optimal sowing window under historical and future scenarios in Hubei Province, China. This province was divided into four zones, and three rice cultivars were selected. Numerical simulations were conducted for sowing dates at three-day intervals across the entire potential sowing window for each cultivar and zone. The simulated rice yields and estimated CH4 emissions for all sowing dates were compared to determine the optimal sowing window. RESULTS AND CONCLUSIONSOur results indicated that due to the lower emergence rate caused by low temperatures during the early stage of the potential sowing window, rice yield gradually increased with sowing date delay and tended to be stable, while CH4 emissions exhibited the opposite pattern. In this study, the optimal sowing window was determined to be when the lowest CH4 emissions and highest rice yields occurred simultaneously. Under heat stress, these two kinds of events often did not overlap, and the optimal sowing window was defined by the highest yields only. In addition, the current sowing date was mostly within the optimal sowing window during the historical period. The optimal sowing window appeared later under future scenarios than in the historical period, suggesting that the sowing date should be delayed in the future. SIGNIFICANCERice plants cultivated in optimal sowing windows can produce high yields and emit the least CH4. This study provides a better understanding of the effects of shifting the sowing date on food security and greenhouse gas emissions, ensuring sustainable rice production.

Physiological and biochemical responses of soybean to drought as represented by the fraction of transpirable soil water

The influence of water deficit on plant physiological and biochemical responses as measured by the fraction of transpirable soil water (FTSW) has not been investigated in cultivars developed by the world's largest soybean producer. This information can help obtain plants with improved tolerance to the abiotic stress that most affects soybean production in Brazil, enabling adaptation to edaphoclimatic conditions to enhance the crop's yield potential. We aim to determine the FTSW threshold for transpiration and evaluate changes in the growth, physiological activities, and biochemical and antioxidant responses of soybean cultivars. Three trials were sown on 11/19/2018 (T1), 12/28/2018 (T2), and 9/9/2019 (T3), representing almost the entire soybean sowing window in Brazil. The estimated FTSW threshold values were 0.33, 0.29, and 0.31 in T1; 0.35, 0.41, and 0.43 in T2; and 0.31, 0.49, and 0.45 in T3 for cultivars BMX GARRA IPRO, DM 66I68 RSF IPRO, and NA 5909 RG, respectively. In the three trials, NA 5909 RG showed the greatest height. The POD enzyme was activated in non-irrigated plants in T2 only in cvs. DM 66I68 RSF IPRO and NA 5909 RG. We conclude that cvs. DM 66I68 RSF IPRO and NA 5909 RG showed a more efficient stomatal control, conserving soil water for a longer time, which indicates greater tolerance to water deficit.

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.

Simulating potential yield of rainfed soybean in northeast Nigeria

We used the CROPGRO-Soybean model to simulate the production potential of rainfed soybean in northeast Nigeria. Data from ten soybean experiments conducted under optimal conditions in 2016–2018 at Kano and Dambatta in the Sudan savanna (SS) agroecological zone were used to determine the cultivar coefficients and calibrate the model for the varieties TGX 1448–2E and TGX1951–3 F. The model was evaluated with data from four phosphorous response trials conducted at Zaria and Doguwa in the northern Guinea savanna (GS) of Nigeria between 2016 and 2018. Results show that the CROPGRO-Soybean model was able to accurately simulate soybean growth and grain yield with low RMSE and high d-index values. Consequently, the model was used to investigate the rainfed yield potential of the two varieties in 24 sites in northeast Nigeria under different sowing windows using 30-year (1985–2014) weather data. The result shows that soybean can be grown in northeast Nigeria, but yield performance is dependent on location, variety and sowing window. The simulated yield was higher in the SS than in the GS agro-ecozone despite the longer growing period in the later. Low yield was simulated for TGX 1448–2E for most of the sites. The yield of TGX1951–3 F was above a threshold of 1500 kg ha−1 in 5 out of 12 sites in the GS and 7 out of 12 sites in the SS, suggesting that this variety is the most suitable for cultivation in northeast Nigeria. Sowing TGX 1951–3 F can be delayed to July 16 at Gwaskara, Nasarawo Demsa and Tawa in the GS and at Briyel, Lakundum, Jara Dali, Kurbo Gayi, and Mathau in the SS with a low-risk of crop failure. The desired yield will be achieved at Chikala and Puba Vidau with a significantly low risk of crop failure for all sowing windows. The results from this study suggest that the CSM-CROPGRO-Soybean model can be a valuable tool in determining the right variety and sowing window for soybean production in targeted agroecological zones in northeast Nigeria.